problem solving in language learning helps to enhance

How Does Learning a Language Affect the Brain, and How Can Teachers Harness the Benefits?

Learning another language has a huge effect on our brains, from boosting brain plasticity to increasing empathy and critical thinking. Let’s take a look at the research and pinpoint the most important benefits of learning languages, and how teachers can amplify these advantages to improve outcomes.

It is thought that over half the people in the world  speak more than one language  and, as the world becomes more globalised and we face more shared challenges, speaking more than one language takes on new importance. To address future global issues, we’ll need to work together across languages, cultures, religions and nationalities and for that, we need the language skills, better empathy, to be able to think critically and to be able to adapt quickly to new situations. Luckily, these are all skills that learning a second language helps to foster.

How learning a language affects the brain

When exploring what learning a language does to the brain, it makes sense to start with neuroplasticity, or brain plasticity, since that determines how well we learn and for how long. The more the brain can adapt and change, the more we can learn. Brain plasticity is the ability of the brain to develop and change in response to stimuli, and learning a second language has a significant, positive effect on plasticity. Recent  studies  have spoken of these benefits helping to hold age-related conditions such as dementia at bay.

When we increase the information in our brains, we need to be able to file and sort that information so we can find it again quickly when we need it. The mental structures that we use to organise knowledge are known as  schema.  As cognitive processes go, these schemas are vital for everything from memory, understanding others, problem-solving and critical thinking. When we speak another language, we become adept at categorising and accessing information quickly, using our executive function skills.

Executive function – the CEO section of the brain

From self-awareness and non-verbal working memory to self-motivation and problem solving,  executive function  covers a crucial set of mental skills. The term executive functions is a business metaphor that pinpoints the essential skills we use to organise and regulate our lives, in the same way a chief executive would use their skills to run a business. They include attention, planning, working memory, abstract thinking, self-control, moral reasoning and decision making. The brains of bilingual children become accustomed to looking for different solutions that consider context, a key element of problem solving. This is because the ability to select the right language for the right context relies on the anterior cingulate cortex (ACC), which is the area of the brain thought necessary to disregard the appropriate distractors; in this case, the other language. The brain needs to work at suppressing the other language to allow the right one to take over and that hones our ability to manage cognitive conflict.

When to stop, when to go – thinking critically and building control

There is a simple cognitive conflict test in which the names of colours are written in different colours (‘green’ written in blue, for instance) and respondents have to say the colour that the word is written in. This is trickier than it sounds because it takes our brains longer to process the colour of the letters than it does to read the word. Those who speak more than one language perform better in this test than monolingual participants. And although the brain isn’t a muscle, it often behaves like one, so this constant work results in more grey matter in the ACC amongst bilingual people than their monolingual counterparts.

In addition, the process of ‘switching’ a language on and actively suppressing the words, grammar and structure of your other language also helps improve self-control, which is often a good indicator of academic success.

Make the most of autonomy, games and active learning

When looking at ways to capitalise on the beneficial changes to the brain that language learning brings, building on self-control and self-direction to help students become independent learners should be top of the list. In part, this is because independent learning extends learning beyond the school in meaningful ways, and this will be useful if we face future school closures as a result of lockdowns. A useful method to try for this is flipped learning.

A teaching tactic that uses metacognitive principles, the flipped classroom dates back quite a few years now but we are paying more attention to it, and to blended learning, as a result of school closures. A simple concept, flipped learning asks students to tackle the lower levels of learning before the class then engage in higher cognitive levels of learning with their peers and teachers.  For teachers who want to try this approach out, free webinars can be a useful guide.

It’s never just play

Contemporary US author and play specialist O. Fred Donaldson said it well; “Children learn as they play. Most importantly, in play, children learn how to learn.”

I saw the evidence of this time and time again in the classroom. Young children learn well when they’re engaged, and they respond best to humour and whimsy in the learning process. They need to be able to use imagination to create context for the things they are learning that they cannot readily see, touch or interact with. When we created games for our students we quickly found that these games helped children build a solid foundation in a new language, and games can be a great tool for scaffolding learning for students.

In light of the disruption to education this year, the benefits of learning a new language should not be ignored. As schools return, helping young learners overcome the past few months is understandably a priority; helping their brains develop so that learning becomes even more effective isn’t just sensible, it’s necessary.

You may also like:

• Should ESL Instructors Speak Other Languages in the Classroom?
• The Titanic
• Listen&Learn: Missouri Cheese Caves

this article make me feel proud of being bilingual person

thanks for the article 😊

Great work. Thanks for sharing. My students and I think this is very helpful.

It’s very important subject. Thank you for sharing it with us,

Insightful topic…

Very true,make our students more independent learners.

Very nice and interesting topic.

Very true! Now I understand, among all other things, why teaching within context is to, indirectly, make our students more independent learners, in a more meaningful way, rather than fostering their dependency on us!

Really insightful.

Leave a comment

Email * (not published)

Why learning a second language can help students achieve other academic goals

Learning a new language brings a whole range of interesting benefits, but how can they be applied to academic goals? The truth is there are plenty of ways that picking up a second language can help a student to progress across other disciplines.

Improved problem-solving ability

For some students, learning a new language is a solitary experience that involves lots of audio clips and watching TV shows/films in another language with English subtitles. However, there are increasingly more interactive means of learning languages. For example, people favor getting one-to-one language lessons online, especially as students. This is because the arrangement allows students to learn on a flexible basis around other classes and it's an affordable alternative with a student discount that makes it cheaper to take additional classes. In addition, having a personal tutor can help to further tailor the experience to a student’s needs.

Regardless of one’s learning style, the mutual outcome is a new language mastery that provides great benefits.

After learning a new language, students’ brains start to work differently. This is something they probably won’t even notice in any sort of conscious way, but helps with cognitive processes like problem-solving. Some sources suggest that a person’s brain grows bigger and the grey matter becomes denser when they start to speak a different language for the first time. That indicates the brain is operating at a healthier capacity. Interestingly, it doesn’t seem to matter at what age a person starts to learn a new language, as people of all ages receive similar mental benefits.

Studying for other subjects at the same time as taking language classes can be a huge boost: some studies suggest that the ability to focus and pay attention are substantially improved by the experience of learning new words in a different language. Of course, a heightened concentration span is invaluable to any student as it makes it much easier to retain information in classes and means less time re-learning or revising in the lead-up to finals!

Enhancing memory

A better memory is another of the benefits to be gained from learning a language. It seems that the process of learning new words helps use other parts of the brain that people don’t normally activate. The way people store words and remember new grammatical rules are both carried out differently according to the language being spoken, so it makes sense that being exposed to different languages turns the brain into a more flexible and powerful tool.

Some research suggests learning a language helps to stimulate the brain in such a way that it becomes easier to retrieve specific information. However it works, it seems to be clear that there is a link between memory function and the language being spoken.

Some worry that studying a language will distract from learning other subjects, but this doesn’t seem to be the case at all. Everything points to the mental benefits of language lessons enhancing the ability to learn other things more effectively by allowing the narrowing a person’s focus so they can avoid distractions.

Feel more confident

The final benefit to learning a second language is one that will stay with you for life: confidence.

Becoming bilingual instills an extra degree of confidence in that it's generally quite an impressive feat to pick up a new tongue with fluency. However, the doors that a new language can open are unlimited in that you can enjoy traveling with the confidence that there will be little to no language barriers, or even set your sights on working abroad comfortably.

Regardless of why you start learning a new language, you will find that your confidence in yourself grows as you become better at it. This is sure to be reflected in your other studies, as you begin to believe in yourself more and understand that you have the capacity to learn large amounts of new information.

There is no doubt that learning new language skills is something that will enhance your life and studies. If you haven’t yet started, now is a great time to consider doing this and getting all of the benefits we have looked at here.

• Admissions Event Registration
• Admissions Inquiry
• Merit Scholarship
• SpiritWear Store
• Alumni Stories
• Parent Portal
• Whitby Difference
• Whitby Education
• Whitby Experience
• Whitby in the News
• Whitby Campus
• Parent Involvement
• Board of Trustees
• Stepping Stones
• Physical Education
• Cocurriculars
• Secondary School Counseling
• Community Service

• Privacy Policy

How learning a new language helps brain development

Luz palmero.

There are many benefits to learning a new language. Languages are windows to different cultures, allowing us to connect with others from around the world. But learning a new language extends past just having a simple conversation or having access to different words, concepts, and metaphors.

A recent study by Dr. Thomas Bak — a lecturer at Edinburgh's School of Philosophy, Psychology and Language Sciences — shows that young adults proficient in two languages performed better on attention tests and had better concentration than those who spoke only one language. Dr. Bak tested 853 participants in 1947, when they were all 11 years old. They were retested in 2008 and 2010, when they were in their early 70s. He found that those who became bilingual performed better than expected. The strongest effects were seen in general intelligence and reading. The results showed that learning a new language in adulthood still has positive results, meaning there’s never a reason to feel too old to gain the cognitive benefits of learning a new language.

Language learning helps improve people's thinking skills and memory abilities. Bilingual students concentrate better, ignoring distractions more effectively than those who only speak one language. “Because the language centers in the brain are so flexible, learning a second language can develop new areas of your mind and strengthen your brain's natural ability to focus."

There is another study conducted in Sweden which helped visualize the benefits of learning a new language have on the brain. The researchers conducted the study on two groups of scholars: one that studied languages and another that studied proportionately rigorous non-linguistic subjects. The MRI scans showed that the brains of the participants studying languages increased in size, while the brain sizes of the other group remained the same.

“As a language learner, you'll not only become a more conscious thinker and listener who can communicate clearly and think creatively, but you'll also gain the most significant benefit of multilingualism: a broader, more global perspective,” writes Dan Roitman of Pimsleur in the Huffington Post . 

In a 2009 study led by Agnes Kovacs of the International School for Advanced Studies in Trieste, Italy, seven-month-old babies exposed to two languages from birth were compared with peers raised with one language. The study showed that the infants raised with two languages from birth displayed improved cognitive control abilities compared to their monolingual counterparts.

The studies suggest the bilingualism improves the brain’s executive function. It helps with ignoring distractions to stay focused, switching attention willfully from one thing to another and holding information in mind. Learning another language is one of the most effective and practical ways to increase intelligence, keep your mind sharp, and buffer your brain against aging.

Browse Topics

• Passion for Learning (33)
• Global Citizenship (22)
• Whole Child Development (21)
• inquiry-based learning (15)
• Parenting (14)
• Primary Years Program (14)
• International Baccalaureate (12)
• Summer Learning (12)
• Perspective (11)
• Technology (11)
• Digital Citizenship (9)
• lifelong learning (9)
• Community (8)
• Empowerment (8)
• Collaboration (7)
• language (7)
• Middle Years Program (6)
• Montessori (6)
• Co-Curriculars (5)
• IB Schools (5)
• Innovation (5)
• Middle School (5)
• IB Learner Profile Traits (4)
• Professional Development (4)
• Reflections (4)
• distance learning (4)
• educational trends (4)
• Athletics (3)
• E-learning (3)
• Makerspace (3)
• Mathematics (3)
• Student Assessment (3)
• whitby school (3)
• Coaching (2)
• Curriculum (2)
• Dominican Republic (2)
• Guest Speakers (2)
• Head of School (2)
• Lower School (2)
• Notes from the Classroom (2)
• Spanish language (2)
• Theater (2)
• cultural competency (2)
• individuals and societies (2)
• language immersion (2)
• social responsibility (2)
• social-emotional (2)
• student leadership (2)
• Admissions (1)
• Cross Country (1)
• Exhibition (1)
• Grade 4 (1)
• In the news (1)
• Perspective Unit (1)
• Secondary School (1)
• Sensory skills (1)
• Service (1)
• Tutorials (1)
• Unit of Inquiry (1)
• assessment (1)
• community service (1)
• competition (1)
• confidence (1)
• early childhood learning (1)
• emotional intelligence (1)
• empathy (1)
• executive functioning (1)
• goal setting (1)
• montesorri (1)
• sensory experiences (1)
• Admission Policy
• Mission and Vision
• Annual Reports

Our systems are now restored following recent technical disruption, and we’re working hard to catch up on publishing. We apologise for the inconvenience caused. Find out more: https://www.cambridge.org/universitypress/about-us/news-and-blogs/cambridge-university-press-publishing-update-following-technical-disruption

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings .

Login Alert

• > Journals
• > Annual Review of Applied Linguistics
• > Volume 25
• > LANGUAGE LEARNING STRATEGY INSTRUCTION: CURRENT ISSUES...

Article contents

Definition and importance of strategies, methods for identifying learners' strategies, identification of language learning strategies, classroom research on language learning strategy instruction, methodological and practical issues in learning strategy instruction, directions for future research, language learning strategy instruction: current issues and research.

Published online by Cambridge University Press:  21 July 2005

This chapter begins with definitions and an overview of methods used to identify learners' strategies, then summarizes what we have learned from the large number of descriptive studies of strategies reported by language learners. Research on language learning strategies has a history of only about thirty years, and much of this history has been sporadic. The 1980s and early 1990s were a period of substantial research on language learning strategies, much of it descriptive. This period was followed by an apparent loss of interest in language learning strategies, judging by limited reported research and few related conference presentations. Recently, however, a number of new investigations have reinvigorated the field. The focus of the chapter is on the evolution of research on language learning strategy intervention studies, the issues that have emerged from this research, and metacognitive models that can be useful in the language classroom. The discussion concludes by setting out directions for future research.

Learning strategies are procedures that facilitate a learning task. Strategies are most often conscious and goal-driven, especially in the beginning stages of tackling an unfamiliar language task. Once a learning strategy becomes familiar through repeated use, it may be used with some automaticity, but most learners will, if required, be able to call the strategy to conscious awareness. Learning strategies are important in second language learning and teaching for two major reasons. First, by examining the strategies used by second language learners during the language learning process, we gain insights into the metacognitive, cognitive, social, and affective processes involved in language learning. The second reason supporting research into language learning strategies is that less successful language learners can be taught new strategies, thus helping them become better language learners (Grenfell & Harris, 1999 ). Numerous descriptive studies have addressed the goal of understanding the range and type of learning strategies used by good language learners and the differences in learning strategy use between more and less effective learners. However, until relatively recently there have been fewer studies focusing on the second goal of trying to teach language learning strategies in classroom settings.

Learning strategies are sensitive to the learning context and to the learner's internal processing preferences. If learners perceive, for example, that a task like vocabulary learning requires correct matching of a new word to its definition within a specified period of time (as in a test), they will likely decide to use a memorization strategy. Their choice of which memorization strategy to use will depend on their understanding of their own learning processes and on which strategies have been successful in the past (Hsiao, 2004 ). A different task, such as being able to discuss the theme of a short story will require strategies different from memorization—such as making inferences about the author's intended meaning and applying the learner's prior knowledge about the topic. The interpretation of a language learning task is closely related to the goals advocated within each learner's cultural context, for a learning strategy valued in one culture may be deemed inappropriate in another (Olivares-Cuhat, 2002 ; Wharton, 2000 ). A particular learning strategy can help a learner in a certain context achieve learning goals that the learner deems important, whereas other learning strategies may not be useful for that learning goal.

Learning strategies are identified through various self-report procedures. Although self-report is always subject to error, no better way has yet been devised for identifying learners' mental processes and techniques for completing a learning task. Learning strategies are for the most part unobservable, though some may be associated with an observable behavior. For example, a student listening to new information may use selective attention (unobservable) to focus on the main ideas and might then decide to take notes (observable) on these main ideas. The only way to find out whether students are using selective attention during a listening comprehension task is to ask them. Mere observation has proven unsatisfactory in identifying learners' strategies (Cohen, 1998 ; O'Malley & Chamot, 1990 ; Rubin, 1975 ; Wenden, 1991 ).

Self-reports have been conducted through retrospective interviews, stimulated recall interviews, questionnaires, written diaries and journals, and think-aloud protocols concurrent with a learning task. Each of these methods has limitations, but at the present time they remain the only way to generate insights into the unobservable mental learning strategies of learners.

In retrospective interviews, learners are prompted to recall a recently completed learning task and describe what they did to complete it (see Macaro, 2001 ). A stimulated recall interview is more likely to accurately reveal students' actual learning strategies because it is conducted immediately after a learning task. The actual task is videotaped, and the interviewer then plays back the videotape, pausing as necessary, asking the student to describe his or her thoughts at specific moments during the learning task.

Questionnaires

The most frequently used method for identifying students' learning strategies is through questionnaires. Some studies have developed questionnaires based on tasks that students have just completed (see Chamot & El-Dinary, 1999 ; Fan, 2003 ; Goh, 2002a ; Kojic-Sabo & Lightbown, 1999 ; Ozeki, 2000; Rubin & Thompson, 1994 ; Weaver & Cohen, 1997 ). Most descriptive studies, however, have relied on a questionnaire developed by Oxford ( 1990 ), the Strategy Inventory for Language Learning (SILL). This instrument has been used extensively to collect data on large numbers of language learners (see Cohen, Weaver, & Li, 1998 ; Olivares-Cuhat, 2002 ; Oxford, 1990 ; 1996 ; Oxford & Burry-Stock, 1995 ; Wharton, 2000 ). The SILL is a standardized measure with versions for English as a second language (ESL) students and students of a variety of other languages, and as such can be used to collect and analyze information about large numbers of students. It has also been used in studies to correlate strategy use with variables such as learning styles, gender, proficiency level, culture, and task (Bedell & Oxford, 1996 ; Bruen, 2001; Green & Oxford, 1995 ; Oxford, Cho, Leung, & Kim, 2004; Nyikos & Oxford, 1993 ; Oxford & Burry-Stock, 1995 ; Wharton, 2000 ). Oxford and her colleague are currently developing a task-based questionnaire to complement the SILL (Oxford et al., 2004 ).

Diaries and Journals

Written diaries and journals have also been used to identify language learners' strategies. In these, learners write personal observations about their own learning experiences and the ways in which they attempted to solve language problems (see, for example, Carson & Longhini, 2002 ). Rubin (2003) suggests using diaries for instructional purposes to help students develop metacognitive awareness of their own learning processes and strategies. An interesting variant on the diary study was recently conducted by Takeuchi ( 2003 ), who examined published books and essays by Japanese good language learners of various languages and analyzed each author for evidence of learning strategy use included in their descriptions of their foreign language learning histories.

Think-Aloud Protocols

A think-aloud protocol can be used for individual interviews in which the learner is given a target language task and asked to describe his or her thoughts while working on it. The interviewer may prompt with open-ended questions such as, “What are you thinking right now? Why did you stop and start over?” Recordings of think-aloud interviews are then analyzed for evidence of learning strategies. The rich insights into language-learning strategies provided through think-aloud protocols tend to reveal online processing, rather than metacognitive aspects of planning or evaluating (see Chamot & Keatley, 2003 ; Cohen et al., 1998 ; O'Malley & Chamot, 1990 ).

Although self-report may be inaccurate if learners do not report truthfully or cannot remember their thinking, it is still the only way available to us to develop some understanding of learners' mental processing. As Grenfell and Harris have pointed out: “It is not easy to get inside the ‘black box’ of the human brain and find out what is going on there. We work with what we can get, which, despite the limitations, provides food for thought” (1999, p. 54)

The language learning strategies identified through these self-report methods have identified characteristics of good language learners and compared the strategies of more-and less-effective language learners. Such studies have been important in identifying and classifying strategies used by language learners and understanding how strategies are actually used in the learning process. This information has in turn guided instructional investigations that have sought to teach learning strategies to language learners and to measure relationships between strategy use and language proficiency, metacognition, motivation, and self-efficacy.

Language learning strategies research began in the 1970s with the seminal work of Joan Rubin, who, like Stern ( 1975 ), suggested that a model of “the good language learner” could be constructed by looking at special strategies used by successful L2 students (Rubin, 1975 ). Other researchers followed with descriptions of learner characteristics and strategic techniques associated with effective second and foreign language learning (Naiman, Fröhlich, Stern, & Todesco, 1978/1996 ; O'Malley, & Chamot, 1990 ). More recently, Takeuchi ( 2003 ) identified the characteristics of Japanese good language learners through their biographies. Taken together, these studies identified the good language learner as one who is a mentally active learner, monitors language comprehension and production, practices communicating in the language, makes use of prior linguistic and general knowledge, uses various memorization techniques, and asks questions for clarification.

Later studies comparing more and less effective language students have revealed a recurring finding that less successful learners do use learning strategies, sometimes even as frequently as more successful peers, but that their strategies are used differently (Chamot & El-Dinary, 1999 ; Khaldieh, 2000 ; Vandergrift, 1997a , 1997b ). A recent study by Vandergrift ( 2003a ) compared the listening comprehension strategies of more- and less-skilled Canadian seventh-grade students of French. Students listened to several French texts and were prompted to think aloud during the process. The more skilled listeners used more metacognitive strategies, especially comprehension monitoring, than did their less skilled peers. In addition, more skilled listeners engaged in questioning for clarification, whereas the less skilled used more translation. Graham ( 2004 ) investigated the attitudes toward learning French of upper secondary English students and found that the less successful students did not seem to be aware of the potential role of learning strategies in improving their language performance.

These studies have confirmed that good language learners are skilled at matching strategies to the task they were working on, whereas less successful language learners apparently do not have the metacognitive knowledge about task requirements needed to select appropriate strategies. This trend is apparent with children in foreign language immersion classrooms, secondary school ESL and foreign language students, and adult language learners (Chamot & El-Dinary, 1999 ; Chamot & Keatley, 2003 ). In addition, more proficient L2 learners use sequences of strategies to complete a task effectively (Chamot, Barnhardt, El-Dinary, & Robbins, 1999 ; Goh, 2002b ; Oxford et al., 2004 ).

The large number of descriptive studies of language learning strategies reveals suggestive differences between more and less successful learners. Can less successful language learners be taught to use the learning strategies that contribute to the achievements of their more successful peers? Proponents of language learning strategy instruction point to the substantial body of research in first language contexts that supports the explicit teaching of learning strategies for academic achievement in other content areas (De La Paz & Graham, 2002 ; Graham & Harris, 2000 ; National Reading Panel, 2000 ; Pressley, 2000 ). Because learning strategy instruction has been shown to improve performance on first language tasks such as vocabulary learning, reading comprehension, and writing, it is likely that it could prove equally helpful for language learners in these and other L2 tasks such as listening and speaking, modalities not investigated in the first language literature.

Although the majority of language learning strategy investigations have been simply descriptive, a number of researchers have conducted studies in which language learning strategies have been taught to students. This section briefly reviews representative studies carried out in language classroom settings in which teachers and/or researchers have provided more or less explicit instruction on learning strategies.

The relatively small number of instructional language learning strategy studies may be due, in part, to the inherent difficulties in conducting classroom research. Ideally, an intervention study should have randomly assigned participants to either a control or an experimental/treatment group. Instruction in each group should be identical except for the presence or absence of the innovation being studied. Participants should be pre- and posttested on valid and reliable instruments that identify not only knowledge about and use of the innovation (e.g., learning strategies), but also measure other factors deemed important in learning, such as achievement/proficiency, motivation, attitude, and/or self-efficacy. It is rarely possible to adequately control for all of these possible variables in any natural classroom setting.

In one of the first experimental studies of language learning strategies instruction, high school ESL students were taught how to apply learning strategies to three different types of tasks, and their performance was compared to that of students in a nonstrategies control group (O'Malley & Chamot, 1990 ). This study was conducted with 75 high school ESL students randomly assigned to experimental or control groups. Students were pretested on three types of language tasks— vocabulary, listening comprehension, and speaking from prepared notes—but not on their use of learning strategies. The experimental group students were taught various strategies for the same types of tasks over a two week period. The instruction was provided by the researchers, all of whom had ESL teaching experience. Students were posttested on the same types of tasks, but did not report on their use of learning strategies. The main conclusions of this first language learning strategies experimental study were as follows:

• Vocabulary learning strategies were effective only for students who had not already developed alternative effective strategies.
• Listening comprehension improved for students instructed in learning strategies on texts that were accessible, not on those that were too difficult and/or for which students lacked relevant prior knowledge.
• Oral reports (presented from written notes) given by strategy-instructed students were judged to be significantly more comprehensible and organized than those of control group students.
• Explicit learning strategy instruction embedded within the language syllabus appeared to be effective.

These conclusions support some of the major tenets proposed in current language learning strategy instructional models, including the importance of not overlooking students' current learning strategies, careful choice of tasks for practicing learning strategies, and providing explicit and embedded learning strategy instruction. Although this study of L2 learning strategies was successful in showing that second language learners could improve their language performance by using instructed learning strategies, limitations of the study are clear. These interrelated limitations include the study's short duration (only two weeks) and absence of follow-up; the lack of a measure of students' use of learning strategies prior and subsequent to instruction; and the fact that researchers rather than the normal classroom teachers provided the instruction. Many of these limitations have been addressed in subsequent intervention studies regarding the effects of language learning strategy instruction for listening comprehension, speaking and oral communication, reading comprehension, vocabulary learning, and writing strategies.

Listening Comprehension Strategies Studies

Several studies have sought to help language learners use strategies to increase their comprehension of oral texts. For example, Ross and Rost ( 1991 ) first identified the listening comprehension strategies used by higher proficiency students and then successfully taught these to lower proficiency students. Another study of listening comprehension was conducted over an entire academic year (Thompson & Rubin, 1996 ). Students receiving strategy instruction showed significant improvement on a video comprehension posttest compared to the students in the control group. In addition, students in the strategies group demonstrated metacognitive awareness through their ability to select and manage the strategies that would help them comprehend the videos.

More recently, Ozeki ( 2000 ) followed the example of Ross and Rost ( 1991 ) by first identifying the listening strategies students already used as a basis for selecting strategies to be taught. In this case, however, the strategies to be taught were those students had reported that they used least frequently. Although intact classes of students of English in a Japanese women's college were used for the treatment and control groups, randomization was achieved by the assignment of students to class sections alphabetically by surname. Strategy instruction was provided in the treatment class during 12 ninety-minute classes focusing on listening comprehension distributed over a 20-week semester. The sequence of instruction was as follows: a preparation stage in which students were explicitly taught a new strategy and earlier strategies were reviewed; and a lesson stage in which students practiced the strategies with listening comprehension tasks. Pretest and posttest scores were compared to evaluate the effects of learning strategy instruction. Improvement in the treatment group was noted in the following dimensions: development of listening comprehension ability; increased use of learning strategies (including some not explicitly taught); positive attitudes towards strategy instruction; transfer of strategies to new tasks; and durability of strategy use after the completion of strategy instruction.

Carrier ( 2003 ) taught listening comprehension strategies to a small group of high school ESL students. This exploratory study focused on academic listening tasks during six weeks of instruction. The strategies included both bottom-up and top-down approaches to listening. The teacher modeled and defined the strategies, then provided practice opportunities for the students. Actual strategies taught included selective attention to various aspects of the text and note-taking . Pre- and posttests on both discrete and overall listening comprehension showed that students had significantly improved both aspects of listening comprehension.

In another recent study of listening comprehension strategies, Vandergrift ( 2003b ) undertook a study of French as a second language university students in which he sought to raise awareness of the listening process through tasks designed to develop effective listening strategies. After being told the topic of the listening task, students completed a column on a worksheet in which they listed (in French and/or in English) their predictions about information they might hear. Then they listened to the text, checking off predictions and vocabulary they had anticipated and adding new information. Next, they worked in pairs to compare and discuss what they had understood. A second listening to the text allowed students to fill in additional information comprehended, and this was followed by a class discussion in which students shared the strategies they had used to comprehend the text. After a third listening, students wrote a personal reflection on what they had learned about their own listening processes and what strategies they might use in future to improve listening comprehension. Similar procedures were followed for an additional listening task. Students' written reflections revealed positive reactions to the strategies, increased motivation, and understanding of their own thinking processes during listening tasks.

Oral Communication Strategies Studies

Perhaps the most challenging language modality for learning strategy instruction is oral communication, for deliberate use of a strategy could restrict the flow of natural speech. Presentational speaking, rather than interactive speaking, has been the focus of several studies (see Cohen, 1998 ; O'Malley & Chamot, 1990 ). In interactive speaking, researchers have looked at communication strategies with some reservations because of doubts that using a communication strategy (such as using a gesture when the needed word or phrase is not known) actually can lead to learning (Cohen, 1998 ; Macaro, 2001 , Nakatani, in press).

A comprehensive study of speaking strategies investigated the impact of strategies-based instruction on college foreign language students taught by their regular instructors over during 10 weeks of instruction (Cohen, et al., 1998 ; Cohen, 1998 ). The intervention groups received instruction in learning strategies for speaking tasks. Students were pre- and post-tested on speaking tasks and on the Strategy Inventory for Language Learning (SILL) (Oxford, 1990 ). In addition, a sample of students provided think-aloud data as they were completing task checklists. The results indicated that integrating strategies instruction into the language course was beneficial to students, although the relationship of reported strategy use to performance was complex.

In a recent study of oral communication strategies, Nakatani ( in press ) compared pre- and posttest oral communication test results of students receiving metacognitive awareness-raising and a control group. The subjects were students at a women's college in Japan who had completed six years of prior English study. The strategy training group was taught communication strategies that could help students learn more of the language such as asking for clarification, checking for comprehension, and paraphrasing, rather than communication strategies without a direct influence on learning, such as abandoning a message or reverting to the L1. Results showed that students taught to use strategies showed significant improvement on oral proficiency tests.

Reading Comprehension Strategies Studies

Although reading strategy interventions in first language contexts have been plentiful (see, for example, Pressley, 2000 ), this modality has attracted less attention among language learning instruction researchers. A recent study investigating different approaches to literacy development in high school ESL students with low literacy in their native language included a learning strategies instructional component (Chamot & Keatley, 2003 ). A curriculum of scripted literacy lessons included explicit language learning strategy instruction for reading comprehension, including sounding out, selective attention, summarizing, cooperation, predicting, brainstorming of prior knowledge, visualization, and making inferences. Six of the teachers provided initial strategy instruction in the students' L1, then asked students to use the same strategies when reading in English. The remaining eight teachers attempted to teach the strategies only in English. Data from classroom observations and from end-of-year individual think-aloud interviews in which students described (in L1) the strategies they were using to read an unfamiliar text in English showed the following:

• [bull ] Teachers found it easier to teach strategies in the native language.
• [bull ] Some students reported using the instructed strategies during the think-aloud interviews.
• [bull ] Students who were more able to verbalize their thinking processes (in L1) displayed greater comprehension of the L2 text than those unable to describe their thoughts.

Another recent study of reading comprehension investigated the effects of strategy instruction on lower and higher proficiency levels and also assessed students' continuing use of strategies after the conclusion of instruction (Ikeda & Takeuchi, 2003 ). Participants were 210 students of English at a Japanese university. Students were divided into two groups according to their English language proficiency; each group was then further divided into an experimental and a control group. The experimental groups received explicit reading strategy instruction integrated into their regular class over an eight-week period. Instructed strategies included making inferences, using selective attention, using imagery, and summarizing . Pre- and posttests (carried out at different intervals) consisted of reading English texts, then completing a survey in Japanese of strategies used during the reading task. The results indicated that the strategy instruction affected the frequency of students' use of the strategies only for the high proficiency level group. The authors' interpretation was that most of the strategies taught involved top-down processing, but that what the low proficiency group probably needed was a focus on bottom-up processing strategies. Students were tested after instruction and then again three months and five months later to see if they continued to use the instructed strategies. An encouraging finding was that students retained their use of learning strategies for reading five months after the conclusion of instruction.

A recently completed study built on Ikeda and Takeuchi's ( 2003 ) work to further explore the effects of task difficulty in reading comprehension and use of strategies (Oxford et al., 2004 ). ESL college students completed two reading tasks (one easy, one difficult); these scores were used to determine whether students were eithermore- or less-proficient readers, and also completed questionnaires about their strategy use for the two readings. For the easy reading, there was little difference in strategy use between more and less proficient readers. However, for the more difficult reading, less proficient students actually used more strategies than their more proficient peers. The authors attributed this finding to the fact that the “difficult” reading was actually not much of a challenge for the higher proficiency students, and thus they did not need to use many learning strategies.

Vocabulary Strategies Studies

Learning new vocabulary in a second language is a continuing process rather than a single event. Beginning level students often believe that vocabulary learning is all that is involved in second language acquisition and may focus their efforts and strategies on this single component. Deep processing strategies such as association have been found more effective in vocabulary retention than rote repetition strategies (see Cohen & Aphek, 1981 ; Hulstijn, 1997 ; O'Malley & Chamot, 1990 ; Schmitt, 2000 ).

In a recent descriptive vocabulary study of Hong Kong university students learning English, Fan ( 2003 ) identified important implications for strategy instruction. For example, when students perceived that a strategy was useful, they used it more often than strategies they did not perceive as useful. Even so, students with higher vocabulary proficiency used strategies significantly more often even when they did not perceive them as useful. This finding suggests that students might use more learning strategies if teachers were to first convince students of their usefulness.

This approach was taken in a series of case studies in England in which researchers worked closely with five secondary teachers of modern languages as teachers experimented with learning strategy instruction for a variety of tasks (Grenfell & Harris, 1999 ). Three of the teachers focused on teaching memorization strategies for vocabulary. The strategy instruction was generally explicit and students' metacognition was developed through a variety of consciousness-raising activities. Most students were willing to adopt the new strategies, though they rarely used them in combination. Performance on tests indicated that the memorization strategies had been helpful for many in learning new vocabulary.

Writing Strategies Studies

Writing in a second language is arguably the most difficult of the modalities in which to achieve communicative competence. Beginning level students struggle with finding the words they need and remembering grammatical conventions, whereas more advanced students find it difficult to link their ideas with coherence and to produce appropriate target language discourse. Given these difficulties, instruction in writing strategies could be beneficial for second language learners.

A study of writing strategies instruction was recently conducted in England with six classes of secondary students of French (Macaro, 2001 ). In this Oxford Writing Project, classes were randomly assigned to control or experimental groups. Pre- and posttests included questionnaires, writing tasks, and think-aloud interviews during a French writing task. Students in the experimental groups received about five months of instruction on a variety of writing strategies that included the metacognitive strategies of advance preparation, monitoring, and evaluating. At posttest, experimental groups had made significant gains in the grammatical accuracy of their writing. In addition, they reported a change in their approach to writing, becoming less reliant on the teacher, more selective in their use of the dictionary, and more careful about their written work.

Another recent writing strategies study explored the effects of translation (a learning strategy) from the L1 on the quality of essays written in French by university students of French (Cohen & Brooks-Carson, 2001 ). Students were given prompts in the target language, then instructed to either write directly in French or to write the essay first in their L1, then translate it to French. Strategy checklists completed after students wrote the essays showed that students writing directly in French reported less thinking in English during the composing process and their essays were also rated higher than those who had gone through the translation process.

Although we can be cautiously optimistic about the effectiveness of learning strategy instruction, given that it has been well established in first language contexts and shows promise in second language learning, a number of issues still remain concerning specific teaching approaches. These include the language of strategy instruction, the practicality of integrating strategy instruction into the regular language class, and the use of metacognitive models to classify learning strategies for instructional purposes. Although these issues are far from resolved, some recent studies that have addressed them are briefly described in this section.

Language of Strategy Instruction

This issue is particular to teaching learning strategies to language learners. In first language contexts, strategies are taught through a language medium in which students are proficient, but in second or foreign language contexts, this is not necessarily so. Beginning level students, in particular, do not have the L2 proficiency to understand explanations of why and how to use learning strategies, yet postponing learning strategy instruction until intermediate or advanced level courses deprives beginners of tools that could enhance language learning and increase motivation for further study. It is probably not possible to avoid using the first language during strategy instruction for beginning to low intermediate level students (Macaro, 2001 ). Suggestions have been made to initially teach the learning strategies in the students' native language, assuming it is the same for all students and that the teacher knows the language; alternatively, teachers have been urged to give the strategy a target language name, explain how to use it in simple language, and model the strategy repeatedly (Chamot et al., 1999 ).

Some recent studies have used a combination of the native and target languages for strategy instruction. In an investigation of strategy instruction by secondary French and German teachers in London, some materials were in English (especially those used by students for planning and evaluating their own work), whereas checklists, descriptions of strategies, and strategy activities were written in the target language, simplified as needed (Grenfell & Harris, 1999 ). In a study of Japanese college students learning English as a foreign language, questionnaires, journal prompts, and self-evaluation checklists were written in “simple” English, but students could respond in Japanese; actual strategy instruction and review was conducted in English (Ozeki, 2000 ). In Chamot and Keatley's ( 2003 ) ESL literacy study, bilingual teachers were able to first teach the learning strategies in students' native language, then had them use the same strategies in English for similar reading tasks in English. Teachers providing instruction in English alone encountered difficulties in teaching learning strategies because of the low level of students' English proficiency, and most then abandoned the attempt to teach strategies. From these few studies, it seems clear that the issue of language of instruction in teaching language learning strategies is far from resolved, and may need to be addressed as a context-specific factor.

Explicit and Integrated Learning Strategy Instruction

Explicit instruction includes the development of students' awareness of their strategies, teacher modeling of strategic thinking, identifying the strategies by name, providing opportunities for practice and self-evaluation. Researchers in both L1 and L2 contexts agree that explicit instruction is far more effective than simply asking students to use one or more strategies and also fosters metacognition, students' ability to understand their own thinking and learning processes (Anderson, 2002 , in press; Carrier, 2003 ; Chamot, 2004 , 2005 ; Chamot et al., 1999 ; Cohen, 1998 , 2003 ; Goh, 2002b ; Graham & Harris, 2000 ; National Reading Panel, 2000; O'Malley & Chamot, 1990 ; Oxford & Leaver, 1996 ; Pressley, 2000 ; Shen, 2003 ).

Less agreement is found on whether strategy instruction should be integrated into and taught concurrently with the language course, or whether to provide a separate “how to learn” course independent of the language course. Although all of the studies reviewed here have included strategy instruction as part of the regular language class, it has been argued that strategies taught in a language class are less likely to transfer to other tasks and that it may not be practical to prepare all language teachers to teach strategies (Gu, 1996 ). Clearly, expertise in teaching language learning strategies must be integrated into pre- and in-service preparation if teachers are to provide it to their L2 students.

Impact of Task and Learner Context

As noted earlier, learning strategies are directed toward particular tasks that can vary in both obvious and subtle ways. Tasks differ depending on whether the context is a second language or foreign language setting and whether the learner's goal is to acquire social or academic language or both (Chamot, 2004 ; Cohen, 2003 ; Cummins, 2000 ; Oxford et al., 2004 ). Differences in strategy use also vary according to proficiency level. Takeuchi's ( 2003 ) multiple case studies of learner journals found that learners reported shifting their use of strategies as they advanced to higher proficiency levels. Similarly, a recent reading study found that perceived difficulty of the task affected use of learning strategies, which were used on more challenging tasks (Oxford et al., 2004 ).

The learner's goals, the context of the learning situation, and the cultural values of the learner's society will also influence choice and acceptability of language learning strategies. For example, in a culture that prizes individual competition and has organized its educational system around competitive tasks, successful language learners may prefer strategies that allow them to work alone rather than social strategies that call for collaboration with others.

Two SILL studies illustrate the learning strategy preferences reported by students in different cultural contexts. A study of ethnically Chinese, bilingual Singaporean university students studying a foreign language (French or Japanese) found that students reported a preference for social strategies as well as a disinclination to use affective strategies (Wharton, 2000 ). Another study examined the language learning strategies of students in a university advanced Spanish writing class and compared achievement on a writing sample between those students speaking Spanish as a first or heritage language and those learning Spanish as a foreign language (Olivares-Cuhat, 2002 ). As expected, students with a Spanish language background were graded higher on their writing samples than the other students, but they also showed a greater preference for affective and memory strategies, and these latter were highly correlated with writing achievement. Preliminary findings of a current study of learning strategies used by university students of less commonly taught languages indicate that both heritage speakers of Arabic and students of Arabic as a foreign language share many of the same challenges and consequent learning strategies for learning Modern Standard Arabic (MSA), but also demonstrate differences (Keatley, Chamot, Spokane, & Greenstreet, 2004 ). For instance, heritage speakers reported using metacognitive strategies to overcome interference from their Arabic dialects when they attempted to speak MSA, but, unlike the foreign language students, had no difficulty in discriminating Arabic sounds and hence did not report any learning strategies for listening comprehension.

The implications for teaching are that language teachers need to find out what learning strategies students already use for different tasks. An open discussion of reasons why students use the strategies they identify can help teachers understand cultural and contextual factors that may be influencing their students. This can lead to clarification of task demands where there is a mismatch with students' current learning strategies. By understanding the task more clearly, students will likely be more motivated to try new strategies to complete it.

Metacognitive Models

The development of students' metacognition, or their ability to understand and regulate their own thinking and learning, has been urged by a number of learning strategy researchers (Anderson, 2002 ; Chamot et al., 1999 ; National Capital Language Resource Center, 2003 ; Rubin, 2001 ; Wenden, 2000 ). Metacognition is believed to involve both declarative (self-knowledge, world knowledge, task knowledge, strategy knowledge) and procedural knowledge (planning for learning, monitoring a learning task while it is in progress, and evaluating learning once a task has been completed; Chamot, 1994 ). Evidence that language learners actually engage in metacognitive knowledge and processes is reported in most of the research on language learning strategies, both descriptive and instructional. Even young children in language immersion classrooms can often describe their thinking processes, demonstrating metacognitive awareness in their ability to describe their own thinking (Chamot, 1999 ).

There are several current models for strategy identification, development, and instruction that emphasize metacognition. My colleagues and I have proposed a metacognitive model for learning strategy instruction that includes four recursive (rather than sequential) processes: planning, monitoring, problem-solving, and evaluating (Chamot, 1994 ; Chamot et al., 1999 ). In this model, teachers select learning strategies to teach depending on the point in a learning task where students need the most help. For example, students who do not seem to realize that a learning task is not progressing well can be taught to monitor their comprehension, production, or recall so that they can identify difficulties and select problem-solving strategies to address the difficulties. Rubin ( 2001 ) equates self-management with self-regulation as defined in the first language learning strategies literature (see, for example, Pressley, 1995 ). Her learner self-management model includes five metacognitive strategies: plan, monitor, evaluate, problem-solve, and implement. The model is partly linear and partly recursive, and interacts with learners' knowledge and beliefs. Anderson ( 2002 ) proposes a five-stage interactive process that includes planning, selecting and using learning strategies, monitoring strategy use, orchestrating various strategies, and evaluating the strategies used. In addition to describing this metacognitive model, he also suggests how teachers can use it to teach students how to become better language learners.

Similarly, the National Capital Language Resource Center (NCLRC; 2003 ) has proposed a metacognitive model in which the learner's problem-solving goals are at the center of the circular model. Surrounding these learner goals are the metacognitive strategies of planning, monitoring, managing learning, and evaluating language learning and learning strategy effectiveness. Task-based learning strategies comprise the outer circle of the model and are grouped into four categories: use what you know, use your imagination, use your organizational skills, and use a variety of resources. Teacher resource guides developed for elementary immersion classrooms (NCLRC, 2003 ), high school foreign language classrooms (NCLRC, 2004a ), and higher education foreign language classrooms (NCLRC, 2004b ) apply this model to classroom instruction.

Developing Teacher Expertise

These metacognitive models of language learning strategies can serve an important instructional goal for learning strategy instruction in second and foreign language classrooms by offering a way to think about language learning strategies from the perspective of the learner and the teacher, rather than from that of the researcher (as has characterized the claims of different strategy classification systems, for example, Hsiao & Oxford, 2002 ). Comprehensive classification schemes of learner strategies are needed to describe the information derived from descriptive studies that seek to chart the subtle permutations and often slippery definitions of learners' self-reported strategies. However, these extended and complex definitions may be less useful in the language classroom where the teacher is trying to help students become more strategic as they cope with actual learning tasks rather than the hypothesized learning tasks proposed in the many questionnaires and interviews designed to identify strategies that language learners claim to use.

The study of language learning strategies will continue to develop as second language acquisition researchers seek to understand different learner characteristics and the complex cognitive, social, and affective processes involved in processing language input and using the language for a variety of purposes. Likewise, language educators and methodologists will continue their quest for more effective instructional approaches, and, with the increasing emphasis on learner-centered instruction and learner empowerment in all areas of education, instruction in learning strategies will assume a greater role in teacher preparation and curriculum design.

First, rigorous intervention studies would provide information about the effects of learning strategy instruction on achievement and language proficiency. Such studies need to be conducted with a variety of language students, including children in foreign language immersion and nonimmersion programs, school-aged students in bilingual and second language programs, older students with differing educational levels in their native language, and students in different learning contexts around the world.

A second area for future research is in the development of language teacher expertise for integrating learning strategies into classroom instruction. The evaluation of different models for teacher preparation in learning strategies instruction could lead to refining and improving current models. In addition, studies need to be undertaken to identify the relationship of effective learning strategy instruction to teacher characteristics such as teaching approach, attitude and teacher beliefs, amount and type of preservice and/or in-service preparation in learning strategy instruction, and years of teaching experience and length of time teaching learning strategies—it might be that effective learning strategy instruction is closely tied to specific individual teacher characteristics and experiences.

It is important that learning strategies research continue, both in these and other directions, for only through a better understanding of the learning and teaching process can more language learners achieve the level of success that currently characterizes only a small proportion of all students studying a foreign or second language around the world. Strategy instruction can contribute to development of learner mastery and autonomy and increased teacher expertise, but additional research in specific language learning contexts is essential to realizing its potential to enhance second language acquisition and instruction.

This article has been cited by the following publications. This list is generated based on data provided by Crossref .

• Google Scholar

View all Google Scholar citations for this article.

Save article to Kindle

To save this article to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle .

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

• Anna Uhl Chamot
• DOI: https://doi.org/10.1017/S0267190505000061

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox .

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive .

Reply to: Submit a response

- No HTML tags allowed - Web page URLs will display as text only - Lines and paragraphs break automatically - Attachments, images or tables are not permitted

Your details

Your email address will be used in order to notify you when your comment has been reviewed by the moderator and in case the author(s) of the article or the moderator need to contact you directly.

You have entered the maximum number of contributors

Conflicting interests.

Please list any fees and grants from, employment by, consultancy for, shared ownership in or any close relationship with, at any time over the preceding 36 months, any organisation whose interests may be affected by the publication of the response. Please also list any non-financial associations or interests (personal, professional, political, institutional, religious or other) that a reasonable reader would want to know about in relation to the submitted work. This pertains to all the authors of the piece, their spouses or partners.

Problem-Based Language Learning

• First Online: 22 July 2018

Cite this chapter

• Loghman Ansarian 3 &
• Mei Lin Teoh 4  

Part of the book series: SpringerBriefs in Education ((BRIEFSEDUCAT))

793 Accesses

1 Citations

In this chapter, we take a more in-depth look at PBL and aim to justify how it refashions the language teaching and learning processes. We shall examine some of the main features of PBL presented in the previous chapter in more detail. We also seize this opportunity to distinguish what PBL is and is not by differentiating between it and other teaching and learning approaches in the language learning field. Throughout, we discuss some difficulties associated with implementing PBL in language classes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Aliyue, M. M. (2017). The effect of problem-based learning on metacognition and writing performance of nigerian undergraduates . (Unpublished Doctoral Thesis). University Putra Malaysia, Malaysia.

Google Scholar  

Block, J. (2015, February 26). 5 PBL best practices for redefining the teachers role . Retrieved June 23, 2017, from https://www.edutopia.org/blog/pbl-practices-redefining-teachers-role-josh-block .

Boud, D. J., & Lubin, J. (1983). Self-assessment in professional education. Tertiary Education Research Centre , University of New South Wales.

Conant, J. (2009). Thomas Kuhn on the difference between a puzzle and a problem . Wittgenstein Archives at the University of Bergen.

Connelly, F. M., & Clandinin, D. J. (1990). Stories of experience and narrative inquiry. Educational researcher, 19 (5), 2–14.

Article   Google Scholar  

Duch, B. J. (2001). Writing problems for deeper understanding. In B. J. Duch, S. E. Groh, & D. E. Allen (Eds.), The power of problem-based learning: A practical “How to” for teaching undergraduate courses in nay discipline (pp. 47–53). Sterling, VA: Stylus.

Ertmer, P. A., & Simons, K. D. (2005). Scaffolding teachers’ efforts to implement problem-based learning. International Journal of Learning, 12 (4), 319–328.

Gregory, J. (2002). Facilitation and facilitator style. The Theory and Practice of Teaching , 79–93.

Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16 (3), 235–266.

Johnson, D. W., & Johnson, F. P. (2009). Joining together: Group theory and group skills (9th ed.). Boston: Allyn and Bacon.

Jonassen, D. H. (2000). Toward a design theory of problem solving. ETR&D, 48 (4), 63–85.

Jonassen, D. H. & Hung, W. (2008). All problems are not equal: Implications for problem-based learning. Interdisciplinary Journal of Problem-Based Learning, 2 (2).

Kolmos, A., Du, X., Holgaard, J. E., & Jensen, L. P. (2008). Facilitation in a PBL environment . UCPBL UNESCO Chair in Problem Based Learning.

Loyens, S. M., Magda, J., & Rikers, R. M. (2008). Self-directed learning in problem-based learning and its relationships with self-regulated learning. Educational Psychology Review, 20 (4), 411–427.

Minnaert, A., & Janssen, P. J. (1999). The additive effect of regulatory activities on top of intelligence in relation to academic performance in higher education. Learning and Instruction, 9, 77–91. https://doi.org/10.1016/S0959-4752(98)00019-X .

Paris, S. G., & Paris, A. H. (2001). Classroom applications of research on self-regulated learning. Educational Psychologist, 36, 89–101. https://doi.org/10.1207/S15326985EP3602_4 .

Rotgans, J. I., & Schmidt, H. G. (2011). Cognitive engagement in the problem-based learning classroom. Advances in Health Sciences Education, 16 (4), 465–479.

Sadaf, S., Ali, S. K., & Zuberi, R. W. (2009). Problem-based learning: Enhancing tutors’ facilitation skills using structured small group experiential learning. Education for Health, 22 (1), 96.

Savin-Baden, M., & Major, C. H. (2004). Foundations of problem-based learning . Berkshire: SRHE & Open University Press.

Schuck, S., & Russell, T. (2005). Self-study, critical friendship, and the complexities of teacher education. Studying Teacher Education, 1 (2), 107–121.

Shin, N., Jonassen, D. H., & McGee, S. (2003). Predictors of well-structured and ill-structured problem solving in an astronomy simulation. Journal of Research in Science Teaching, 40 (1), 6–33.

Skinner, V. J., Braunack-Mayer, A., & Winning, T. A. (2016). Another piece of the “Silence in PBL” puzzle: Students’ explanations of dominance and quietness as complementary group roles. Interdisciplinary Journal of Problem-Based Learning, 10 (2), 8.

Slavin, R. E. (1996). Research on cooperative learning and achievement: What we know, what we need to know. Contemporary Educational Psychology, 21 (1), 43–69.

Takahashi, Y. (2008). Problem-based learning and task-based learning: A practical synthesis. The Kaohsiung Journal of Medical Sciences, 24 (3), S31–S33.

Download references

Author information

Authors and affiliations.

Faculty of Languages and Linguistics, University of Malaya, Kuala Lumpur, Malaysia

Loghman Ansarian

Mei Lin Teoh

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Loghman Ansarian .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 The Author(s)

About this chapter

Ansarian, L., Teoh, M. (2018). Problem-Based Language Learning. In: Problem-based Language Learning and Teaching. SpringerBriefs in Education. Springer, Singapore. https://doi.org/10.1007/978-981-13-0941-0_2

Download citation

DOI : https://doi.org/10.1007/978-981-13-0941-0_2

Published : 22 July 2018

Publisher Name : Springer, Singapore

Print ISBN : 978-981-13-0940-3

Online ISBN : 978-981-13-0941-0

eBook Packages : Education Education (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Open access
• Published: 11 January 2023

The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature

• Enwei Xu   ORCID: orcid.org/0000-0001-6424-8169 1 ,
• Wei Wang 1 &
• Qingxia Wang 1  

Humanities and Social Sciences Communications volume  10 , Article number:  16 ( 2023 ) Cite this article

19k Accesses

22 Citations

3 Altmetric

Metrics details

• Science, technology and society

Collaborative problem-solving has been widely embraced in the classroom instruction of critical thinking, which is regarded as the core of curriculum reform based on key competencies in the field of education as well as a key competence for learners in the 21st century. However, the effectiveness of collaborative problem-solving in promoting students’ critical thinking remains uncertain. This current research presents the major findings of a meta-analysis of 36 pieces of the literature revealed in worldwide educational periodicals during the 21st century to identify the effectiveness of collaborative problem-solving in promoting students’ critical thinking and to determine, based on evidence, whether and to what extent collaborative problem solving can result in a rise or decrease in critical thinking. The findings show that (1) collaborative problem solving is an effective teaching approach to foster students’ critical thinking, with a significant overall effect size (ES = 0.82, z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]); (2) in respect to the dimensions of critical thinking, collaborative problem solving can significantly and successfully enhance students’ attitudinal tendencies (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI[0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI[0.58, 0.82]); and (3) the teaching type (chi 2  = 7.20, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), and learning scaffold (chi 2  = 9.03, P  < 0.01) all have an impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. On the basis of these results, recommendations are made for further study and instruction to better support students’ critical thinking in the context of collaborative problem-solving.

Similar content being viewed by others

A meta-analysis of the effects of design thinking on student learning

Fostering twenty-first century skills among primary school students through math project-based learning

A meta-analysis to gauge the impact of pedagogies employed in mixed-ability high school biology classrooms

Introduction.

Although critical thinking has a long history in research, the concept of critical thinking, which is regarded as an essential competence for learners in the 21st century, has recently attracted more attention from researchers and teaching practitioners (National Research Council, 2012 ). Critical thinking should be the core of curriculum reform based on key competencies in the field of education (Peng and Deng, 2017 ) because students with critical thinking can not only understand the meaning of knowledge but also effectively solve practical problems in real life even after knowledge is forgotten (Kek and Huijser, 2011 ). The definition of critical thinking is not universal (Ennis, 1989 ; Castle, 2009 ; Niu et al., 2013 ). In general, the definition of critical thinking is a self-aware and self-regulated thought process (Facione, 1990 ; Niu et al., 2013 ). It refers to the cognitive skills needed to interpret, analyze, synthesize, reason, and evaluate information as well as the attitudinal tendency to apply these abilities (Halpern, 2001 ). The view that critical thinking can be taught and learned through curriculum teaching has been widely supported by many researchers (e.g., Kuncel, 2011 ; Leng and Lu, 2020 ), leading to educators’ efforts to foster it among students. In the field of teaching practice, there are three types of courses for teaching critical thinking (Ennis, 1989 ). The first is an independent curriculum in which critical thinking is taught and cultivated without involving the knowledge of specific disciplines; the second is an integrated curriculum in which critical thinking is integrated into the teaching of other disciplines as a clear teaching goal; and the third is a mixed curriculum in which critical thinking is taught in parallel to the teaching of other disciplines for mixed teaching training. Furthermore, numerous measuring tools have been developed by researchers and educators to measure critical thinking in the context of teaching practice. These include standardized measurement tools, such as WGCTA, CCTST, CCTT, and CCTDI, which have been verified by repeated experiments and are considered effective and reliable by international scholars (Facione and Facione, 1992 ). In short, descriptions of critical thinking, including its two dimensions of attitudinal tendency and cognitive skills, different types of teaching courses, and standardized measurement tools provide a complex normative framework for understanding, teaching, and evaluating critical thinking.

Cultivating critical thinking in curriculum teaching can start with a problem, and one of the most popular critical thinking instructional approaches is problem-based learning (Liu et al., 2020 ). Duch et al. ( 2001 ) noted that problem-based learning in group collaboration is progressive active learning, which can improve students’ critical thinking and problem-solving skills. Collaborative problem-solving is the organic integration of collaborative learning and problem-based learning, which takes learners as the center of the learning process and uses problems with poor structure in real-world situations as the starting point for the learning process (Liang et al., 2017 ). Students learn the knowledge needed to solve problems in a collaborative group, reach a consensus on problems in the field, and form solutions through social cooperation methods, such as dialogue, interpretation, questioning, debate, negotiation, and reflection, thus promoting the development of learners’ domain knowledge and critical thinking (Cindy, 2004 ; Liang et al., 2017 ).

Collaborative problem-solving has been widely used in the teaching practice of critical thinking, and several studies have attempted to conduct a systematic review and meta-analysis of the empirical literature on critical thinking from various perspectives. However, little attention has been paid to the impact of collaborative problem-solving on critical thinking. Therefore, the best approach for developing and enhancing critical thinking throughout collaborative problem-solving is to examine how to implement critical thinking instruction; however, this issue is still unexplored, which means that many teachers are incapable of better instructing critical thinking (Leng and Lu, 2020 ; Niu et al., 2013 ). For example, Huber ( 2016 ) provided the meta-analysis findings of 71 publications on gaining critical thinking over various time frames in college with the aim of determining whether critical thinking was truly teachable. These authors found that learners significantly improve their critical thinking while in college and that critical thinking differs with factors such as teaching strategies, intervention duration, subject area, and teaching type. The usefulness of collaborative problem-solving in fostering students’ critical thinking, however, was not determined by this study, nor did it reveal whether there existed significant variations among the different elements. A meta-analysis of 31 pieces of educational literature was conducted by Liu et al. ( 2020 ) to assess the impact of problem-solving on college students’ critical thinking. These authors found that problem-solving could promote the development of critical thinking among college students and proposed establishing a reasonable group structure for problem-solving in a follow-up study to improve students’ critical thinking. Additionally, previous empirical studies have reached inconclusive and even contradictory conclusions about whether and to what extent collaborative problem-solving increases or decreases critical thinking levels. As an illustration, Yang et al. ( 2008 ) carried out an experiment on the integrated curriculum teaching of college students based on a web bulletin board with the goal of fostering participants’ critical thinking in the context of collaborative problem-solving. These authors’ research revealed that through sharing, debating, examining, and reflecting on various experiences and ideas, collaborative problem-solving can considerably enhance students’ critical thinking in real-life problem situations. In contrast, collaborative problem-solving had a positive impact on learners’ interaction and could improve learning interest and motivation but could not significantly improve students’ critical thinking when compared to traditional classroom teaching, according to research by Naber and Wyatt ( 2014 ) and Sendag and Odabasi ( 2009 ) on undergraduate and high school students, respectively.

The above studies show that there is inconsistency regarding the effectiveness of collaborative problem-solving in promoting students’ critical thinking. Therefore, it is essential to conduct a thorough and trustworthy review to detect and decide whether and to what degree collaborative problem-solving can result in a rise or decrease in critical thinking. Meta-analysis is a quantitative analysis approach that is utilized to examine quantitative data from various separate studies that are all focused on the same research topic. This approach characterizes the effectiveness of its impact by averaging the effect sizes of numerous qualitative studies in an effort to reduce the uncertainty brought on by independent research and produce more conclusive findings (Lipsey and Wilson, 2001 ).

This paper used a meta-analytic approach and carried out a meta-analysis to examine the effectiveness of collaborative problem-solving in promoting students’ critical thinking in order to make a contribution to both research and practice. The following research questions were addressed by this meta-analysis:

What is the overall effect size of collaborative problem-solving in promoting students’ critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills)?

How are the disparities between the study conclusions impacted by various moderating variables if the impacts of various experimental designs in the included studies are heterogeneous?

This research followed the strict procedures (e.g., database searching, identification, screening, eligibility, merging, duplicate removal, and analysis of included studies) of Cooper’s ( 2010 ) proposed meta-analysis approach for examining quantitative data from various separate studies that are all focused on the same research topic. The relevant empirical research that appeared in worldwide educational periodicals within the 21st century was subjected to this meta-analysis using Rev-Man 5.4. The consistency of the data extracted separately by two researchers was tested using Cohen’s kappa coefficient, and a publication bias test and a heterogeneity test were run on the sample data to ascertain the quality of this meta-analysis.

Data sources and search strategies

There were three stages to the data collection process for this meta-analysis, as shown in Fig. 1 , which shows the number of articles included and eliminated during the selection process based on the statement and study eligibility criteria.

This flowchart shows the number of records identified, included and excluded in the article.

First, the databases used to systematically search for relevant articles were the journal papers of the Web of Science Core Collection and the Chinese Core source journal, as well as the Chinese Social Science Citation Index (CSSCI) source journal papers included in CNKI. These databases were selected because they are credible platforms that are sources of scholarly and peer-reviewed information with advanced search tools and contain literature relevant to the subject of our topic from reliable researchers and experts. The search string with the Boolean operator used in the Web of Science was “TS = (((“critical thinking” or “ct” and “pretest” or “posttest”) or (“critical thinking” or “ct” and “control group” or “quasi experiment” or “experiment”)) and (“collaboration” or “collaborative learning” or “CSCL”) and (“problem solving” or “problem-based learning” or “PBL”))”. The research area was “Education Educational Research”, and the search period was “January 1, 2000, to December 30, 2021”. A total of 412 papers were obtained. The search string with the Boolean operator used in the CNKI was “SU = (‘critical thinking’*‘collaboration’ + ‘critical thinking’*‘collaborative learning’ + ‘critical thinking’*‘CSCL’ + ‘critical thinking’*‘problem solving’ + ‘critical thinking’*‘problem-based learning’ + ‘critical thinking’*‘PBL’ + ‘critical thinking’*‘problem oriented’) AND FT = (‘experiment’ + ‘quasi experiment’ + ‘pretest’ + ‘posttest’ + ‘empirical study’)” (translated into Chinese when searching). A total of 56 studies were found throughout the search period of “January 2000 to December 2021”. From the databases, all duplicates and retractions were eliminated before exporting the references into Endnote, a program for managing bibliographic references. In all, 466 studies were found.

Second, the studies that matched the inclusion and exclusion criteria for the meta-analysis were chosen by two researchers after they had reviewed the abstracts and titles of the gathered articles, yielding a total of 126 studies.

Third, two researchers thoroughly reviewed each included article’s whole text in accordance with the inclusion and exclusion criteria. Meanwhile, a snowball search was performed using the references and citations of the included articles to ensure complete coverage of the articles. Ultimately, 36 articles were kept.

Two researchers worked together to carry out this entire process, and a consensus rate of almost 94.7% was reached after discussion and negotiation to clarify any emerging differences.

Eligibility criteria

Since not all the retrieved studies matched the criteria for this meta-analysis, eligibility criteria for both inclusion and exclusion were developed as follows:

The publication language of the included studies was limited to English and Chinese, and the full text could be obtained. Articles that did not meet the publication language and articles not published between 2000 and 2021 were excluded.

The research design of the included studies must be empirical and quantitative studies that can assess the effect of collaborative problem-solving on the development of critical thinking. Articles that could not identify the causal mechanisms by which collaborative problem-solving affects critical thinking, such as review articles and theoretical articles, were excluded.

The research method of the included studies must feature a randomized control experiment or a quasi-experiment, or a natural experiment, which have a higher degree of internal validity with strong experimental designs and can all plausibly provide evidence that critical thinking and collaborative problem-solving are causally related. Articles with non-experimental research methods, such as purely correlational or observational studies, were excluded.

The participants of the included studies were only students in school, including K-12 students and college students. Articles in which the participants were non-school students, such as social workers or adult learners, were excluded.

The research results of the included studies must mention definite signs that may be utilized to gauge critical thinking’s impact (e.g., sample size, mean value, or standard deviation). Articles that lacked specific measurement indicators for critical thinking and could not calculate the effect size were excluded.

Data coding design

In order to perform a meta-analysis, it is necessary to collect the most important information from the articles, codify that information’s properties, and convert descriptive data into quantitative data. Therefore, this study designed a data coding template (see Table 1 ). Ultimately, 16 coding fields were retained.

The designed data-coding template consisted of three pieces of information. Basic information about the papers was included in the descriptive information: the publishing year, author, serial number, and title of the paper.

The variable information for the experimental design had three variables: the independent variable (instruction method), the dependent variable (critical thinking), and the moderating variable (learning stage, teaching type, intervention duration, learning scaffold, group size, measuring tool, and subject area). Depending on the topic of this study, the intervention strategy, as the independent variable, was coded into collaborative and non-collaborative problem-solving. The dependent variable, critical thinking, was coded as a cognitive skill and an attitudinal tendency. And seven moderating variables were created by grouping and combining the experimental design variables discovered within the 36 studies (see Table 1 ), where learning stages were encoded as higher education, high school, middle school, and primary school or lower; teaching types were encoded as mixed courses, integrated courses, and independent courses; intervention durations were encoded as 0–1 weeks, 1–4 weeks, 4–12 weeks, and more than 12 weeks; group sizes were encoded as 2–3 persons, 4–6 persons, 7–10 persons, and more than 10 persons; learning scaffolds were encoded as teacher-supported learning scaffold, technique-supported learning scaffold, and resource-supported learning scaffold; measuring tools were encoded as standardized measurement tools (e.g., WGCTA, CCTT, CCTST, and CCTDI) and self-adapting measurement tools (e.g., modified or made by researchers); and subject areas were encoded according to the specific subjects used in the 36 included studies.

The data information contained three metrics for measuring critical thinking: sample size, average value, and standard deviation. It is vital to remember that studies with various experimental designs frequently adopt various formulas to determine the effect size. And this paper used Morris’ proposed standardized mean difference (SMD) calculation formula ( 2008 , p. 369; see Supplementary Table S3 ).

Procedure for extracting and coding data

According to the data coding template (see Table 1 ), the 36 papers’ information was retrieved by two researchers, who then entered them into Excel (see Supplementary Table S1 ). The results of each study were extracted separately in the data extraction procedure if an article contained numerous studies on critical thinking, or if a study assessed different critical thinking dimensions. For instance, Tiwari et al. ( 2010 ) used four time points, which were viewed as numerous different studies, to examine the outcomes of critical thinking, and Chen ( 2013 ) included the two outcome variables of attitudinal tendency and cognitive skills, which were regarded as two studies. After discussion and negotiation during data extraction, the two researchers’ consistency test coefficients were roughly 93.27%. Supplementary Table S2 details the key characteristics of the 36 included articles with 79 effect quantities, including descriptive information (e.g., the publishing year, author, serial number, and title of the paper), variable information (e.g., independent variables, dependent variables, and moderating variables), and data information (e.g., mean values, standard deviations, and sample size). Following that, testing for publication bias and heterogeneity was done on the sample data using the Rev-Man 5.4 software, and then the test results were used to conduct a meta-analysis.

Publication bias test

When the sample of studies included in a meta-analysis does not accurately reflect the general status of research on the relevant subject, publication bias is said to be exhibited in this research. The reliability and accuracy of the meta-analysis may be impacted by publication bias. Due to this, the meta-analysis needs to check the sample data for publication bias (Stewart et al., 2006 ). A popular method to check for publication bias is the funnel plot; and it is unlikely that there will be publishing bias when the data are equally dispersed on either side of the average effect size and targeted within the higher region. The data are equally dispersed within the higher portion of the efficient zone, consistent with the funnel plot connected with this analysis (see Fig. 2 ), indicating that publication bias is unlikely in this situation.

This funnel plot shows the result of publication bias of 79 effect quantities across 36 studies.

Heterogeneity test

To select the appropriate effect models for the meta-analysis, one might use the results of a heterogeneity test on the data effect sizes. In a meta-analysis, it is common practice to gauge the degree of data heterogeneity using the I 2 value, and I 2  ≥ 50% is typically understood to denote medium-high heterogeneity, which calls for the adoption of a random effect model; if not, a fixed effect model ought to be applied (Lipsey and Wilson, 2001 ). The findings of the heterogeneity test in this paper (see Table 2 ) revealed that I 2 was 86% and displayed significant heterogeneity ( P  < 0.01). To ensure accuracy and reliability, the overall effect size ought to be calculated utilizing the random effect model.

The analysis of the overall effect size

This meta-analysis utilized a random effect model to examine 79 effect quantities from 36 studies after eliminating heterogeneity. In accordance with Cohen’s criterion (Cohen, 1992 ), it is abundantly clear from the analysis results, which are shown in the forest plot of the overall effect (see Fig. 3 ), that the cumulative impact size of cooperative problem-solving is 0.82, which is statistically significant ( z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]), and can encourage learners to practice critical thinking.

This forest plot shows the analysis result of the overall effect size across 36 studies.

In addition, this study examined two distinct dimensions of critical thinking to better understand the precise contributions that collaborative problem-solving makes to the growth of critical thinking. The findings (see Table 3 ) indicate that collaborative problem-solving improves cognitive skills (ES = 0.70) and attitudinal tendency (ES = 1.17), with significant intergroup differences (chi 2  = 7.95, P  < 0.01). Although collaborative problem-solving improves both dimensions of critical thinking, it is essential to point out that the improvements in students’ attitudinal tendency are much more pronounced and have a significant comprehensive effect (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI [0.87, 1.47]), whereas gains in learners’ cognitive skill are slightly improved and are just above average. (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI [0.58, 0.82]).

The analysis of moderator effect size

The whole forest plot’s 79 effect quantities underwent a two-tailed test, which revealed significant heterogeneity ( I 2  = 86%, z  = 12.78, P  < 0.01), indicating differences between various effect sizes that may have been influenced by moderating factors other than sampling error. Therefore, exploring possible moderating factors that might produce considerable heterogeneity was done using subgroup analysis, such as the learning stage, learning scaffold, teaching type, group size, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, in order to further explore the key factors that influence critical thinking. The findings (see Table 4 ) indicate that various moderating factors have advantageous effects on critical thinking. In this situation, the subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), learning scaffold (chi 2  = 9.03, P  < 0.01), and teaching type (chi 2  = 7.20, P  < 0.05) are all significant moderators that can be applied to support the cultivation of critical thinking. However, since the learning stage and the measuring tools did not significantly differ among intergroup (chi 2  = 3.15, P  = 0.21 > 0.05, and chi 2  = 0.08, P  = 0.78 > 0.05), we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving. These are the precise outcomes, as follows:

Various learning stages influenced critical thinking positively, without significant intergroup differences (chi 2  = 3.15, P  = 0.21 > 0.05). High school was first on the list of effect sizes (ES = 1.36, P  < 0.01), then higher education (ES = 0.78, P  < 0.01), and middle school (ES = 0.73, P  < 0.01). These results show that, despite the learning stage’s beneficial influence on cultivating learners’ critical thinking, we are unable to explain why it is essential for cultivating critical thinking in the context of collaborative problem-solving.

Different teaching types had varying degrees of positive impact on critical thinking, with significant intergroup differences (chi 2  = 7.20, P  < 0.05). The effect size was ranked as follows: mixed courses (ES = 1.34, P  < 0.01), integrated courses (ES = 0.81, P  < 0.01), and independent courses (ES = 0.27, P  < 0.01). These results indicate that the most effective approach to cultivate critical thinking utilizing collaborative problem solving is through the teaching type of mixed courses.

Various intervention durations significantly improved critical thinking, and there were significant intergroup differences (chi 2  = 12.18, P  < 0.01). The effect sizes related to this variable showed a tendency to increase with longer intervention durations. The improvement in critical thinking reached a significant level (ES = 0.85, P  < 0.01) after more than 12 weeks of training. These findings indicate that the intervention duration and critical thinking’s impact are positively correlated, with a longer intervention duration having a greater effect.

Different learning scaffolds influenced critical thinking positively, with significant intergroup differences (chi 2  = 9.03, P  < 0.01). The resource-supported learning scaffold (ES = 0.69, P  < 0.01) acquired a medium-to-higher level of impact, the technique-supported learning scaffold (ES = 0.63, P  < 0.01) also attained a medium-to-higher level of impact, and the teacher-supported learning scaffold (ES = 0.92, P  < 0.01) displayed a high level of significant impact. These results show that the learning scaffold with teacher support has the greatest impact on cultivating critical thinking.

Various group sizes influenced critical thinking positively, and the intergroup differences were statistically significant (chi 2  = 8.77, P  < 0.05). Critical thinking showed a general declining trend with increasing group size. The overall effect size of 2–3 people in this situation was the biggest (ES = 0.99, P  < 0.01), and when the group size was greater than 7 people, the improvement in critical thinking was at the lower-middle level (ES < 0.5, P  < 0.01). These results show that the impact on critical thinking is positively connected with group size, and as group size grows, so does the overall impact.

Various measuring tools influenced critical thinking positively, with significant intergroup differences (chi 2  = 0.08, P  = 0.78 > 0.05). In this situation, the self-adapting measurement tools obtained an upper-medium level of effect (ES = 0.78), whereas the complete effect size of the standardized measurement tools was the largest, achieving a significant level of effect (ES = 0.84, P  < 0.01). These results show that, despite the beneficial influence of the measuring tool on cultivating critical thinking, we are unable to explain why it is crucial in fostering the growth of critical thinking by utilizing the approach of collaborative problem-solving.

Different subject areas had a greater impact on critical thinking, and the intergroup differences were statistically significant (chi 2  = 13.36, P  < 0.05). Mathematics had the greatest overall impact, achieving a significant level of effect (ES = 1.68, P  < 0.01), followed by science (ES = 1.25, P  < 0.01) and medical science (ES = 0.87, P  < 0.01), both of which also achieved a significant level of effect. Programming technology was the least effective (ES = 0.39, P  < 0.01), only having a medium-low degree of effect compared to education (ES = 0.72, P  < 0.01) and other fields (such as language, art, and social sciences) (ES = 0.58, P  < 0.01). These results suggest that scientific fields (e.g., mathematics, science) may be the most effective subject areas for cultivating critical thinking utilizing the approach of collaborative problem-solving.

The effectiveness of collaborative problem solving with regard to teaching critical thinking

According to this meta-analysis, using collaborative problem-solving as an intervention strategy in critical thinking teaching has a considerable amount of impact on cultivating learners’ critical thinking as a whole and has a favorable promotional effect on the two dimensions of critical thinking. According to certain studies, collaborative problem solving, the most frequently used critical thinking teaching strategy in curriculum instruction can considerably enhance students’ critical thinking (e.g., Liang et al., 2017 ; Liu et al., 2020 ; Cindy, 2004 ). This meta-analysis provides convergent data support for the above research views. Thus, the findings of this meta-analysis not only effectively address the first research query regarding the overall effect of cultivating critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills) utilizing the approach of collaborative problem-solving, but also enhance our confidence in cultivating critical thinking by using collaborative problem-solving intervention approach in the context of classroom teaching.

Furthermore, the associated improvements in attitudinal tendency are much stronger, but the corresponding improvements in cognitive skill are only marginally better. According to certain studies, cognitive skill differs from the attitudinal tendency in classroom instruction; the cultivation and development of the former as a key ability is a process of gradual accumulation, while the latter as an attitude is affected by the context of the teaching situation (e.g., a novel and exciting teaching approach, challenging and rewarding tasks) (Halpern, 2001 ; Wei and Hong, 2022 ). Collaborative problem-solving as a teaching approach is exciting and interesting, as well as rewarding and challenging; because it takes the learners as the focus and examines problems with poor structure in real situations, and it can inspire students to fully realize their potential for problem-solving, which will significantly improve their attitudinal tendency toward solving problems (Liu et al., 2020 ). Similar to how collaborative problem-solving influences attitudinal tendency, attitudinal tendency impacts cognitive skill when attempting to solve a problem (Liu et al., 2020 ; Zhang et al., 2022 ), and stronger attitudinal tendencies are associated with improved learning achievement and cognitive ability in students (Sison, 2008 ; Zhang et al., 2022 ). It can be seen that the two specific dimensions of critical thinking as well as critical thinking as a whole are affected by collaborative problem-solving, and this study illuminates the nuanced links between cognitive skills and attitudinal tendencies with regard to these two dimensions of critical thinking. To fully develop students’ capacity for critical thinking, future empirical research should pay closer attention to cognitive skills.

The moderating effects of collaborative problem solving with regard to teaching critical thinking

In order to further explore the key factors that influence critical thinking, exploring possible moderating effects that might produce considerable heterogeneity was done using subgroup analysis. The findings show that the moderating factors, such as the teaching type, learning stage, group size, learning scaffold, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, could all support the cultivation of collaborative problem-solving in critical thinking. Among them, the effect size differences between the learning stage and measuring tool are not significant, which does not explain why these two factors are crucial in supporting the cultivation of critical thinking utilizing the approach of collaborative problem-solving.

In terms of the learning stage, various learning stages influenced critical thinking positively without significant intergroup differences, indicating that we are unable to explain why it is crucial in fostering the growth of critical thinking.

Although high education accounts for 70.89% of all empirical studies performed by researchers, high school may be the appropriate learning stage to foster students’ critical thinking by utilizing the approach of collaborative problem-solving since it has the largest overall effect size. This phenomenon may be related to student’s cognitive development, which needs to be further studied in follow-up research.

With regard to teaching type, mixed course teaching may be the best teaching method to cultivate students’ critical thinking. Relevant studies have shown that in the actual teaching process if students are trained in thinking methods alone, the methods they learn are isolated and divorced from subject knowledge, which is not conducive to their transfer of thinking methods; therefore, if students’ thinking is trained only in subject teaching without systematic method training, it is challenging to apply to real-world circumstances (Ruggiero, 2012 ; Hu and Liu, 2015 ). Teaching critical thinking as mixed course teaching in parallel to other subject teachings can achieve the best effect on learners’ critical thinking, and explicit critical thinking instruction is more effective than less explicit critical thinking instruction (Bensley and Spero, 2014 ).

In terms of the intervention duration, with longer intervention times, the overall effect size shows an upward tendency. Thus, the intervention duration and critical thinking’s impact are positively correlated. Critical thinking, as a key competency for students in the 21st century, is difficult to get a meaningful improvement in a brief intervention duration. Instead, it could be developed over a lengthy period of time through consistent teaching and the progressive accumulation of knowledge (Halpern, 2001 ; Hu and Liu, 2015 ). Therefore, future empirical studies ought to take these restrictions into account throughout a longer period of critical thinking instruction.

With regard to group size, a group size of 2–3 persons has the highest effect size, and the comprehensive effect size decreases with increasing group size in general. This outcome is in line with some research findings; as an example, a group composed of two to four members is most appropriate for collaborative learning (Schellens and Valcke, 2006 ). However, the meta-analysis results also indicate that once the group size exceeds 7 people, small groups cannot produce better interaction and performance than large groups. This may be because the learning scaffolds of technique support, resource support, and teacher support improve the frequency and effectiveness of interaction among group members, and a collaborative group with more members may increase the diversity of views, which is helpful to cultivate critical thinking utilizing the approach of collaborative problem-solving.

With regard to the learning scaffold, the three different kinds of learning scaffolds can all enhance critical thinking. Among them, the teacher-supported learning scaffold has the largest overall effect size, demonstrating the interdependence of effective learning scaffolds and collaborative problem-solving. This outcome is in line with some research findings; as an example, a successful strategy is to encourage learners to collaborate, come up with solutions, and develop critical thinking skills by using learning scaffolds (Reiser, 2004 ; Xu et al., 2022 ); learning scaffolds can lower task complexity and unpleasant feelings while also enticing students to engage in learning activities (Wood et al., 2006 ); learning scaffolds are designed to assist students in using learning approaches more successfully to adapt the collaborative problem-solving process, and the teacher-supported learning scaffolds have the greatest influence on critical thinking in this process because they are more targeted, informative, and timely (Xu et al., 2022 ).

With respect to the measuring tool, despite the fact that standardized measurement tools (such as the WGCTA, CCTT, and CCTST) have been acknowledged as trustworthy and effective by worldwide experts, only 54.43% of the research included in this meta-analysis adopted them for assessment, and the results indicated no intergroup differences. These results suggest that not all teaching circumstances are appropriate for measuring critical thinking using standardized measurement tools. “The measuring tools for measuring thinking ability have limits in assessing learners in educational situations and should be adapted appropriately to accurately assess the changes in learners’ critical thinking.”, according to Simpson and Courtney ( 2002 , p. 91). As a result, in order to more fully and precisely gauge how learners’ critical thinking has evolved, we must properly modify standardized measuring tools based on collaborative problem-solving learning contexts.

With regard to the subject area, the comprehensive effect size of science departments (e.g., mathematics, science, medical science) is larger than that of language arts and social sciences. Some recent international education reforms have noted that critical thinking is a basic part of scientific literacy. Students with scientific literacy can prove the rationality of their judgment according to accurate evidence and reasonable standards when they face challenges or poorly structured problems (Kyndt et al., 2013 ), which makes critical thinking crucial for developing scientific understanding and applying this understanding to practical problem solving for problems related to science, technology, and society (Yore et al., 2007 ).

Suggestions for critical thinking teaching

Other than those stated in the discussion above, the following suggestions are offered for critical thinking instruction utilizing the approach of collaborative problem-solving.

First, teachers should put a special emphasis on the two core elements, which are collaboration and problem-solving, to design real problems based on collaborative situations. This meta-analysis provides evidence to support the view that collaborative problem-solving has a strong synergistic effect on promoting students’ critical thinking. Asking questions about real situations and allowing learners to take part in critical discussions on real problems during class instruction are key ways to teach critical thinking rather than simply reading speculative articles without practice (Mulnix, 2012 ). Furthermore, the improvement of students’ critical thinking is realized through cognitive conflict with other learners in the problem situation (Yang et al., 2008 ). Consequently, it is essential for teachers to put a special emphasis on the two core elements, which are collaboration and problem-solving, and design real problems and encourage students to discuss, negotiate, and argue based on collaborative problem-solving situations.

Second, teachers should design and implement mixed courses to cultivate learners’ critical thinking, utilizing the approach of collaborative problem-solving. Critical thinking can be taught through curriculum instruction (Kuncel, 2011 ; Leng and Lu, 2020 ), with the goal of cultivating learners’ critical thinking for flexible transfer and application in real problem-solving situations. This meta-analysis shows that mixed course teaching has a highly substantial impact on the cultivation and promotion of learners’ critical thinking. Therefore, teachers should design and implement mixed course teaching with real collaborative problem-solving situations in combination with the knowledge content of specific disciplines in conventional teaching, teach methods and strategies of critical thinking based on poorly structured problems to help students master critical thinking, and provide practical activities in which students can interact with each other to develop knowledge construction and critical thinking utilizing the approach of collaborative problem-solving.

Third, teachers should be more trained in critical thinking, particularly preservice teachers, and they also should be conscious of the ways in which teachers’ support for learning scaffolds can promote critical thinking. The learning scaffold supported by teachers had the greatest impact on learners’ critical thinking, in addition to being more directive, targeted, and timely (Wood et al., 2006 ). Critical thinking can only be effectively taught when teachers recognize the significance of critical thinking for students’ growth and use the proper approaches while designing instructional activities (Forawi, 2016 ). Therefore, with the intention of enabling teachers to create learning scaffolds to cultivate learners’ critical thinking utilizing the approach of collaborative problem solving, it is essential to concentrate on the teacher-supported learning scaffolds and enhance the instruction for teaching critical thinking to teachers, especially preservice teachers.

Implications and limitations

There are certain limitations in this meta-analysis, but future research can correct them. First, the search languages were restricted to English and Chinese, so it is possible that pertinent studies that were written in other languages were overlooked, resulting in an inadequate number of articles for review. Second, these data provided by the included studies are partially missing, such as whether teachers were trained in the theory and practice of critical thinking, the average age and gender of learners, and the differences in critical thinking among learners of various ages and genders. Third, as is typical for review articles, more studies were released while this meta-analysis was being done; therefore, it had a time limit. With the development of relevant research, future studies focusing on these issues are highly relevant and needed.

Conclusions

The subject of the magnitude of collaborative problem-solving’s impact on fostering students’ critical thinking, which received scant attention from other studies, was successfully addressed by this study. The question of the effectiveness of collaborative problem-solving in promoting students’ critical thinking was addressed in this study, which addressed a topic that had gotten little attention in earlier research. The following conclusions can be made:

Regarding the results obtained, collaborative problem solving is an effective teaching approach to foster learners’ critical thinking, with a significant overall effect size (ES = 0.82, z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]). With respect to the dimensions of critical thinking, collaborative problem-solving can significantly and effectively improve students’ attitudinal tendency, and the comprehensive effect is significant (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI [0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI [0.58, 0.82]).

As demonstrated by both the results and the discussion, there are varying degrees of beneficial effects on students’ critical thinking from all seven moderating factors, which were found across 36 studies. In this context, the teaching type (chi 2  = 7.20, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), and learning scaffold (chi 2  = 9.03, P  < 0.01) all have a positive impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. Since the learning stage (chi 2  = 3.15, P  = 0.21 > 0.05) and measuring tools (chi 2  = 0.08, P  = 0.78 > 0.05) did not demonstrate any significant intergroup differences, we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving.

Data availability

All data generated or analyzed during this study are included within the article and its supplementary information files, and the supplementary information files are available in the Dataverse repository: https://doi.org/10.7910/DVN/IPFJO6 .

Bensley DA, Spero RA (2014) Improving critical thinking skills and meta-cognitive monitoring through direct infusion. Think Skills Creat 12:55–68. https://doi.org/10.1016/j.tsc.2014.02.001

Article   Google Scholar  

Castle A (2009) Defining and assessing critical thinking skills for student radiographers. Radiography 15(1):70–76. https://doi.org/10.1016/j.radi.2007.10.007

Chen XD (2013) An empirical study on the influence of PBL teaching model on critical thinking ability of non-English majors. J PLA Foreign Lang College 36 (04):68–72

Google Scholar  

Cohen A (1992) Antecedents of organizational commitment across occupational groups: a meta-analysis. J Organ Behav. https://doi.org/10.1002/job.4030130602

Cooper H (2010) Research synthesis and meta-analysis: a step-by-step approach, 4th edn. Sage, London, England

Cindy HS (2004) Problem-based learning: what and how do students learn? Educ Psychol Rev 51(1):31–39

Duch BJ, Gron SD, Allen DE (2001) The power of problem-based learning: a practical “how to” for teaching undergraduate courses in any discipline. Stylus Educ Sci 2:190–198

Ennis RH (1989) Critical thinking and subject specificity: clarification and needed research. Educ Res 18(3):4–10. https://doi.org/10.3102/0013189x018003004

Facione PA (1990) Critical thinking: a statement of expert consensus for purposes of educational assessment and instruction. Research findings and recommendations. Eric document reproduction service. https://eric.ed.gov/?id=ed315423

Facione PA, Facione NC (1992) The California Critical Thinking Dispositions Inventory (CCTDI) and the CCTDI test manual. California Academic Press, Millbrae, CA

Forawi SA (2016) Standard-based science education and critical thinking. Think Skills Creat 20:52–62. https://doi.org/10.1016/j.tsc.2016.02.005

Halpern DF (2001) Assessing the effectiveness of critical thinking instruction. J Gen Educ 50(4):270–286. https://doi.org/10.2307/27797889

Hu WP, Liu J (2015) Cultivation of pupils’ thinking ability: a five-year follow-up study. Psychol Behav Res 13(05):648–654. https://doi.org/10.3969/j.issn.1672-0628.2015.05.010

Huber K (2016) Does college teach critical thinking? A meta-analysis. Rev Educ Res 86(2):431–468. https://doi.org/10.3102/0034654315605917

Kek MYCA, Huijser H (2011) The power of problem-based learning in developing critical thinking skills: preparing students for tomorrow’s digital futures in today’s classrooms. High Educ Res Dev 30(3):329–341. https://doi.org/10.1080/07294360.2010.501074

Kuncel NR (2011) Measurement and meaning of critical thinking (Research report for the NRC 21st Century Skills Workshop). National Research Council, Washington, DC

Kyndt E, Raes E, Lismont B, Timmers F, Cascallar E, Dochy F (2013) A meta-analysis of the effects of face-to-face cooperative learning. Do recent studies falsify or verify earlier findings? Educ Res Rev 10(2):133–149. https://doi.org/10.1016/j.edurev.2013.02.002

Leng J, Lu XX (2020) Is critical thinking really teachable?—A meta-analysis based on 79 experimental or quasi experimental studies. Open Educ Res 26(06):110–118. https://doi.org/10.13966/j.cnki.kfjyyj.2020.06.011

Liang YZ, Zhu K, Zhao CL (2017) An empirical study on the depth of interaction promoted by collaborative problem solving learning activities. J E-educ Res 38(10):87–92. https://doi.org/10.13811/j.cnki.eer.2017.10.014

Lipsey M, Wilson D (2001) Practical meta-analysis. International Educational and Professional, London, pp. 92–160

Liu Z, Wu W, Jiang Q (2020) A study on the influence of problem based learning on college students’ critical thinking-based on a meta-analysis of 31 studies. Explor High Educ 03:43–49

Morris SB (2008) Estimating effect sizes from pretest-posttest-control group designs. Organ Res Methods 11(2):364–386. https://doi.org/10.1177/1094428106291059

Article   ADS   Google Scholar  

Mulnix JW (2012) Thinking critically about critical thinking. Educ Philos Theory 44(5):464–479. https://doi.org/10.1111/j.1469-5812.2010.00673.x

Naber J, Wyatt TH (2014) The effect of reflective writing interventions on the critical thinking skills and dispositions of baccalaureate nursing students. Nurse Educ Today 34(1):67–72. https://doi.org/10.1016/j.nedt.2013.04.002

National Research Council (2012) Education for life and work: developing transferable knowledge and skills in the 21st century. The National Academies Press, Washington, DC

Niu L, Behar HLS, Garvan CW (2013) Do instructional interventions influence college students’ critical thinking skills? A meta-analysis. Educ Res Rev 9(12):114–128. https://doi.org/10.1016/j.edurev.2012.12.002

Peng ZM, Deng L (2017) Towards the core of education reform: cultivating critical thinking skills as the core of skills in the 21st century. Res Educ Dev 24:57–63. https://doi.org/10.14121/j.cnki.1008-3855.2017.24.011

Reiser BJ (2004) Scaffolding complex learning: the mechanisms of structuring and problematizing student work. J Learn Sci 13(3):273–304. https://doi.org/10.1207/s15327809jls1303_2

Ruggiero VR (2012) The art of thinking: a guide to critical and creative thought, 4th edn. Harper Collins College Publishers, New York

Schellens T, Valcke M (2006) Fostering knowledge construction in university students through asynchronous discussion groups. Comput Educ 46(4):349–370. https://doi.org/10.1016/j.compedu.2004.07.010

Sendag S, Odabasi HF (2009) Effects of an online problem based learning course on content knowledge acquisition and critical thinking skills. Comput Educ 53(1):132–141. https://doi.org/10.1016/j.compedu.2009.01.008

Sison R (2008) Investigating Pair Programming in a Software Engineering Course in an Asian Setting. 2008 15th Asia-Pacific Software Engineering Conference, pp. 325–331. https://doi.org/10.1109/APSEC.2008.61

Simpson E, Courtney M (2002) Critical thinking in nursing education: literature review. Mary Courtney 8(2):89–98

Stewart L, Tierney J, Burdett S (2006) Do systematic reviews based on individual patient data offer a means of circumventing biases associated with trial publications? Publication bias in meta-analysis. John Wiley and Sons Inc, New York, pp. 261–286

Tiwari A, Lai P, So M, Yuen K (2010) A comparison of the effects of problem-based learning and lecturing on the development of students’ critical thinking. Med Educ 40(6):547–554. https://doi.org/10.1111/j.1365-2929.2006.02481.x

Wood D, Bruner JS, Ross G (2006) The role of tutoring in problem solving. J Child Psychol Psychiatry 17(2):89–100. https://doi.org/10.1111/j.1469-7610.1976.tb00381.x

Wei T, Hong S (2022) The meaning and realization of teachable critical thinking. Educ Theory Practice 10:51–57

Xu EW, Wang W, Wang QX (2022) A meta-analysis of the effectiveness of programming teaching in promoting K-12 students’ computational thinking. Educ Inf Technol. https://doi.org/10.1007/s10639-022-11445-2

Yang YC, Newby T, Bill R (2008) Facilitating interactions through structured web-based bulletin boards: a quasi-experimental study on promoting learners’ critical thinking skills. Comput Educ 50(4):1572–1585. https://doi.org/10.1016/j.compedu.2007.04.006

Yore LD, Pimm D, Tuan HL (2007) The literacy component of mathematical and scientific literacy. Int J Sci Math Educ 5(4):559–589. https://doi.org/10.1007/s10763-007-9089-4

Zhang T, Zhang S, Gao QQ, Wang JH (2022) Research on the development of learners’ critical thinking in online peer review. Audio Visual Educ Res 6:53–60. https://doi.org/10.13811/j.cnki.eer.2022.06.08

Download references

Acknowledgements

This research was supported by the graduate scientific research and innovation project of Xinjiang Uygur Autonomous Region named “Research on in-depth learning of high school information technology courses for the cultivation of computing thinking” (No. XJ2022G190) and the independent innovation fund project for doctoral students of the College of Educational Science of Xinjiang Normal University named “Research on project-based teaching of high school information technology courses from the perspective of discipline core literacy” (No. XJNUJKYA2003).

Author information

Authors and affiliations.

College of Educational Science, Xinjiang Normal University, 830017, Urumqi, Xinjiang, China

Enwei Xu, Wei Wang & Qingxia Wang

You can also search for this author in PubMed   Google Scholar

Corresponding authors

Correspondence to Enwei Xu or Wei Wang .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

Additional information.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary tables, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Xu, E., Wang, W. & Wang, Q. The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature. Humanit Soc Sci Commun 10 , 16 (2023). https://doi.org/10.1057/s41599-023-01508-1

Download citation

Received : 07 August 2022

Accepted : 04 January 2023

Published : 11 January 2023

DOI : https://doi.org/10.1057/s41599-023-01508-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

The cognitive benefits of being multilingual

Multilingualism has widely recognized social and career benefits. But did you know you can also reap the huge cognitive benefits of being multilingual?

Recent studies estimate that over half the world’s population is multilingual to some extent, speaking more than just one language. When a person is multilingual, they reap the social benefits of being able to communicate with and blend into a whole new community and culture of people.

But there are other significant benefits to speaking more than one language such as career benefits and, the subject of this article, the cognitive benefits of being multilingual. In fact, these benefits can positively and profoundly impact your cognition until well into old age! So, read on to discover some of the many cognitive benefits of being multilingual!

[And for a complete toolkit for how to teach yourself a language, check out the best ways to learn a language on your own .]

8 Demonstrated cognitive benefits of being multilingual

1. A better, innate understanding of how language works

Because learning a second (or third, or fourth) language brings your attention to the mechanics of the two languages, (including how they differ), multilingual people tend to understand things like grammar, conjugations, and sentence structure better than monolinguals. These people can more quickly pick up on the structure of any language and clearly understand how it can be used.

Multilingual people also tend to be more effective communicators, more exact editors, and more compelling writers, because they better understand how languages function, including in their native language.

Read: ' Why you should practice SPEAKING a second language '

2. Less mental decline in old age

Many studies have shown that the more elderly people "exercise" their brains every day, the less cognitive decline they experience overall. And it turns out that jumping between languages—or acquiring a new one—is a particularly effective way to attain this benefit!

Read: ' Learning a new language when you’re older and how to do it! '

In fact, several studies have demonstrated that bilingualism can delay the onset of dementia and Alzheimer’s disease by an average of five years! Even better, bilingual patients who do develop Alzheimer’s tend to display less decay in cognitive abilities than monolingual patients.

3. More efficient and better developed executive control in the brain

When you are multilingual, you constantly switch between languages without thinking about it. And this is why multilingual people tend to have better developed executive control systems, the part of the brain that controls your ability to switch your attention between things and exercise working memory. A more developed executive control system allows multilingual people to better perform tasks that require high-level thought, multitasking , and sustained attention.

Psssst! Check out Brainscape's flashcards for any foreign language to learn twice as efficiently as any other study method!

4. Greater cognitive flexibility and problem-solving skills

Learning a new language requires the brain to express similar thoughts in different ways and because of this multilingual people develop greater cognitive flexibility . This translates into other areas as improved creativity and problem-solving, as well as the ability to perceive situations in different ways. Multilingual people tend to solve complex problems in more creative ways than their monolingual peers, no matter what kind of problem is being solved.

5. Improvements in learning abilities

As mentioned earlier, multilingual people have more developed executive functions. One important executive function is inhibition, the ability to discard irrelevant or unimportant stimuli and focus on the key stimuli. Inhibition is key to learning new information and skills, as it allows you to focus on what's important while reducing interference from what you already know , as well as similar concepts. Since multilingual people have better-developed inhibition, studies demonstrate that they can not only learn a third or fourth language quicker, but can develop any learned skill faster.

6. Changes in neurological processing

Brain imaging techniques, such as fMRIs, have shown that multilingual brains tend to activate the linguistic portion of their brains even when not engaged in linguistic tasks. This leads researchers to believe that the brain’s ability to connect skills tends to enhance cognitive function over time . Bilingual brains tend to show higher level of activation to auditory stimuli overall, which gives them an advantage in sensory processing. Even the actual structure of the brain is affected.

Studies show that multilingual people have a higher density of grey matter in their brains, and older bilingual people usually have better-maintained white matter, even late in life. The cognitive control required to manage multiple languages seems to broadly impact neurological function and structure, fine-tuning cognitive control mechanisms and sensory processes.

7. More rational decision-making skills

A study done at the University of Chicago demonstrated that bilinguals tend to make more rational decisions. As language contains nuance and subtle implications in its vocabulary that can subconsciously influence your judgment, thinking in your native language tends to be fraught with emotional biases. Interestingly, though, multilingual people tend to be less affected by such biases, especially in their second language. Bilinguals are able to draw from their understanding of a problem using both languages, which allows them to rely more on analytic processes than emotional linguistic cues.

Read: How brain science can help you learn a language faster

8. A more perceptive understanding of the world

Multilingual people tend to be better at observing their environment and spotting misleading information. Perhaps this is because of their enhanced inhibition skills that allow them to focus on relevant information and edit out the rest. Due to this, multilingual people have been shown to be keen observers of the world around them, as well as more skilled at identifying and correctly analyzing the sub-context of a situation and interpreting the social environment. This makes multilingual people highly perceptive , a skill that's also exercised by interacting with the unfamiliar social or cultural context of a second language.

Multilingualism is great for your brain

As you can see, the cognitive benefits of multilingualism can potentially outweigh the concerted effort of learning a new language. This is especially true when you find an effective and simple way to develop your linguistic skills.

[See also: Should you learn a language? Maybe not (and that's ok) ]

If you're seriously thinking about learning a new language, you should check out Brainscape's groundbreaking spaced repetition system for learning a language , which makes learning a new language as efficient as possible for learners of any level.

If you are still monolingual or are simply ready to tackle your next language, check out the many foreign language flashcards we have, and get started today!

Bartolotti, J., & Marian, V. (2012). Language learning and control in monolinguals and bilinguals. Cognitive Science , 36 (6), 1129-1147. https://dx.doi.org/10.1111%2Fj.1551-6709.2012.01243.x

Craik, F. I., Bialystok, E., & Freedman, M. (2010). Delaying the onset of Alzheimer disease: Bilingualism as a form of cognitive reserve. Neurology , 75 (19), 1726-1729. https://doi.org/10.1212/wnl.0b013e3181fc2a1c

Keysar, B., Hayakawa, S. L., & An, S. G. (2012). The foreign-language effect: Thinking in a foreign tongue reduces decision biases. Psychological Science , 23 (6), 661-668. https://doi.org/10.1177%2F0956797611432178

Flashcards for serious learners .

• Architecture and Design
• Asian and Pacific Studies
• Business and Economics
• Classical and Ancient Near Eastern Studies
• Computer Sciences
• Cultural Studies
• Engineering
• General Interest
• Geosciences
• Industrial Chemistry
• Islamic and Middle Eastern Studies
• Jewish Studies
• Library and Information Science, Book Studies
• Life Sciences
• Linguistics and Semiotics
• Literary Studies
• Materials Sciences
• Mathematics
• Social Sciences
• Sports and Recreation
• Theology and Religion
• Publish your article
• The role of authors
• Promoting your article
• Abstracting & indexing
• Publishing Ethics
• Why publish with De Gruyter
• How to publish with De Gruyter
• Our book series
• Our subject areas
• Your digital product at De Gruyter
• Contribute to our reference works
• Product information
• Tools & resources
• Product Information
• Promotional Materials
• Orders and Inquiries
• FAQ for Library Suppliers and Book Sellers
• Repository Policy
• Free access policy
• Open Access agreements
• Database portals
• For Authors
• Customer service
• People + Culture
• Journal Management
• How to join us
• Working at De Gruyter
• Mission & Vision
• De Gruyter Foundation
• De Gruyter Ebound
• Our Responsibility
• Partner publishers

Your purchase has been completed. Your documents are now available to view.

Problem Solving in a Foreign Language

• X / Twitter

Please login or register with De Gruyter to order this product.

• Language: English
• Publisher: De Gruyter Mouton
• Copyright year: 2010
• Audience: Scholars in the Areas of Content and Language Integrated Learning and Bilingual Education, Cognitive Linguists with an Interest in Educational Perspectives, Scholars Interested in the Relationship between (Foreign) Language and Thought.
• Front matter: 9
• Main content: 217
• Other: num. figs. and tables
• Keywords: Language Teaching ; CLICL ; Second Language Acquisition
• Published: April 23, 2010
• ISBN: 9783110224467
• Published: March 17, 2010
• ISBN: 9783110224450

• Not For Profit Charity
• Early Childhood Language Delays
• Language & Literacy Development
• Professionals
• Our Thought Leaders
• Our Global Reach
• It Takes Two To Talk
• More Than Words
• TalkAbility
• Target Word
• Learning Language and Loving It
• ABC and Beyond
• Teacher Talk Training Series
• Program Search
• Hanen 4 “I”s to Socialize
• SPARK Communication
• Workshop Schedule
• Academic Presentation
• Workshop Notification
• It Takes Two to Talk
• Make Play R.O.C.K.™ Booklet Series
• You Make The Difference
• Teacher Talk Workbooks
• It Takes Two to Talk Parent Workbook - member only
• Healthcare Professionals
• When You Are Concerned
• Parent Tips
• Language and Literacy Tips
• Fun Activities
• Autism Corner
• Literacy at Home
• Literacy in the Classroom
• Technology Corner
• Hanen Newsletter
• Speech-Language Pathologists

Critical Thinking: Building a Key Foundation for Language and Literacy Success

Did you know that school curriculums around the world are increasing their focus on critical thinking skills? Experts on early childhood development agree that the basic skills of reading, writing and math are no longer enough – children also need to learn to think critically if they’re going to be successful in today’s complex world.

What Is Critical Thinking?

Critical thinking happens when children draw on their existing knowledge and experience, as well as on their problem-solving skills, to do things like:

• Compare and contrast
• Explain why things happen
• Evaluate ideas and form opinions
• Understand the perspectives of others
• Predict what will happen in the future
• Think of creative solutions

Why is critical thinking so important?

Critical thinking is a fundamental skills for both language and literacy success.

• Language − Language and critical thinking grow together and nurture each other’s development. As children engage in critical thinking, their language skills expand because they’re encouraged to develop and use more complex language with words like “because”, phrases with “if” and “then” and different verb tenses. Conversely, as children’s language development progresses, their ability to think critically grows as well.  
• Literacy − To truly understand the meaning of a book, children must be able to do more than recognize and sound out letters and words. They must also “read between the lines” to figure things out that are not actually stated in the book. To do this, they must use critical thinking skills like problem-solving, predicting and explaining. Encouraging this kind of thinking early in a child’s life prepares her for understanding the books she’ll read on her own later on.  

When and How Does Critical Thinking Develop?

Research shows that children begin to think critically at a very young age. These skills develop during the natural, back and forth conversations children have with the important adults in their lives.

As soon as children are able to speak in sentences, they’re ready for you − the parent, caregiver or educator − to nurture the critical thinking skills that will prepare them for success in school. Whether you’re reading a book or taking a walk in the park, any time is a good time to build critical thinking.

Tips for Building Critical Thinking – It’s all about the E’s and P’s!

Use the arrows to scroll through the E’s and P’s and get a fun tip from the 2016 Calendar for promoting each one!

Helpful articles on critical thinking

More Than ABCs: Building the Critical Thinking Skills Your Child Needs for Literacy Success Get more fun tips on building children’s E’s and P’s during book reading. Read article

Teaching Children to Think: Meeting the Demands of the 21st Century Learn more about the evolving role of early childhood educators and what governments around the world are doing to increase the focus on critical thinking. Read article

Share This Page  

• Contact Us |
• Copyright |
• Privacy Policy |
• Accessibility Policy |
• Terms of Use

Collaborative Learning: An Effective Approach to Promote Language Development

• International Journal of Social Sciences and Educational Studies 7(2):5

• Tishk International University

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

• Suksan Suppasetseree
• John Robin H Raton

• Carlo G. Soberano

• Paulino Faque Mois&eacute;s

• Musdalifah Musdalifah
• Nazilul Umami
• Mega Selviana Dewi

• M Vijayakumar
• Asst Professor

• AE GOTTFRIED

• ROLAND CASE
• Jerrold R. Coombs
• Leroi B. Daniels

• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

• U.S. Department of Health & Human Services

• Virtual Tour
• Staff Directory
• En Español

You are here

News releases.

News Release

Tuesday, April 3, 2012

Bilinguals switch tasks faster than monolinguals, NIH funded study shows

Bilinguals slower to build vocabulary but better at multitasking than monolinguals.

Children who grow up learning to speak two languages are better at switching between tasks than are children who learn to speak only one language, according to a study funded in part by the National Institutes of Health. However, the study also found that bilinguals are slower to acquire vocabulary than are monolinguals, because bilinguals must divide their time between two languages while monolinguals focus on only one.

In the study, bilingual and monolingual children were asked to press a computer key as they viewed a series of images — either of animals or of depictions of colors. When the responses were limited to either of the two categories, the children responded at the same speed. But when the children were asked to switch, from animals to a color, and press a different button for the new category, bilinguals were faster at making the change than were the monolinguals.

Researchers often use this switching task to gauge a set of mental processes known as executive functioning — generally defined as the ability to pay attention, plan, organize, and strategize. The task engages three mental processes: the ability to keep a rule or principle in mind (working memory), inhibition (the ability to refrain from carrying out one rule), and shifting (the ability to make the change and act on another rule).

“In simplest terms, the switching task is an indicator of the ability to multi-task,” said Peggy McCardle, Ph.D., chief of the Child Development and Behavior Branch at the NIH's Eunice Kennedy Shriver National Institute of Child Health and Human Development, which provided funding for the study. “Bilinguals have two sets of language rules in mind, and their brains apparently are wired to toggle back and forth between them depending on the circumstances.”

(The NIH Radio interview with Dr. McCardle on the study, “Bilingual kids may have a cognitive advantage,” is available at http://www.nih.gov/news/radio/healthmatters/index.htm .)

The study, published online in Child Development, was conducted by Raluca Barac and Ellen Bialystok at York University in Toronto, Canada. The researchers tested a total of 104 children. They compared test results of English-speaking monolinguals to those of Chinese-English bilinguals, French-English bilinguals, and Spanish-English bilinguals.

The NICHD's Child Development and Behavior Branch sponsors research on reading and reading disabilities, with the goal of identifying those factors that help English speaking children, bilinguals, and children who learn English as a second language become proficient in reading and writing in English. In 2009, 21 percent of U.S. children spoke a language other than English at home.

Dr. McCardle noted that, in the United States, studies of bilingualism are often complicated by the cultural and economic differences between the majority, English-speaking monolinguals and bilingual, or second language-learning immigrant groups, who often also lack economic resources. For this reason, researchers don't know if the difference they may see in test scores between groups is due to bilingualism itself, or to the economic differences between recent immigrants and those whose families have been in the country longer. Canada has a large French speaking population, with income levels comparable to that of the English speaking population. For this reason, the researchers of the current study could rule out economic differences as a potential contributor to the study results, at least when comparing English-speaking monolinguals to the French-English bilinguals.

In the study, the researchers tested verbal and nonverbal cognitive abilities of 104 6-year-old children from the Toronto area. All were public school students, and from similar economic and social backgrounds. In addition to English monolinguals and English-French bilinguals, the study also included English-Spanish and English-Chinese bilinguals. Along with the switching task, the test battery consisted of three English language tests of verbal ability. The verbal tests measured vocabulary and children's understanding of such linguistic tasks as forming plurals, conjugating verbs, grammatical structure, and English pronunciation rules.

For the switching task, accuracy scores were similar for all the groups, with the groups choosing the correct option approximately the same proportion of times. However, all of the bilinguals could switch from one task to another more rapidly than could the monolinguals.

Earlier studies also had shown that bilinguals could perform the switching task more rapidly than could monolinguals. However, these studies tended to include only one group of bilinguals, and so couldn't rule out whether it was bilingualism itself that conferred the increased ability to make the switch, or whether it was some aspect of the language the bilinguals spoke. The fact that all three groups of bilinguals in the current study could make the switch faster than could the monolinguals indicates that it's the bilingualism itself that confers the more rapid switching ability.

In tests of verbal ability, the English language monolinguals scored highest on a measure of English receptive vocabulary — the body of words a person recognizes well enough to comprehend when hearing them or listening to them. Because they have to learn only English, the monolinguals were able to acquire a larger vocabulary than could any of the bilingual groups, who need to divide their time between acquiring two vocabularies. However, English-Spanish bilinguals scored nearly as well as English monolinguals on the measure of receptive vocabulary.

The monolinguals also scored higher than did the other groups on a test measuring knowledge of English grammar and word meaning. The English-Spanish bilinguals scored higher on the grammatical test than did the Chinese-English bilinguals, who scored higher than did English-French bilinguals. The Spanish bilinguals attended English language schools, which may have provided an advantage in tests of English grammar in comparison to the French bilinguals, who attended French language schools.

The Spanish bilinguals scored highest on the test of metalinguistic awareness — an understanding of the structure of words as a basis for forming plurals, possessive, verb tenses, and compound words. The monolinguals and the Chinese and French bilinguals received comparable scores on the metalinguistic test. The researchers concluded that the similarity of Spanish to English, and the fact that the Spanish bilinguals attended English speaking schools likely combined to give the Spanish bilinguals an advantage over all the other groups on the metalinguistic task, and an advantage over all of the bilingual groups in the other language tasks.

About the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD): The NICHD sponsors research on development, before and after birth; maternal, child, and family health; reproductive biology and population issues; and medical rehabilitation. For more information, visit the Institute's website at http://www.nichd.nih.gov .

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

NIH…Turning Discovery Into Health ®

Connect with Us

• More Social Media from NIH

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

44 Language and Problem Solving

Learning outcomes.

By the end of this section, you will be able to:

• Define language and demonstrate familiarity with the components of language
• Understand how the use of language develops
• Explain the relationship between language and thinking
• Describe problem solving strategies
• Define algorithm and heuristic
• Explain some common roadblocks to effective problem solving

Language  is a communication system that involves using words and systematic rules to organize those words to transmit information from one individual to another. While language is a form of communication, not all communication is language. Many species communicate with one another through their postures, movements, odors, or vocalizations. This communication is crucial for species that need to interact and develop social relationships with their conspecifics. However, many people have asserted that it is language that makes humans unique among all of the animal species (Corballis & Suddendorf, 2007; Tomasello & Rakoczy, 2003). This section will focus on what distinguishes language as a special form of communication, how the use of language develops, and how language affects the way we think.

COMPONENTS OF LANGUAGE

Language, be it spoken, signed, or written, has specific components: a lexicon and grammar.  Lexicon  refers to the words of a given language. Thus, lexicon is a language’s vocabulary.  Grammar  refers to the set of rules that are used to convey meaning through the use of the lexicon (Fernández & Cairns, 2011). For instance, English grammar dictates that most verbs receive an “-ed” at the end to indicate past tense.

Words are formed by combining the various phonemes that make up the language. A  phoneme  (e.g., the sounds “ah” vs. “eh”) is a basic sound unit of a given language, and different languages have different sets of phonemes. Phonemes are combined to form  morphemes , which are the smallest units of language that convey some type of meaning (e.g., “I” is both a phoneme and a morpheme). We use semantics and syntax to construct language. Semantics and syntax are part of a language’s grammar.  Semantics  refers to the process by which we derive meaning from morphemes and words.  Syntax  refers to the way words are organized into sentences (Chomsky, 1965; Fernández & Cairns, 2011).

We apply the rules of grammar to organize the lexicon in novel and creative ways, which allow us to communicate information about both concrete and abstract concepts. We can talk about our immediate and observable surroundings as well as the surface of unseen planets. We can share our innermost thoughts, our plans for the future, and debate the value of a college education. We can provide detailed instructions for cooking a meal, fixing a car, or building a fire. The flexibility that language provides to relay vastly different types of information is a property that makes language so distinct as a mode of communication among humans.

LANGUAGE DEVELOPMENT

Given the remarkable complexity of a language, one might expect that mastering a language would be an especially arduous task; indeed, for those of us trying to learn a second language as adults, this might seem to be true. However, young children master language very quickly with relative ease. B. F.  Skinner  (1957) proposed that language is learned through reinforcement. Noam  Chomsky  (1965) criticized this behaviorist approach, asserting instead that the mechanisms underlying language acquisition are biologically determined. The use of language develops in the absence of formal instruction and appears to follow a very similar pattern in children from vastly different cultures and backgrounds. It would seem, therefore, that we are born with a biological predisposition to acquire a language (Chomsky, 1965; Fernández & Cairns, 2011). Moreover, it appears that there is a critical period for language acquisition, such that this proficiency at acquiring language is maximal early in life; generally, as people age, the ease with which they acquire and master new languages diminishes (Johnson & Newport, 1989; Lenneberg, 1967; Singleton, 1995).

Children begin to learn about language from a very early age ( Table ). In fact, it appears that this is occurring even before we are born. Newborns show preference for their mother’s voice and appear to be able to discriminate between the language spoken by their mother and other languages. Babies are also attuned to the languages being used around them and show preferences for videos of faces that are moving in synchrony with the audio of spoken language versus videos that do not synchronize with the audio (Blossom & Morgan, 2006; Pickens, 1994; Spelke & Cortelyou, 1981).

In the fall of 1970, a social worker in the Los Angeles area found a 13-year-old girl who was being raised in extremely neglectful and abusive conditions. The girl, who came to be known as Genie, had lived most of her life tied to a potty chair or confined to a crib in a small room that was kept closed with the curtains drawn. For a little over a decade, Genie had virtually no social interaction and no access to the outside world. As a result of these conditions, Genie was unable to stand up, chew solid food, or speak (Fromkin, Krashen, Curtiss, Rigler, & Rigler, 1974; Rymer, 1993). The police took Genie into protective custody.

Genie’s abilities improved dramatically following her removal from her abusive environment, and early on, it appeared she was acquiring language—much later than would be predicted by critical period hypotheses that had been posited at the time (Fromkin et al., 1974). Genie managed to amass an impressive vocabulary in a relatively short amount of time. However, she never developed a mastery of the grammatical aspects of language (Curtiss, 1981). Perhaps being deprived of the opportunity to learn language during a critical period impeded Genie’s ability to fully acquire and use language.

You may recall that each language has its own set of phonemes that are used to generate morphemes, words, and so on. Babies can discriminate among the sounds that make up a language (for example, they can tell the difference between the “s” in vision and the “ss” in fission); early on, they can differentiate between the sounds of all human languages, even those that do not occur in the languages that are used in their environments. However, by the time that they are about 1 year old, they can only discriminate among those phonemes that are used in the language or languages in their environments (Jensen, 2011; Werker & Lalonde, 1988; Werker & Tees, 1984).

Visit this  website  to learn more about how babies lose the ability to discriminate among all possible human phonemes as they age.

After the first few months of life, babies enter what is known as the babbling stage, during which time they tend to produce single syllables that are repeated over and over. As time passes, more variations appear in the syllables that they produce. During this time, it is unlikely that the babies are trying to communicate; they are just as likely to babble when they are alone as when they are with their caregivers (Fernández & Cairns, 2011). Interestingly, babies who are raised in environments in which sign language is used will also begin to show babbling in the gestures of their hands during this stage (Petitto, Holowka, Sergio, Levy, & Ostry, 2004).

Generally, a child’s first word is uttered sometime between the ages of 1 year to 18 months, and for the next few months, the child will remain in the “one word” stage of language development. During this time, children know a number of words, but they only produce one-word utterances. The child’s early vocabulary is limited to familiar objects or events, often nouns. Although children in this stage only make one-word utterances, these words often carry larger meaning (Fernández & Cairns, 2011). So, for example, a child saying “cookie” could be identifying a cookie or asking for a cookie.

As a child’s lexicon grows, she begins to utter simple sentences and to acquire new vocabulary at a very rapid pace. In addition, children begin to demonstrate a clear understanding of the specific rules that apply to their language(s). Even the mistakes that children sometimes make provide evidence of just how much they understand about those rules. This is sometimes seen in the form of  overgeneralization . In this context, overgeneralization refers to an extension of a language rule to an exception to the rule. For example, in English, it is usually the case that an “s” is added to the end of a word to indicate plurality. For example, we speak of one dog versus two dogs. Young children will overgeneralize this rule to cases that are exceptions to the “add an s to the end of the word” rule and say things like “those two gooses” or “three mouses.” Clearly, the rules of the language are understood, even if the exceptions to the rules are still being learned (Moskowitz, 1978).

LANGUAGE AND THOUGHT

When we speak one language, we agree that words are representations of ideas, people, places, and events. The given language that children learn is connected to their culture and surroundings. But can words themselves shape the way we think about things? Psychologists have long investigated the question of whether language shapes thoughts and actions, or whether our thoughts and beliefs shape our language. Two researchers, Edward Sapir and Benjamin Lee Whorf, began this investigation in the 1940s. They wanted to understand how the language habits of a community encourage members of that community to interpret language in a particular manner (Sapir, 1941/1964). Sapir and Whorf proposed that language determines thought, suggesting, for example, that a person whose community language did not have past-tense verbs would be challenged to think about the past (Whorf, 1956). Researchers have since identified this view as too absolute, pointing out a lack of empiricism behind what Sapir and Whorf proposed (Abler, 2013; Boroditsky, 2011; van Troyer, 1994). Today, psychologists continue to study and debate the relationship between language and thought.

Think about what you know of other languages; perhaps you even speak multiple languages. Imagine for a moment that your closest friend fluently speaks more than one language. Do you think that friend thinks differently, depending on which language is being spoken? You may know a few words that are not translatable from their original language into English. For example, the Portuguese word  saudade  originated during the 15th century, when Portuguese sailors left home to explore the seas and travel to Africa or Asia. Those left behind described the emptiness and fondness they felt as  saudade  ( Figure ) .  The word came to express many meanings, including loss, nostalgia, yearning, warm memories, and hope. There is no single word in English that includes all of those emotions in a single description. Do words such as  saudade  indicate that different languages produce different patterns of thought in people? What do you think??

Language may indeed influence the way that we think, an idea known as linguistic determinism. One recent demonstration of this phenomenon involved differences in the way that English and Mandarin Chinese speakers talk and think about time. English speakers tend to talk about time using terms that describe changes along a horizontal dimension, for example, saying something like “I’m running behind schedule” or “Don’t get ahead of yourself.” While Mandarin Chinese speakers also describe time in horizontal terms, it is not uncommon to also use terms associated with a vertical arrangement. For example, the past might be described as being “up” and the future as being “down.” It turns out that these differences in language translate into differences in performance on cognitive tests designed to measure how quickly an individual can recognize temporal relationships. Specifically, when given a series of tasks with vertical priming, Mandarin Chinese speakers were faster at recognizing temporal relationships between months. Indeed, Boroditsky (2001) sees these results as suggesting that “habits in language encourage habits in thought” (p. 12).

One group of researchers who wanted to investigate how language influences thought compared how English speakers and the Dani people of Papua New Guinea think and speak about color. The Dani have two words for color: one word for  light  and one word for  dark . In contrast, the English language has 11 color words. Researchers hypothesized that the number of color terms could limit the ways that the Dani people conceptualized color. However, the Dani were able to distinguish colors with the same ability as English speakers, despite having fewer words at their disposal (Berlin & Kay, 1969). A recent review of research aimed at determining how language might affect something like color perception suggests that language can influence perceptual phenomena, especially in the left hemisphere of the brain. You may recall from earlier chapters that the left hemisphere is associated with language for most people. However, the right (less linguistic hemisphere) of the brain is less affected by linguistic influences on perception (Regier & Kay, 2009)

Language is a communication system that has both a lexicon and a system of grammar. Language acquisition occurs naturally and effortlessly during the early stages of life, and this acquisition occurs in a predictable sequence for individuals around the world. Language has a strong influence on thought, and the concept of how language may influence cognition remains an area of study and debate in psychology.

Review Questions

________ provides general principles for organizing words into meaningful sentences.

• Linguistic determinism

________ are the smallest unit of language that carry meaning.

The meaning of words and phrases is determined by applying the rules of ________.

• overgeneralization

________ is (are) the basic sound units of a spoken language.

Critical Thinking Questions

How do words not only represent our thoughts but also represent our values?

How could grammatical errors actually be indicative of language acquisition in children?

Personal Application Question

Can you think of examples of how language affects cognition?

Problem Solving

People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

PROBLEM-SOLVING STRATEGIES

When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

A  problem-solving strategy  is a plan of action used to find a solution. Different strategies have different action plans associated with them ( Table ). For example, a well-known strategy is  trial and error . The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Another type of strategy is an algorithm. An  algorithm  is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a  heuristic  is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backwards  is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below ( Figure ) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

Here is another popular type of puzzle ( Figure ) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Take a look at the “Puzzling Scales” logic puzzle below ( Figure ). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

PITFALLS TO PROBLEM SOLVING

Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A  mental set  is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness  is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the  Apollo 13  mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

Check out this  Apollo 13  scene  where the group of NASA engineers are given the task of overcoming functional fixedness.

Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An  anchoring bias  occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The  confirmation bias  is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis.  Hindsight bias  leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did.  Representative bias  describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the  availability heuristic  is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision .  Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in  Table .

Please visit this  site  to see a clever music video that a high school teacher made to explain these and other cognitive biases to his AP psychology students.

Were you able to determine how many marbles are needed to balance the scales in  Figure ? You need nine. Were you able to solve the problems in  Figure  and  Figure ? Here are the answers ( Figure ).

Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.

A specific formula for solving a problem is called ________.

• an algorithm
• a heuristic
• a mental set
• trial and error

A mental shortcut in the form of a general problem-solving framework is called ________.

Which type of bias involves becoming fixated on a single trait of a problem?

• anchoring bias
• confirmation bias
• representative bias
• availability bias

Which type of bias involves relying on a false stereotype to make a decision?

What is functional fixedness and how can overcoming it help you solve problems?

How does an algorithm save you time and energy when solving a problem?

Which type of bias do you recognize in your own decision making processes? How has this bias affected how you’ve made decisions in the past and how can you use your awareness of it to improve your decisions making skills in the future?

[glossary-page] [glossary-term]algorithm:[/glossary-term] [glossary-definition]problem-solving strategy characterized by a specific set of instructions[/glossary-definition]

[glossary-term]anchoring bias:[/glossary-term] [glossary-definition]faulty heuristic in which you fixate on a single aspect of a problem to find a solution[/glossary-definition]

[glossary-term]availability heuristic:[/glossary-term] [glossary-definition]faulty heuristic in which you make a decision based on information readily available to you[/glossary-definition]

[glossary-term]confirmation bias:[/glossary-term] [glossary-definition]faulty heuristic in which you focus on information that confirms your beliefs[/glossary-definition]

[glossary-term]functional fixedness:[/glossary-term] [glossary-definition]inability to see an object as useful for any other use other than the one for which it was intended[/glossary-definition]

[glossary-term]grammar:[/glossary-term] [glossary-definition]set of rules that are used to convey meaning through the use of a lexicon[/glossary-definition]

[glossary-term]heuristic:[/glossary-term] [glossary-definition]mental shortcut that saves time when solving a problem[/glossary-definition]

[glossary-term]hindsight bias:[/glossary-term] [glossary-definition]belief that the event just experienced was predictable, even though it really wasn’t[/glossary-definition]

[glossary-term]language:[/glossary-term] [glossary-definition]communication system that involves using words to transmit information from one individual to another[/glossary-definition]

[glossary-term]lexicon:[/glossary-term] [glossary-definition]the words of a given language[/glossary-definition]

[glossary-term]mental set:[/glossary-term] [glossary-definition]continually using an old solution to a problem without results[/glossary-definition]

[glossary-term]morpheme:[/glossary-term] [glossary-definition]smallest unit of language that conveys some type of meaning[/glossary-definition]

[glossary-term]overgeneralization:[/glossary-term] [glossary-definition]extension of a rule that exists in a given language to an exception to the rule[/glossary-definition]

[glossary-term]phoneme:[/glossary-term] [glossary-definition]basic sound unit of a given language[/glossary-definition]

[glossary-term]problem-solving strategy:[/glossary-term] [glossary-definition]method for solving problems[/glossary-definition]

[glossary-term]representative bias:[/glossary-term] [glossary-definition]faulty heuristic in which you stereotype someone or something without a valid basis for your judgment[/glossary-definition]

[glossary-term]semantics:[/glossary-term] [glossary-definition]process by which we derive meaning from morphemes and words[/glossary-definition]

[glossary-term]syntax:[/glossary-term] [glossary-definition]manner by which words are organized into sentences[/glossary-definition]

[glossary-term]trial and error:[/glossary-term] [glossary-definition]problem-solving strategy in which multiple solutions are attempted until the correct one is found[/glossary-definition]

[glossary-term]working backwards:[/glossary-term] [glossary-definition]heuristic in which you begin to solve a problem by focusing on the end result[/glossary-definition] [/glossary-page]

General Psychology Copyright © by Lumen Learning is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

Share This Book

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

7 Thinking, Language, and Problem Solving

What is the best way to solve a problem? How does a person who has never seen or touched snow in real life develop an understanding of the concept of snow? How do young children acquire the ability to learn language with no formal instruction? Psychologists who study thinking explore questions like these and are called cognitive psychologists.

In other chapters, we discussed the cognitive processes of perception, learning, and memory. In this chapter, we will focus on high-level cognitive processes. As a part of this discussion, we will consider thinking and briefly explore the development and use of language. We will also discuss problem solving and creativity. After finishing this chapter, you will have a greater appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.

Table of Contents

7.1 What is Cognition? 7.2 Language 7.3 Problem Solving

7.1 What is Cognition?

Learning Objectives

By the end of this section, you will be able to:

• Describe cognition
• Distinguish concepts and prototypes
• Explain the difference between natural and artificial concepts
• Describe how schemata are organized and constructed

Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet, you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one facet of the complex processes involved in cognition . Simply put,  cognition  is thinking, and it encompasses the processes associated with perception, knowledge, problem solving, judgment, language, and memory. Scientists who study cognition are searching for ways to understand how we integrate, organize, and utilize our conscious cognitive experiences without being aware of all of the unconscious work that our brains are doing (for example, Kahneman, 2011).

Upon waking each morning, you begin thinking—contemplating the tasks that you must complete that day. In what order should you run your errands? Should you go to the bank, the cleaners, or the grocery store first? Can you get these things done before you head to class or will they need to wait until school is done? These thoughts are one example of cognition at work. Exceptionally complex, cognition is an essential feature of human consciousness, yet not all aspects of cognition are consciously experienced.

Cognitive psychology  is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem solving, in addition to other cognitive processes. Cognitive psychologists strive to determine and measure different types of intelligence, why some people are better at problem solving than others, and how emotional intelligence affects success in the workplace, among countless other topics. They also sometimes focus on how we organize thoughts and information gathered from our environments into meaningful categories of thought, which will be discussed later.

Concepts and Prototypes

The human nervous system is capable of handling endless streams of information. The senses serve as the interface between the mind and the external environment, receiving stimuli and translating it into nervous impulses that are transmitted to the brain. The brain then processes this information and uses the relevant pieces to create thoughts, which can then be expressed through language or stored in memory for future use. To make this process more complex, the brain does not gather information from external environments only. When thoughts are formed, the mind synthesizes information from emotions and memories ( Figure 7.2 ). Emotion and memory are powerful influences on both our thoughts and behaviors.

Concepts are informed by our semantic memory (you will learn more about semantic memory in a later chapter) and are present in every aspect of our lives; however, one of the easiest places to notice concepts is inside a classroom, where they are discussed explicitly. When you study United States history, for example, you learn about more than just individual events that have happened in America’s past. You absorb a large quantity of information by listening to and participating in discussions, examining maps, and reading first-hand accounts of people’s lives. Your brain analyzes these details and develops an overall understanding of American history. In the process, your brain gathers details that inform and refine your understanding of related concepts like democracy, power, and freedom.

Concepts can be complex and abstract, like justice, or more concrete, like types of birds. Some concepts, like tolerance, are agreed upon by many people, because they have been used in various ways over many years. Other concepts, like the characteristics of your ideal friend or your family’s birthday traditions, are personal and individualized. In this way, concepts touch every aspect of our lives, from our many daily routines to the guiding principles behind the way governments function.

Another technique used by your brain to organize information is the identification of prototypes for the concepts you have developed. A  prototype  is the best example or representation of a concept. For example, what comes to your mind when you think of a dog? Most likely your early experiences with dogs will shape what you imagine. If your first pet was a Golden Retriever, there is a good chance that this would be your prototype for the category of dogs.

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories, natural and artificial. Natural concepts  are created “naturally” through your experiences and can be developed from either direct or indirect experiences. For example, if you live in Essex Junction, Vermont, you have probably had a lot of direct experience with snow. You’ve watched it fall from the sky, you’ve seen lightly falling snow that barely covers the windshield of your car, and you’ve shoveled out 18 inches of fluffy white snow as you’ve thought, “This is perfect for skiing.” You’ve thrown snowballs at your best friend and gone sledding down the steepest hill in town. In short, you know snow. You know what it looks like, smells like, tastes like, and feels like. If, however, you’ve lived your whole life on the island of Saint Vincent in the Caribbean, you may never have actually seen snow, much less tasted, smelled, or touched it. You know snow from the indirect experience of seeing pictures of falling snow—or from watching films that feature snow as part of the setting. Either way, snow is a natural concept because you can construct an understanding of it through direct observations, experiences with snow, or indirect knowledge (such as from films or books) ( Figure 7.3 ).

An  artificial concept , on the other hand, is a concept that is defined by a specific set of characteristics. Various properties of geometric shapes, like squares and triangles, serve as useful examples of artificial concepts. A triangle always has three angles and three sides. A square always has four equal sides and four right angles. Mathematical formulas, like the equation for area (length × width) are artificial concepts defined by specific sets of characteristics that are always the same. Artificial concepts can enhance the understanding of a topic by building on one another. For example, before learning the concept of “area of a square” (and the formula to find it), you must understand what a square is. Once the concept of “area of a square” is understood, an understanding of area for other geometric shapes can be built upon the original understanding of area. The use of artificial concepts to define an idea is crucial to communicating with others and engaging in complex thought. According to Goldstone and Kersten (2003), concepts act as building blocks and can be connected in countless combinations to create complex thoughts.

A  schema (plural: schemata)  is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.

There are several types of schemata. A  role schema  makes assumptions about how individuals in certain roles will behave (Callero, 1994). For example, imagine you meet someone who introduces himself as a firefighter. When this happens, your brain automatically activates the “firefighter schema” and begins making assumptions that this person is brave, selfless, and community-oriented. Despite not knowing this person, already you have unknowingly made judgments about him. Schemata also help you fill in gaps in the information you receive from the world around you. While schemata allow for more efficient information processing, there can be problems with schemata, regardless of whether they are accurate: Perhaps this particular firefighter is not brave, he just works as a firefighter to pay the bills while studying to become a children’s librarian.

An  event schema , also known as a  cognitive script , is a set of behaviors that can feel like a routine. Think about what you do when you walk into an elevator ( Figure 7.4 ). First, the doors open and you wait to let exiting passengers leave the elevator car. Then, you step into the elevator and turn around to face the doors, looking for the correct button to push. You never face the back of the elevator, do you? And when you’re riding in a crowded elevator and you can’t face the front, it feels uncomfortable, doesn’t it? Interestingly, event schemata can vary widely among different cultures and countries. For example, while it is quite common for people to greet one another with a handshake in the United States, in Tibet, you greet someone by sticking your tongue out at them, and in Belize, you bump fists (Cairns Regional Council, n.d.)

Because event schemata are automatic, they can be difficult to change. Imagine that you are driving home from work or school. This event schema involves getting in the car, shutting the door, and buckling your seatbelt before putting the key in the ignition. You might perform this script two or three times each day. As you drive home, you hear your phone’s ring tone. Typically, the event schema that occurs when you hear your phone ringing involves locating the phone and answering it or responding to your latest text message. So without thinking, you reach for your phone, which could be in your pocket, in your bag, or on the passenger seat of the car. This powerful event schema is informed by your pattern of behavior and the pleasurable stimulation that a phone call or text message gives your brain. Because it is a schema, it is extremely challenging for us to stop reaching for the phone, even though we know that we endanger our own lives and the lives of others while we do it (Neyfakh, 2013) ( Figure 7.5 ).

Remember the elevator? It feels almost impossible to walk in and  not  face the door. Our powerful event schema dictates our behavior in the elevator, and it is no different with our phones. Current research suggests that it is the habit, or event schema, of checking our phones in many different situations that makes refraining from checking them while driving especially difficult (Bayer & Campbell, 2012). Because texting and driving has become a dangerous epidemic in recent years, psychologists are looking at ways to help people interrupt the “phone schema” while driving. Event schemata like these are the reason why many habits are difficult to break once they have been acquired. As we continue to examine thinking, keep in mind how powerful the forces of concepts and schemata are to our understanding of the world.

7.2 LAnguage

• Define language and demonstrate familiarity with the components of language
• Understand the development of language
• Explain the relationship between language and thinking

Language  is a communication system that involves using words and systematic rules to organize those words to transmit information from one individual to another. While language is a form of communication, not all communication is language. Many species communicate with one another through their postures, movements, odors, or vocalizations. This communication is crucial for species that need to interact and develop social relationships with their conspecifics. However, many people have asserted that it is language that makes humans unique among all of the animal species (Corballis & Suddendorf, 2007; Tomasello & Rakoczy, 2003). This section will focus on what distinguishes language as a special form of communication, how the use of language develops, and how language affects the way we think.

Components of Language

Language, be it spoken, signed, or written, has specific components: a lexicon and lexicon grammar .  Lexicon  refers to the words of a given language. Thus, lexicon is a language’s vocabulary.  Grammar  refers to the set of rules that are used to convey meaning through the use of the lexicon (Fernández & Cairns, 2011). For instance, English grammar dictates that most verbs receive an “-ed” at the end to indicate past tense.

Words are formed by combining the various phonemes that make up the language. A  phoneme  (e.g., the sounds “ah” vs. “eh”) is a basic sound unit of a given language, and different languages have different sets of phonemes. For example, the phoneme English speakers associate with the letter ‘L’ is not used in the Japanese language. Similarly, many Southern African languages use phonemes, sometimes referred to as ‘click consonants’ that are not used in English.

Phonemes are combined to form  morphemes , which are the smallest units of language that convey some type of meaning. Some words are morphemes, but not all morphemes are words.  For example, “-ed” is a morpheme used to convey the past-tense in English, but it is not a word. The word “review” contains two morphemes: re- (meaning to do something again) and view (to see). Finally, some words like “I” and “a” are both a phonemes and morphemes.

We use semantics and syntax to construct language. Semantics and syntax are part of a language’s grammar.  Semantics  refers to the process by which we derive meaning from morphemes and words by connecting those morphemes and words to stored concepts.  Syntax  refers to the way words are organized into sentences (Chomsky, 1965; Fernández & Cairns, 2011). For example, you would never say “the dog walked I today” to let someone know you took your dog for a walk–that sentence does not obey English syntax and is therefore difficult to make sense of.

We apply the rules of grammar to organize the lexicon in novel and creative ways, which allow us to communicate information about both concrete and abstract concepts. We can talk about our immediate and observable surroundings as well as the surface of unseen planets. We can share our innermost thoughts, our plans for the future, and debate the value of a college education. We can provide detailed instructions for cooking a meal, fixing a car, or building a fire. Through our use of words and language, we are able to form, organize, and express ideas, schema, and artificial concepts.

Language Development

Given the remarkable complexity of a language, one might expect that mastering a language would be an especially arduous task; indeed, for those of us trying to learn a second language as adults, this might seem to be true. However, young children master language very quickly with relative ease. B. F.  Skinner  (1957) proposed that language is learned through reinforcement. Noam  Chomsky  (1965) criticized this behaviorist approach, asserting instead that the mechanisms underlying language acquisition are biologically determined. The use of language develops in the absence of formal instruction and appears to follow a very similar pattern in children from vastly different cultures and backgrounds. It would seem, therefore, that we are born with a biological predisposition to acquire a language (Chomsky, 1965; Fernández & Cairns, 2011). Moreover, it appears that there is a critical period for language acquisition, such that this proficiency at acquiring language is maximal early in life; generally, as people age, the ease with which they acquire and master new languages diminishes (Johnson & Newport, 1989; Lenneberg, 1967; Singleton, 1995).

Children begin to learn about language from a very early age ( Table 7.1 ). In fact, it appears that this is occurring even before we are born. Newborns show preference for their mother’s voice and appear to be able to discriminate between the language spoken by their mother and other languages. Babies are also attuned to the languages being used around them and show preferences for videos of faces that are moving in synchrony with the audio of spoken language versus videos that do not synchronize with the audio (Blossom & Morgan, 2006; Pickens, 1994; Spelke & Cortelyou, 1981).

DIG DEEPER: The Case of Genie

In the fall of 1970, a social worker in the Los Angeles area found a 13-year-old girl who was being raised in extremely neglectful and abusive conditions. The girl, who came to be known as Genie, had lived most of her life tied to a potty chair or confined to a crib in a small room that was kept closed with the curtains drawn. For a little over a decade, Genie had virtually no social interaction and no access to the outside world. As a result of these conditions, Genie was unable to stand up, chew solid food, or speak (Fromkin, Krashen, Curtiss, Rigler, & Rigler, 1974; Rymer, 1993). The police took Genie into protective custody.

Genie’s abilities improved dramatically following her removal from her abusive environment, and early on, it appeared she was acquiring language—much later than would be predicted by critical period hypotheses that had been posited at the time (Fromkin et al., 1974). Genie managed to amass an impressive vocabulary in a relatively short amount of time. However, she never developed a mastery of the grammatical aspects of language (Curtiss, 1981). Perhaps being deprived of the opportunity to learn language during a critical period impeded Genie’s ability to fully acquire and use language.

You may recall that each language has its own set of phonemes that are used to generate morphemes, words, and so on. Babies can discriminate among the sounds that make up a language (for example, they can tell the difference between the “s” in vision and the “ss” in fission); early on, they can differentiate between the sounds of all human languages, even those that do not occur in the languages that are used in their environments. However, by the time that they are about 1 year old, they can only discriminate among those phonemes that are used in the language or languages in their environments (Jensen, 2011; Werker & Lalonde, 1988; Werker & Tees, 1984).

After the first few months of life, babies enter what is known as the babbling stage, during which time they tend to produce single syllables that are repeated over and over. As time passes, more variations appear in the syllables that they produce. During this time, it is unlikely that the babies are trying to communicate; they are just as likely to babble when they are alone as when they are with their caregivers (Fernández & Cairns, 2011). Interestingly, babies who are raised in environments in which sign language is used will also begin to show babbling in the gestures of their hands during this stage (Petitto, Holowka, Sergio, Levy, & Ostry, 2004).

Generally, a child’s first word is uttered sometime between the ages of 1 year to 18 months, and for the next few months, the child will remain in the “one word” stage of language development. During this time, children know a number of words, but they only produce one-word utterances. The child’s early vocabulary is limited to familiar objects or events, often nouns. Although children in this stage only make one-word utterances, these words often carry larger meaning (Fernández & Cairns, 2011). So, for example, a child saying “cookie” could be identifying a cookie or asking for a cookie.

As a child’s lexicon grows, she begins to utter simple sentences and to acquire new vocabulary at a very rapid pace. In addition, children begin to demonstrate a clear understanding of the specific rules that apply to their language(s). Even the mistakes that children sometimes make provide evidence of just how much they understand about those rules. This is sometimes seen in the form of  overgeneralization . In this context, overgeneralization refers to an extension of a language rule to an exception to the rule. For example, in English, it is usually the case that an “s” is added to the end of a word to indicate plurality. For example, we speak of one dog versus two dogs. Young children will overgeneralize this rule to cases that are exceptions to the “add an s to the end of the word” rule and say things like “those two gooses” or “three mouses.” Clearly, the rules of the language are understood, even if the exceptions to the rules are still being learned (Moskowitz, 1978).

Language and Thought

When we speak one language, we agree that words are representations of ideas, people, places, and events. The given language that children learn is connected to their culture and surroundings. But can words themselves shape the way we think about things? Psychologists have long investigated the question of whether language shapes thoughts and actions, or whether our thoughts and beliefs shape our language. Two researchers, Edward Sapir and Benjamin Lee Whorf, began this investigation in the 1940s. They wanted to understand how the language habits of a community encourage members of that community to interpret language in a particular manner (Sapir, 1941/1964). Sapir and Whorf proposed that language determines thought. For example, in some languages there are many different words for love. However, in English we use the word love for all types of love. Does this affect how we think about love depending on the language that we speak (Whorf, 1956)? Researchers have since identified this view as too absolute, pointing out a lack of empiricism behind what Sapir and Whorf proposed (Abler, 2013; Boroditsky, 2011; van Troyer, 1994). Today, psychologists continue to study and debate the relationship between language and thought.

WHAT DO YOU THINK? The Meaning of Language

Think about what you know of other languages; perhaps you even speak multiple languages. Imagine for a moment that your closest friend fluently speaks more than one language. Do you think that friend thinks differently, depending on which language is being spoken? You may know a few words that are not translatable from their original language into English. For example, the Portuguese word  saudade  originated during the 15th century, when Portuguese sailors left home to explore the seas and travel to Africa or Asia. Those left behind described the emptiness and fondness they felt as  saudade  ( Figure 7.6 ) .  The word came to express many meanings, including loss, nostalgia, yearning, warm memories, and hope. There is no single word in English that includes all of those emotions in a single description. Do words such as  saudade  indicate that different languages produce different patterns of thought in people? What do you think??

One group of researchers who wanted to investigate how language influences thought compared how English speakers and the Dani people of Papua New Guinea think and speak about color. The Dani have two words for color: one word for  light  and one word for  dark . In contrast, the English language has 11 color words. Researchers hypothesized that the number of color terms could limit the ways that the Dani people conceptualized color. However, the Dani were able to distinguish colors with the same ability as English speakers, despite having fewer words at their disposal (Berlin & Kay, 1969). A recent review of research aimed at determining how language might affect something like color perception suggests that language can influence perceptual phenomena, especially in the left hemisphere of the brain. You may recall from earlier chapters that the left hemisphere is associated with language for most people. However, the right (less linguistic hemisphere) of the brain is less affected by linguistic influences on perception (Regier & Kay, 2009)

7.3 Problem Solving

• Describe problem solving strategies
• Define algorithm and heuristic
• Explain some common roadblocks to effective problem solving and decision making

People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

Problem-Solving Strategies

When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

A  problem-solving strategy  is a plan of action used to find a solution. Different strategies have different action plans associated with them ( Table 7.2 ). For example, a well-known strategy is  trial and error . The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Another type of strategy is an algorithm. An  algorithm  is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a  heuristic  is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backwards  is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

EVERYDAY CONNECTION: Solving Puzzles

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below ( Figure 7.7 ) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

Here is another popular type of puzzle ( Figure 7.8 ) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A  mental set  is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness  is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. Duncker (1945) conducted foundational research on functional fixedness. He created an experiment in which participants were given a candle, a book of matches, and a box of thumbtacks. They were instructed to use those items to attach the candle to the wall so that it did not drip wax onto the table below. Participants had to use functional fixedness to solve the problem ( Figure 7.10 ). During the  Apollo 13  mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An  anchoring bias  occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The  confirmation bias  is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis.  Hindsight bias  leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did.  Representative bias  describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the  availability heuristic  is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision .  Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in  Table 7.3 .

Were you able to determine how many marbles are needed to balance the scales in  Figure 7.9 ? You need nine. Were you able to solve the problems in  Figure 7.7  and  Figure 7.8 ? Here are the answers ( Figure 7.11 ).

Chapter Summary

7.1 what is cognition.

In this section, you were introduced to cognitive psychology, which is the study of cognition, or the brain’s ability to think, perceive, plan, analyze, and remember. Concepts and their corresponding prototypes help us quickly organize our thinking by creating categories into which we can sort new information. We also develop schemata, which are clusters of related concepts. Some schemata involve routines of thought and behavior, and these help us function properly in various situations without having to “think twice” about them. Schemata show up in social situations and routines of daily behavior.

7.2 Language

Language is a communication system that has both a lexicon and a system of grammar. Language acquisition occurs naturally and effortlessly during the early stages of life, and this acquisition occurs in a predictable sequence for individuals around the world. Language has a strong influence on thought, and the concept of how language may influence cognition remains an area of study and debate in psychology.

Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.

thinking; or, all of the processes associated with perception, knowledge, problem solving, judgement, language, and memory.

A modern school of psychological thought that empirically examines mental processes such as perception, memory, language, and judgement.

a category or grouping of linguistic information, images, ideas or memories, such as life experiences.

knowledge about words, concepts, and language-based knowledge and facts

the best example or representation of a concept, specific to an individual

concepts developed through direct or indirect experiences with the world

a concept defined by a specific set of characteristics.

a mental construct consisting of a cluster of related concepts

a set of ideas relating to how individuals in certain roles will behave.

also known as a cognitive script; a set of behaviors associated with a particular place or event

also known as an event schema; a set of behaviors associated with a particular place or event

a communication system that involves using words and systematic rules to organize those words to transmit information from one individual to another.

the words of a language

the rules of a language used to convey meaning through the use of the lexicon

the basic sounds that make up a language

the smallest unit of language that conveys meaning

the process by which we derive meaning from morphemes and words

the rules guiding the organization of morphemes into words and words into sentences.

Psychology 2e Copyright © 2020 by Openstax is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

Share This Book

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

The PMC website is updating on October 15, 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Front Neurosci

How musical training affects cognitive development: rhythm, reward and other modulating variables

Ewa a. miendlarzewska.

1 Department of Fundamental Neurosciences, (CMU), University of Geneva, Geneva, Switzerland

2 Swiss Centre of Affective Sciences, University of Geneva, Geneva, Switzerland

Wiebke J. Trost

Musical training has recently gained additional interest in education as increasing neuroscientific research demonstrates its positive effects on brain development. Neuroimaging revealed plastic changes in the brains of adult musicians but it is still unclear to what extent they are the product of intensive music training rather than of other factors, such as preexisting biological markers of musicality. In this review, we synthesize a large body of studies demonstrating that benefits of musical training extend beyond the skills it directly aims to train and last well into adulthood. For example, children who undergo musical training have better verbal memory, second language pronunciation accuracy, reading ability and executive functions. Learning to play an instrument as a child may even predict academic performance and IQ in young adulthood. The degree of observed structural and functional adaptation in the brain correlates with intensity and duration of practice. Importantly, the effects on cognitive development depend on the timing of musical initiation due to sensitive periods during development, as well as on several other modulating variables. Notably, we point to motivation, reward and social context of musical education, which are important yet neglected factors affecting the long-term benefits of musical training. Further, we introduce the notion of rhythmic entrainment and suggest that it may represent a mechanism supporting learning and development of executive functions. It also hones temporal processing and orienting of attention in time that may underlie enhancements observed in reading and verbal memory. We conclude that musical training uniquely engenders near and far transfer effects, preparing a foundation for a range of skills, and thus fostering cognitive development.

Introduction

Psychological and neuroscientific research demonstrates that musical training in children is associated with heightening of sound sensitivity as well as enhancement in verbal abilities and general reasoning skills. Studies in the domain of auditory cognitive neuroscience have begun revealing the functional and structural brain plasticity underlying these effects. However, the extent to which the intensity and duration of instrumental training or other factors such as family background, extracurricular activities, attention, motivation, or instructional methods contribute to the benefits for brain development is still not clear. Music training correlates with plastic changes in auditory, motor, and sensorimotor integration areas. However, the current state of the literature does not lend itself to the conclusion that the observed changes are caused by music training alone (Merrett et al., 2013 ).

In this article we briefly review the recent literature on how musical training changes brain structure and function in adult musicians and during development. We next report evidence for near and far transfer effects in various cognitive functions that are unprecedented in comparison to other long-term practice activities in childhood. Finally, we point out the important and overlooked role of other factors that could contribute to the observed cognitive enhancement as well as structural and functional brain differences between musicians and non-musicians. We propose the mechanism of rhythmic entrainment and social synchrony as factors contributing to the plasticity-promoting role of musical training that is unique to music education. The proposed mechanism of rhythmic synchronization by which musical training yields a unique advantage of transferrable skills may provide a promising avenue of research explaining the beneficial effects on a developing brain. In addition, we pinpoint the potentially important role of genetic predispositions and motivation that is rarely controlled for in the existing literature.

The review focuses on studies investigating healthy children's and adults' response to formal musical education (primarily instrumental training) in terms of neuroplasticity observed with neuroimaging techniques, as well as in behavioral effects on cognitive performance in various domains. Although we mention and acknowledge the enormous value of music therapy with the aim of restoring lost function in diseased or disabled individuals, this topic is outside the main focus of this review. Reviewing the progress in musical training research embraced in this article leads us to the promising supposition that the induced changes in brain development and plasticity are not only relevant in music-specific domains but also enhance other cognitive skills.

Cognitive, emotional and social functions in music perception and production

Listening to music requires certain perceptual abilities, including pitch discrimination, auditory memory, and selective attention in order to perceive the temporal and harmonic structure of the music as well as its affective components, and engages a distributed network of brain structures (Peretz and Zatorre, 2005 ). Music performance, unlike most other motor activities, in addition requires precise timing of several hierarchically organized actions and control over pitch interval production (Zatorre et al., 2007 ). Music, like all sounds, unfolds over time. Thus, the auditory cognitive system must depend on working memory mechanisms that allow a stimulus to be maintained on-line to be able to relate one element in a sequence to another that occurs later. The process of music recognition requires access and selection of potential predictions in a perceptual memory system (Dalla Bella et al., 2003 ; Peretz and Zatorre, 2005 ). Unlike speech, music is not associated with a fixed semantic system, although it may convey meaning through systems such as emotional appraisal (Koelsch, 2010 ; Trost et al., 2012 ) and associative memories.

Furthermore, music is also known to have a powerful emotional impact. Neuroimaging studies have shown that musically induced emotions involve very similar brain regions that are also implicated in non-musical basic emotions, such as the reward system, insula, and orbitofrontal cortex, amygdala and hippocampus (Blood and Zatorre, 2001 ; Koelsch et al., 2006 ; Salimpoor et al., 2011 ; Trost et al., 2012 ). However, music can have a strong influence on the emotion of the listener as well as the performer: musical engagement can be experienced as highly emotional not only as in the case of stage fright (Studer et al., 2011 ) but also as highly rewarding (de Manzano et al., 2010 ; Nakahara et al., 2011 ). Furthermore, in a social context, making music in a group has been suggested to increase communication, coordination, cooperation and even empathy between in-group members (Koelsch, 2010 ). Therefore, it could easily be conceived how musical training could have a positive impact on the well-being and social development of children and adults.

Instrumental training is a multisensory motor experience, typically initiated at an early age. Playing an instrument requires a host of skills, including reading a complex symbolic system (musical notation) and translating it into sequential, bimanual motor activity dependent on multisensory feedback; developing fine motor skills coupled with metric precision; memorizing long musical passages; and improvising within given musical parameters. Music performance, unlike most other motor activities, requires precise timing of several hierarchically organized actions and control over pitch interval production (Zatorre et al., 2007 ). Music sight-reading calls for the simultaneous and sequential processing of a vast amount of information in a very brief time for immediate use. This task requires, at the very least, interpretation of the pitch and duration of the notes (written on the two staves of a piano score) in the context of the prespecified key signature and meter, detection of familiar patterns, anticipation of what the music should sound like, and generation of a performance plan suited for motor translation. Formal musical instruction, therefore, trains a set of attentional and executive functions, which have both domain-specific and general consequences.

The musician's brain: plasticity and functional changes due to musical training

Given the engagement of multiple cognitive functions in musical activities, it seems natural that in highly trained musicians brain networks underlying these functions would show increased plasticity. Several recent review papers have critically assessed the effects of musical training on brain plasticity based on neuroimaging literature accumulated to this date (Herholz and Zatorre, 2012 ; Barrett et al., 2013 ; Moreno and Bidelman, 2013 ). Among others, it has been reported that apart from anatomical differences in auditory and motor cortices, there are structural differences (usually in the form of increased gray matter volume) also in somatosensory areas, premotor cortex, inferior temporal and frontal regions, as well as the cerebellum in the brains of musicians compared to non-musicians' (see Barrett et al., 2013 ). Several longitudinal studies have found a correlation between duration of musical training and the degree of structural change in white matter tracts (Bengtsson et al., 2005 ), including in the corpus callosum (Schlaug et al., 2005 ).

While it may not be surprising that structural and functional differences are found in those brain regions that are closely linked to skills learned during instrumental music training (such as independent fine motor movements in both hands and auditory discrimination), differences outside of these primary regions are particularly interesting (for instance, in the inferior frontal gyrus in Sluming et al., 2002 ). Such findings indicate that plasticity can occur in brain regions that either have control over primary musical functions or serve as multimodal integration regions for musical skills, possibly mediating the transfer of musical training onto other skills. For example, a recent study investigating resting-state activity measured with fMRI in musicians compared to non-musicians found that musicians have increased functional connectivity in motor and multi-sensory areas (Luo et al., 2012 ). This result shows that long-term musical training influences functional brain connectivity even in research designs where no task is given, and points out that for musicians' motor and multi-sensory networks may be better trained to act jointly.

In the next section, we review the effects of musical training on cognitive functions and brain plasticity and discuss the role of the age at commencement. However, we note that the evidence for musical training-induced brain plasticity is largely correlational due to the number of additional variables that have not been controlled for in most of the (cross-sectional) studies (Merrett et al., 2013 ), and that there are unanswered questions surrounding the attribution of causal influence to musical training alone. The few random group assignment studies that have been conducted to this date, typically include a control group of participants that attend theater play, dance (Young et al., 2013 ), or visual arts classes (Moreno et al., 2009 ; Moreno and Bidelman, 2013 ). And while the methodological and subject-specific considerations of this matter have been discussed elsewhere (Barrett et al., 2013 ; Merrett et al., 2013 ), in section Variables Modulating Brain Plasticity via Musical Trainin we propose possible unacknowledged mechanisms that enable musicians to excel in many areas unrelated to musical skill (near- and far-transfer skills described in section Effects on Cognitive Functions). Namely, we identify the higher efficiency of attentional and memory processes engendered by rhythmic entrainment, as well as an extension of this phenomenon to social synchrony that is evoked when people sing, play music or dance together in synchrony. To summarize, in Figure ​ Figure1 1 we propose a schema depicting the transfer skills that are enhanced by musical instrumental training, including the modulating factors discussed in sections Effects of Musical Training in Childhood and Variables Modulating Brain Plasticity via Musical Training.

Schematic representation of near and far transfer skills that benefit from musical instrumental training . In the inner rectangle variables modulating the influence of musical training on cognitive development are listed (see main text, in particular section Variables Modulating Brain Plasticity via Musical Training). Near transfer skills are marked in solid rectangles and far transfer skills are marked in dashed rectangles (described in detail in section Effects on Cognitive Functions). Terms in italic indicate results inconclusive in the present state of the literature.

Effects of musical training in childhood

Correlational and interventional studies of children undergoing music training consistently show that they perform better in the areas closely associated with music: fine motor skill, rhythm perception and auditory discrimination. There is also strong evidence for near-transfer effects of these abilities to phoneme discrimination, as well as far-transfer effects to vocabulary, and non-verbal reasoning subsets of general intelligence tests. While near-transfer effects (transfer to tasks within the same domain) are often observed with various training programs, such as computerized executive function training (attention, working memory and task-switching) (Diamond and Lee, 2011 ; Jolles and Crone, 2012 ), far-transfer is notoriously difficult to induce and has been observed only after demanding multi-skills training such as action video games (Bavelier et al., 2010 ; Green and Bavelier, 2012 ). The reports we review in this section show that musical training also brings about promising far-transfer effects in domains such as verbal intelligence and executive functions, and may even lead to better general academic performance.

Neural development is complex and various neural processes affect plasticity. Such processes include synaptic proliferation, pruning, myelination at neurofilament and neurotransmitter levels, each of which has its own developmental trajectory (e.g., Lenroot and Giedd, 2006 ; Perani et al., 2010 ). Observing brain plasticity as years of musical training go by elucidates the way practice becomes engraved in the brain and how memory finds its reflection in brain structure. In general, studies of music learning are consistent with the animal literature indicating greater plastic changes in the brain for behaviorally relevant (e.g., associated with reward or emotional arousal) than for passive exposure to auditory stimuli (Weinberger, 2004 ). However, the picture is not complete until we take into account the maturational dynamics that shape the brain simultaneously with musical training. The next section introduces the concept of critical and sensitive periods in brain development which, although not exhaustively, adds to the understanding of musical training-induced neuroplasticity. The notion of “windows of opportunity” is important in that it places limits on training-related brain plasticity and hence allows to explain why certain abilities can only be developed in early childhood, which is crucial for the design of educational programs and child rearing.

Critical and sensitive periods

It is known that plasticity is affected by how much a person actively engages in music training relatively early in their life (Knudsen, 2004 ). “Sensitive period” is a term applied to a limited period in development when the effects of experience on the brain are unusually strong, derived from the property of particular malleability of the neural circuits (Knudsen, 2004 ). During this time, the basic architecture of the neural circuits is laid out and all learning (and plasticity) that occurs after the sensitive period will cause alterations only within the connectivity patterns constraint by this framework (Knudsen, 2004 ). The regulation of sensitive period onset and duration is not simply by age, but by experience, and thus the presence of enriched environments may prolong sensitive periods (Hensch, 2004 ). For example, second language proficiency is better in individuals who have been exposed to it by the age of 11–13, marking puberty as the end of a sensitive period for language learning (Weber-Fox and Neville, 2001 ). In other words, the sensitive period is to some extent use-dependent (Hensch, 2004 ). In contrast, critical periods, are strict time windows during which experience provides information that is essential for normal development and permanently alters performance. For instance, critical period for auditory cortex plasticity ends by the age of 3–4 years in humans, as demonstrated in studies of cochlear implantation in congenitally deaf children: sensory deprivation in that time period prevents normal sensory discrimination and oral language learning (Kral and Sharma, 2012 ).

Not all brain regions develop with the same time course and there are unique timing and duration of critical periods across various neural systems. Sensory and motor regions enter the sensitive period earlier than temporal-parietal and frontal areas (Sowell et al., 2004 ), the visual cortex reaches adult levels of myelination by few months of life (Kinney et al., 1988 ), while in the auditory cortex myelination does not finish until 4–5 years of age (Moore and Linthicum, 2007 ) and white matter connectivity continues to develop until late childhood (Moore and Guan, 2001 ). Kral and Eggermont ( 2007 ) proposed that this extended period of developmental plasticity in the auditory cortex serves for language acquisition, wherein sensory bottom-up processing is trained by feedback from top-down cognitive processes. During this time, between ages 1 and 5, experience-dependent plasticity of the consistency of the auditory brainstem response is maximized (Skoe and Kraus, 2013 ).

Maturation of fiber tracts in the left frontal, temporo-occipital and anterior corpus callosum connecting the frontal lobes coincides with the development of working memory capacity, while reading ability is related to fractional anisotropy values in the left temporal lobe, as observed in children between ages of 8 and 18 (Nagy et al., 2004 ). Similarly, the maturation of corticospinal fibers parallels the development of fine finger movements (Paus et al., 1999 ). The cross-sectional area of the corpus callosum grows at least until early adulthood (Keshavan et al., 2002 ), while projection fibers of the posterior limb of the internal capsule (carrying sensory fibers to their processing areas in respective cortices) only approach an asymptotic point in maturation between the ages of 21 and 24 (Bava et al., 2010 ).

This sub-section emphasized that any intense training, including musical instrumental training in childhood, may have a different impact on brain plasticity and cognitive development depending on the age of commencement. However, many scholars of sensitive periods in brain development note that the role of motivation and attention is profound in all learning and should not be underestimated, especially during sensitive periods (Hensch, 2004 ). And as the example of language learning in infants shows (Kuhl et al., 2003 ; Kuhl, 2007 ), social environment and teachers may be of equally high importance.

Effects on brain plasticity

Plastic changes in the cortical and subcortical structures of the auditory system (Gregersen et al., 2000 ; Wong et al., 2007 ; Penhune, 2011 ), as well as in the sensory-motor cortex (larger representation of fingers) and their functional expression depend on early age of commencement (Herholz and Zatorre, 2012 ), which emphasizes the role of sensitive periods in shaping training-induced plasticity (Merrett et al., 2013 ). Instrumental training may accelerate the gradual development of neurofilament in upper cortical layers that occurs between ages 6 and 12, underlying fast, synchronized firing of neurons (Moore and Guan, 2001 ; Hannon and Trainor, 2007 ).

Two longitudinal studies tracked the influence of musical training on behavioral and brain activity in children between the ages of five and nine. Schlaug et al. ( 2005 ) recruited 50 children who were about to begin their musical education and compared them with a group of 25 age-, socioeconomic status and verbal IQ-matched controls. At baseline, there were no pre-existing cognitive, music, motor, or structural brain differences between the instrumental and control groups as tested by functional MR scans (Norton et al., 2005 ). Tests performed after 14 months of musical training revealed significantly greater change scores in the instrumental group compared to the control group in fine motor skills and auditory discrimination. However, no significant changes in gray or white matter volume nor transfer effects in domains such as verbal, visual–spatial, and math were found, but the instrumental group showed a trend in the anticipated direction.

A study by Hyde et al. ( 2009 ) compared two groups of 6 years old children, one of which took private keyboard lessons for 15 months and the other spent a similar amount of time per week in a group music lesson that included singing and playing with drums and bells. Applying deformation-based morphometry to assess the differences between groups throughout the whole brain before and after the musical training revealed that children with piano lessons showed areas of greater relative voxel size in motor brain areas, such as the right precentral gyrus (motor hand area), and the midbody of the corpus callosum, as well as in the right primary auditory region, consistent with the plastic changes observed in professional musicians. Furthermore, structural brain differences in various frontal areas were observed which, however, did not correlate with improvement in behavioral performance.

This evidence demonstrates that regular musical training during the sensitive period can induce structural changes in the brain and they are unlikely only due to pre-existing morphological differences. Yet, 14 months may not be long enough to engrave statistically significant growth in white and gray matter volume (Schlaug et al., 2005 ), and the differences observed may potentially be confounded by parents' higher level of education (Hyde et al., 2009 ).

Effects on cognitive functions

A further interesting question we explore in this section is the generalization of musical training-induced learning to other functional domains. According to the “temporal opportunity” conception of environmental stimulation during brain development, experiences in childhood and adolescence are vital to many abilities in adult life, which makes the decision of what education to provide to a child a serious matter. Is musical training a good choice? Although many longitudinal developmental studies of music education include a well-matched control group, such as another arts program, there is only limited research contrasting instrumental training in childhood with dance or sports, which could offer interesting avenues in plasticity research and aid the parents in making an informed decision. Thus, although all arts and sports programs do have beneficial effects on cognitive development (Green and Bavelier, 2008 ), instrumental musical training appears unique in the wide array of observed long-term effects, although there may be other factors mediating this effect (Young et al., 2013 ).

Listening skills

When comparing musically trained with untrained children, it is not surprising that differences in the performance of listening tasks and auditory processing are found. For example, it has been shown that children who benefit from musical lessons are more sensitive to the key and harmonics of Western music than untrained children (Corrigall and Trainor, 2009 ). More specifically, concerning pitch processing, children as young as 8, who have undergone a 6-month long music training, demonstrated increased accuracy in minor pitch differences discrimination and its electroencephalographic signature—increased amplitude of the N300 (Besson et al., 2007 ). No such differences were observed in the control group who has undergone an equal period of painting classes. Another recent well-controlled longitudinal study showed that children aged between 8 and 10 who benefitted from a 12-month music lesson program were better in discriminating syllabic duration and voice onset time in comparison to children who followed painting classes during the same period (Chobert et al., 2012 ). These results suggest thus that musical training can improve the temporal fine-tuning of auditory perception. Moreover, musicians are better at recognizing speech in noise, an ability developed through consistent practice and enhanced if music training began early in life (Parbery-Clark et al., 2009 , 2011 ; Strait et al., 2012 ).

Taken together, these results suggest that musical training increases listening skills, including sound discrimination, an ability also involved in speech segmentation (Francois et al., 2013 ), allowing a more accurate processing of speech and voices. In line with our proposed role of rhythmic entrainment (see section Rhythm and Entrainment below), Besson et al. ( 2011 ) suggested that these differences in language processing distinguishing musicians from non-musicians may reflect a learned ability to precisely orient attention in time in order to discriminate sounds more accurately.

Linguistic skills

Musical sounds and all other sounds share most of the processing stages throughout the auditory system and although speech is different from music production in several dimensions (Hannon and Trainor, 2007 ), musical training has been shown to transfer to language related skills. For example, auditory brainstem responses to stop consonants in musically trained children as young as 3 years is more distinct, indicating enhanced neural differentiation of similar sounds that characterizes adult musicians and later translates into better ability to distinguish sounds in speech (Strait et al., 2013 ). While the cross-links between language and musical training have been reviewed elsewhere (e.g., Chandrasekaran and Kraus, 2010 ; Besson et al., 2011 ; Strait and Kraus, 2011 , 2013 ), two examples include neurophysiological mechanisms underlying syntax processing in both music and language that develop earlier in children with musical training (Jentschke and Koelsch, 2009 ), and the transfer of musical training to pitch discrimination in speech as well as reading aloud in 8-year old children (Moreno et al., 2009 ).

The fact that music and language share common auditory substrates may indicate that exercising the responsible brain mechanisms with sounds from one domain could enhance the ability of these mechanisms to acquire sound categories in the other domain (Patel and Iversen, 2007 ; Patel, 2008 ). Patel argues in his OPERA hypothesis that the benefits of musicians in speech encoding are due to five mechanisms (Patel, 2011 , 2013 ). He suggests that there is an overlap of common brain networks between speech and music, which are especially trained because music production demands high precision. Furthermore, musical activities have high emotional reinforcement potential, which stimulates these brain networks repeatedly and requires a certain attentional focus. Patel claims that these processes are responsible for the good performance of musicians in speech processing.

This benefit of musical training can not only be found in tasks of auditory perception (for example tested with the Gordon's Intermediate Measures of Music Audiation, Schlaug et al., 2005 ), but also in verbal abilities such as verbal fluency and memory, second language acquisition and reading abilities, demonstrating far transfer effects of musical training (for a review see Besson et al., 2011 ). For example, it has been shown that children with musical training performed better at the vocabulary subtest of the Wechsler Intelligence Scale for Children (WISC-III) than a matched control group (Schlaug et al., 2005 ; Forgeard et al., 2008 ). Moreover, musical training has also been associated with enhanced verbal memory (Chan et al., 1998 ; Ho et al., 2003 ; Jakobson et al., 2003 ).

Research in adults clearly showed that musical ability could predict linguistic skills in the second language learning. Slevc and Miyake ( 2006 ) tested 50 Japanese adult learners of English, and found a relationship between musical ability and second language skills in receptive and productive phonology, showing that musical expertise can be a benefit for learning a second language. And in young children, a study by Milovanov et al. ( 2008 ) showed that second language pronunciation accuracy correlates with musical skills.

Empirical research on children and adults suggests that musical abilities predict phonological skills in language, such as reading. For example, Butzlaff ( 2000 ) found a significant association between music training and reading skills. In another study Anvari et al. ( 2002 ) studied the relation between early reading skills and musical development in a large sample of English-speaking 4- and 5-year-olds. Learning to read English requires mapping visual symbols onto phonemic contrasts, and thus taps into linguistic sound categorization skills. In this study, both musical pitch and rhythm discrimination were tested. For the group of 5-year-olds, performance on musical pitch, but not rhythm tasks predicted reading abilities. Such a finding is consistent with the idea of shared learning processes for linguistic and musical sound categories. However, despite this negative finding in 5-year old participants, there seems to be a link between abilities of rhythm production and reading, as we elaborate in section Rhythm and Entrainment below. For example, a recent study Tierney and Kraus showed that in adolescents the ability to tap to the beat is related to better reading abilities, as well as with performance in temporal attention demanding tasks, such as backward masking (Tierney and Kraus, 2013 ). This difference in rhythm processing might be due to the way how rhythm perception and production was studied by Anvari and colleagues, which required short term memory abilities, whereas the task of tapping to the beat solicits rather sensorimotor synchronization, and more importantly temporal orienting of attention—an ability required also in reading.

Spatial and mathematical skills

A meta-analysis of 15 experimental studies by Hetland ( 2000 ) showed that music instruction enhances performance on certain spatial tasks (such as the Object Assembly subtest of the WISC) but not on Raven's Standard Progressive Matrices, which is a test of non-verbal reasoning with some visual-spatial elements. The results of correlational studies testing the association between music training and spatial outcomes show no clear-cut association, with five out of 13 studies reporting a positive correlation between music training and spatial outcomes and eight a negative, null, or mixed results. Forgeard et al. ( 2008 ), however, did not find any differences in spatial skills between children who received at least 3 years of musical training and controls. Another study (Costa-Giomi, 1999 ) found that children receiving piano lessons improved more than controls in visual-spatial skills but only during the first 2 years of instruction, with no differences between the groups by the end of the third year. A study with adults showed that musicians did not perform better than non-musicians in a spatial working memory task (Hansen et al., 2012 ). It appears, therefore, that instrumental music training may aid the acquisition of spatial abilities in children rather than bring about a permanent advantage in musicians. Finally, Schlaug et al. ( 2005 ) found no transfer effects of musical training to math skills or general intelligence in 9–11-year-olds with an average of 4 years of musical training, although the children scored higher on the vocabulary subtest of the Wechsler Intelligence Scale for Children (WISC-III), suggesting that far transfer to linguistic abilities may be the most robust one, observable already after a relatively short period of practice.

A meta-analysis of the studies investigating the influence of musical training on math performance did not show convincing evidence in favor of a transfer effect (Vaughn, 2000 ). Also in more recent studies no positive relation between musical training and performance in a mathematical skills tests (Forgeard et al., 2008 ), nor increased musicality among mathematicians has been reported (Haimson et al., 2011 ).

Executive function

The notion of executive function refers to the cognitive processes orchestrated by the prefrontal cortex that allow us to stay focused on means and goals, and to willfully (with conscious control) alter our behaviors in response to changes in the environment (Banich, 2009 ). They include cognitive control (attention and inhibition), working memory and cognitive flexibility (task switching).

Hannon and Trainor ( 2007 ) proposed that musical training invokes domain-specific processes that affect salience of musical input and the amount of cortical tissue devoted to its processing, as well as processes of attention and executive functioning. In fact, the attentional and memory demands, as well as the coordination and ability to switch between different tasks, which are involved in learning to play an instrument, are very large. This learning depends on the integration of top-down and bottom up processes and it may well be that it is the training of this integration that underlies the enhanced attentional and memory processes observed in the musically trained (Trainor et al., 2009 ). Executive functions seem thus highly solicited when learning to play an instrument (Bialystok and Depape, 2009 ). In fact, Moreno et al. ( 2011 ) found that even after a short-term musical training (20 days) with a computerized program children improved their executive functions tested in a go-/no-go task. Similarly, in terms of working memory capacity, a recent longitudinal study showed that children that had been included in 18-months long instrumental music program outperformed the children in the control group that followed a natural science program during the same period (Roden et al., 2013 ).

General IQ and academic achievement

Extensive amount of research on how music can increase intelligence and make the listener smarter has been carried out (Rauscher et al., 1993 ; Degé et al., 2011 ; Moreno et al., 2011 ). The outcome of this research shows that not music listening but active engagement with music in the form of music lessons sometimes confers a positive impact on intelligence and cognitive functions although such results are not always replicated. A major discussion in this area is whether musical training increases specific skills or leads to a global un-specific increase in cognitive abilities, measured by a general IQ score.

For children, music lessons act as additional schooling—requiring focused attention, memorization, and the progressive mastery of a technical skill. It is therefore likely that transfer skills of executive function, self-control and sustained focused attention translate into better results in other subjects, and eventually in higher scores of general IQ. General IQ is typically tested with Raven's Progressive Matrices (Raven, 1976 ), although various types of intelligence can also be tested on specific tests. These tests require different kinds of cognitive performance, such as providing definitions of words or visualizing three-dimensional objects from two-dimensional diagrams, and are regarded as a good indicator of mental arithmetic skills and non-verbal reasoning. For example, Forgeard et al. ( 2008 ) found that practicing a musical instrument increases the performance in the Raven's Matrices test, which could suggest that non-verbal reasoning skills are better developed in children with musical training.

Measuring intelligence implies the sensitive discussion on genetic predisposition and environmental influence, and experience-acquired abilities. Schellenberg points out that children with higher cognitive abilities are more likely to take music lessons and that this fact can bias studies in which participants are not randomly assigned to music or control conditions (Schellenberg, 2011a ). Similarly, also the socioeconomic context is known to influence the probability that children get access to musical education (Southgate and Roscigno, 2009 ; Young et al., 2013 ). Controlling for this potentially confounding factor, Schellenberg ( 2006 ) reported a positive correlation between music lessons and IQ in 6–11 year olds, and showed that taking music lessons in childhood predicts both academic performance and IQ in young adulthood (holding constant family income and parents' education). In another study, two groups of 6 year-olds were tested, one of which received keyboard or singing lessons in small groups for 36 weeks (Schellenberg, 2004 ), and the other children received drama lessons. The latter did not show related increases in full-scale IQ and standardized educational achievement, but notably, the most pronounced results were in the group of children who received singing rather than piano lessons. Modest but consistent gains were made across all four indexes of the IQ, including verbal comprehension, perceptual organization, and freedom from distractibility and processing speed, suggesting that music training has widespread domain-general effects.

Intelligence measurements are often used to predict academic achievement. One question in this domain of research is therefore how musical activities influence academic achievement in children and adolescents. Despite initial claims that this effect may be primarily due to differences in socioeconomic status and family background, intervention studies as well as tests of general intelligence seem to show a positive association between music education and academic achievement. For example in a study by Southgate and Roscigno ( 2009 ) longitudinal data bases which include information on music participation, academic achievement and family background were analyzed. Their results show that indeed music involvement in- and outside of school can act as a mediator of academic achievement tested as math and reading skills. However, their results show also that there is a systematic relation between music participation and family background. Nonetheless, a recent study found that academic achievement can be predicted independently of socioeconomic status only when the child has access to a musical instrument (Young et al., 2013 ). Interestingly, this finding emphasizes that musical activities with an instrument differ from other arts activities in this respect.

Furthermore, it has been suggested that executive functions act as a mediator in the impact of music lessons on enhanced cognitive functions and intelligence. Schellenberg ( 2011a ) had the goal to investigate in detail this hypothesis of the mediating effect of the executive functions. He designed a study with 9–12-year old musically trained and un-trained children and tested their IQ and executive functions. Schellenberg's results suggest that there is no impact of executive functions on the relation between music training and intelligence. However, other studies have reported such an influence. For example there has been evidence that musical training improves executive function through training bimanual coordination, sustained attention and working memory (Diamond and Lee, 2011 ; Moreno et al., 2011 ). Degé et al. ( 2011 ) even used a design very similar to Schellenberg's with 9–12-year old children in order to test the role of executive functions. These authors did find a positive influence of musical training on executive functions and argued that this difference of results is due to the fact that in Schellenberg's study no direct measure of selective attention was included, which supposedly plays a crucial role in music.

Social skills

Apart from the concept of general IQ, Schellenberg ( 2011b ) studied the influence of musical training in children on emotional intelligence but did not find any relation between them. Moreover, another study with 7–8 year-old children found a positive correlation between musical training and emotion comprehension which disappeared, however, when the individual level of intelligence was controlled (Schellenberg and Mankarious, 2012 ). Also other studies with adults did not find any correlation between musical training and emotional intelligence (Trimmer and Cuddy, 2008 ). One study by Petrides and colleagues with musicians did find a positive correlation between length of musical training and scores of emotional intelligence (Petrides et al., 2006 ). There seems to be thus a still contradictory picture concerning the association between emotional intelligence and musical education. This result is interesting insofar as it could be thought that musical training could also increase social competences, given that active musical activities have shown to enhance communicative and social development in infants (Gerry et al., 2012 ). Moreover, a study by Kirschner and Tomasello ( 2009 ) found that in children at the age of 4 musical activities produced behaviors of spontaneous cooperation.

Another way to test social skills is to investigate the sensitivity to emotional prosody, which is a precious capacity in social communication. Studies have shown that musical training enhances the perception and recognition of emotions expressed by human voices (Strait et al., 2009 ; Lima and Castro, 2011 ), although an earlier study found that not musical training, but rather emotional intelligence predicted the recognition of emotional prosody (Trimmer and Cuddy, 2008 ). Thus, like with regards to emotional competence, the literature linking musical education and the recognition of emotional prosody is equivocal. The impact of musical education on social skills might therefore have to be investigated more in depth, comparing aspects such as music teaching methods in groups vs. single pupil lessons, and the role of musical activities in groups, for example in instrumental ensembles or choirs.

Plasticity over the life-span

Musical activities can have a beneficial impact on brain plasticity and cognitive and physical abilities also later in adult life after the critical and sensitive periods in childhood (Wan and Schlaug, 2010 ). For example, Herdender and colleagues showed that musical ear training in students can evoke functional changes in activation of the hippocampus in response to acoustic novelty detection (Herdener et al., 2010 ). In general, at an advanced age, a decline of cognitive functions and brain plasticity can be observed. However, physical as well as cognitive activities can have a positive impact on the preservation of these abilities in old age (Pitkala et al., 2013 ). In this sense, musical training has been proposed as a viable means to mitigate age-related changes in auditory cognition (for a review see Alain et al., 2013 )

It is often reported that with age fluid intelligence decreases and that this can be related to a diminishment of hippocampal volume (Reuben et al., 2011 ). In turn, a recent study by Oechslin et al. ( 2013 ) found that fluid intelligence is predicted by the volume of the hippocampus in musicians, which suggests that musical training could be used as a strategy to reduce age-related decline of fluid intelligence. In another study by Hanna-Pladdy and Mackay ( 2011 ), significant differences between elderly musicians and non-musicians (60–83 years) were found in non-verbal memory, verbal fluency, and executive functions. This shows as well that musical activity can prevent to some degree the decline of cognitive functions in ageing. However, these differences could be due to predisposition differences. Nonetheless, Bugos et al. ( 2007 ) performed a study in which predisposition influences were ruled out as they assigned participants randomly to two groups that received either piano lessons or no treatment. They found that persons over 60 who only began to learn to play the piano and continued during 6 months showed improved results in working memory tests as well as tests of motor skills and perceptual speed, in comparison to a control group without treatment. Dalcroze Eurhythmics, which is a pedagogic method based on learning music through movements and rhythm as basic elements has also been administered to seniors. One study showed that a treatment with this method during 6 months positively influences the equilibrium and regularity of gait in elderly (Trombetti et al., 2011 ). Given that falls in this population are a major risk, it is especially important to engage in training of these physical abilities at this age, which seems to be more efficient in combination with musical aspects of rhythmical movement synchronization and adaptation within a group.

Although there are promising results suggesting that older musicians compared to matched controls show benefits not only in near-transfer but also some far-transfer tasks such as visuospatial span, control over competing responses and distraction (Amer et al., 2013 ), the nature vs. nurture problem remains. Apart from the study of Bugos et al. ( 2007 ) who used a random-assignment design, research on the influence of musical training on plasticity and cognitive benefits in advanced ages should take into account the influence of other cognitive stimulations and overall physical fitness, which are known to play an important role in the preservation of cognitive functioning and independence in the elderly (Raz and Rodrigue, 2006 ; Erickson et al., 2012 ).

Variables modulating brain plasticity via musical training

One challenge in assessing developmental changes in the brain due to long-term learning such as musical training is that many studies demonstrating structural brain differences are retrospective and look at mature musicians, which does not rule out the possibility that people with certain structural atypicalities are more predisposed to become musicians. If this is the case, then the distinction between innate and developed differences is rather difficult. In fact, the biggest goal for most training studies, notwithstanding musical training, is to disentangle the effects of longitudinal training and pre-existing differences or factors other than the intervention, such as gender, genetic predisposition, general IQ, socio-economic background and parents' influence. Another difficulty of interventions in young populations concerns the fact that children's brains are very inhomogeneous, and therefore comparisons, even within similar age groups, may not be very informative.

Genetic predispositions

The musician's brain is recognized as a good model for studying neural plasticity (Munte et al., 2002 ). The fact that in several studies, a correlation was found between the extent of the anatomical differences and the age at which musical training started strongly argues against the possibility that these differences are preexisting and the cause, rather than the result of practicing music. On the other hand, the contamination of most longitudinal studies with children is that they are correlational, and most do not assign the subjects randomly to either musical education or a control group. As a result, the observed positive effects on cognitive functioning may not solely derive from practicing music but also from differences in motivation for learning or general intelligence, musical predispositions aside. Because general cognitive abilities (Deary et al., 2010 ) and personality (Veselka et al., 2009 ) are to some degree genetically predetermined, individual differences in these areas observed in musicians (vs. non-musicians) are unlikely to be solely a consequence of music training (Barrett et al., 2013 ; Corrigall et al., 2013 ).

The nature vs. nurture debate around musical practice-induced plasticity goes on and has begun to gain momentum as the number of neuroimaging studies continues to grow and recent genome-wide association studies have confirmed that many attributes of musicality are hereditary. Musical pitch perception (Drayna et al., 2001 ), absolute pitch (Theusch et al., 2009 ), as well as creativity in music (Ukkola et al., 2009 ), and perhaps even sensitivity to music (Levitin et al., 2004 ), have all been found to have genetic determinants. Importantly, these predispositions are typically tested for in children in a music school entrance exam. Therefore, it is fair to acknowledge that while learning a complex skill, such as playing an instrument, shapes brain function and structure, there may be additional explanatory variables that contribute to the observed differences between the brains of “musicians” and “non-musicians.”

Motivation and the rewarding power of music

At least some components of cognitive abilities that are found to be better in the musically-trained stem from innate qualities (Irvine, 1998 ), but it is difficult to expect ecologically-valid intervention studies to be able to untangle this factor from the effect of training (Barrett et al., 2013 ). Corrigall et al. ( 2013 ) have pointed out that musically trained children and adolescents are typically good students, with high auditory and visual working memory and high IQ not necessarily due to their music education but due to genetic predispositions, which also make them more likely to take on instrumental classes. They describe how a number of individual traits, such as conscientiousness, persistence, selective attention and self-discipline that are needed in music training, could be the pre-existing qualities that facilitate learning, brain plasticity, as well as far-transfer effects.

In fact, personality trait “openness to experience,” which Corrigall et al. ( 2013 ) found to be considerably more prominent in those who took music lessons than in those who did not, is correlated with curiosity and tendency to explore, and may affect the way children learn and approach new skills such as music. This particular personality trait is genetically determined to some extent and may be also responsible for motivation to learn. Specifically, the expression of dopamine D4 receptors in the prefrontal cortex has been associated with the trait Openness/Intellect (DeYoung et al., 2011 ) and it is considered that prefrontal dopaminergic transmission is responsible for attentional control and working memory (Robbins, 2005 ). Dopamine receptors also play a major role in shaping motivation: genetic variants of the proportion of the dopamine receptors type 1 to type 2 in the striatum (Frank and Fossella, 2010 ), determine the tendency to learn from positive feedback as opposed to negative feedback and may thus affect intrinsic motivation—a major factor in training any complex skill in the long term.

Rewarding value of a musical activity could be one of the driving forces for brain plasticity induced by musical training. Due to dopamine's important role in long-term memory formation (e.g., Lisman and Grace, 2005 ; Schott et al., 2006 ; Rossato et al., 2009 ; Wimber et al., 2011 ), both the genetic polymorphisms suggested above and activity-induced dopaminergic transmission will have an influence on learning outcome as well as on future learning and the reinforcing quality of music learning. A positive affective experience, such as pleasure and pride derived from first music lessons will likely promote future practice and total duration of training. In practice, it is difficult to control for levels of intrinsic motivation in empirical studies of musical training, such as those conducted by Moreno and colleagues (Besson et al., 2007 ; Moreno et al., 2009 ; Moreno and Bidelman, 2013 ), but its role may considerably affect the long-term outcome.

Other factors that affect music performance ability are emotional support from parents and a nurturing relationship with the teacher characterized by mutual liking (Sloboda, 1993 ). Although these are not the focus of this article, they greatly affect a child's motivation to practice and the learning outcome, and should be taken into consideration in future studies investigating effects of musical training compared to other forms of long-term training intervention.

Variance within musicians may also be a variable contributing to the musical training effect. The level of musical training is linked to pleasurable experience when listening to music (Gold et al., 2013 ), due to the adopted listening style in musicians and an involvement of the musically activated reward system that is also implicated in reinforcement learning (Salimpoor et al., 2013 ; Zatorre and Salimpoor, 2013 ). However, little is known about individual variability in music-induced positive emotional responses. It is possible, for instance, that individuals who experience deeply rewarding musical emotions are drawn to taking on musical training (again, with potential genetic influences such as in individuals with William's syndrome, Levitin, 2012 ). Later on, pleasure from the performance of music may add to the intrinsic motivation to continue training, thus forming a self-reinforcing cycle in which a student with innate predispositions to rewarding musical emotions experiences satisfaction with his own performance which encourages the student to practice. In addition, as with any skill learning that takes years to master, a high tolerance to frustration and perseverance are personality traits that would render a student more likely to continue the training (Barrett et al., 2013 ).

Interestingly, musicians may differ in the level of enjoyment they derive from their artistic activity, with a particular difference between popular, jazz- and folk- vs. classical musicians. Although studies mostly concentrate on musicians trained in playing a particular instrument, the type of education they received may affect the outcome not only due to instructional differences but also through differences in motivation. One large survey conducted in the UK between 2006 and 2008 reported that folk, jazz and popular music students/artists derive more pleasure from their work than classical musicians (de Bezenac and Swindells, 2009 ). The non-classical musicians reported more frequent “playing for fun” and generally more enjoyment derived from group performances. One of the study's conclusions was that popular music artists tend to have higher levels of intrinsic motivation (and reportedly learning to play an instrument out of own desire) and later age at training commencement than classical musicians. The latter, who may have been confronted with higher demands for discipline and compliance in the formal educational system, tended to value technical skills higher than pleasure, and presumably had higher levels of extrinsic motivation for awards in adult career, and for teacher's praise during training. Although brain plasticity studies have so far mainly concentrated on classical music education, it may be important to note that students with classical and non-classical music education may actually differ in personality traits (such as conscientiousness, Corrigall et al., 2013 ), motivational goals, and these could in turn contribute to observed transfer of cognitive advantage and their functional and structural brain correlates.

The aforementioned consideration of motivation as a learning-modulating variable leads us to the question of what happens to the learning outcomes and skill transfer in children who are forced to learn to play an instrument. In this case, music training may be an unpleasant and stressful experience. Stress experienced around the learning episode may actually promote the formation of memory related to the stressor via the cortisol and noradrenergic receptor activation in the amygdala which projects to the hippocampus and prioritizes consolidation of the emotional arousal-laden stimuli (Joëls et al., 2006 ). However, evidence form more ecological designs shows that stress impairs word learning and recall performance in comparison to no stress (Schwabe and Wolf, 2010 ). This has to do with the role of the amygdala in memory formation under stress: it not only enhances the consolidation of the stress-related stimuli but also facilitates a switch toward more habitual responding (mediated by the dorsal striatum) and away from goal-directed behavior that is mediated by the medial temporal lobe and the prefrontal cortex (Schwabe et al., 2010 ). The equivalent of such a switch in a typical learning situation would be moving away from deep, reflective processing under supportive, non-demanding circumstances to superficial processing under test-anxiety, which profoundly affects factual memory (Fransson, 1977 ).

Stress derived from fear of punishment therefore affects the way we learn and often leads to worse performance than reward motivation. The effect depends on the task at hand but a negative impact has been found in the formation of spatial (Murty et al., 2011 ), procedural (Wächter et al., 2009 ) and declarative memory formation that requires cognitive processing (Schwabe et al., 2010 ). Although we cannot exhaustively elaborate on the literature treating motivation, learning and transfer in education research, suffice it to say that some forms of punishment motivation resulting in stress have a negative impact on learning (Lepine et al., 2004 ).

In the context of musical education, we suggest thus that the aforementioned influence of personality and intrinsic motivation should be taken into account in future studies. For example, in random assignment studies on the impact of musical training, participants should also be asked to declare their personal motivation to adhere to the training at least before and after the intervention. Furthermore, personality questionnaires could be incorporated to test for traits that affect the learning style (e.g., reward sensitivity, openness, perseverance). These factors could then be used as covariates in the analysis of the effect of musical training in both behavioral and neuroimaging studies. Such information would help determine the extent of the influence of personality and motivational disposition on the long-term adherence to the program as well as its outcome in terms of transfer skills. This could be particularly pertinent given the fact, that these factors could not only limit the positive effects of musical activities but even be detrimental to cognitive and emotional development if the activity represents mainly a source of stress and negative affect. In addition, this information might also help to disentangle the real impact of the training from the influence of personality and motivation.

Rhythm and entrainment

Here we want to point at one specific aspect, which could represent an underlying mechanism of the beneficial transferrable effects of musical training. This specific feature is related to the fact that musical activities are usually based on rhythm. Most musical styles have an underlying temporal pattern that is called meter, which defines a hierarchical structure between time points (London, 2004 ). Ontogenetically, rhythm discrimination is observed in infants as young as 2 months of age (Trehub and Hannon, 2006 ). Like adults, 7-months old infants can infer an underlying beat, categorizing rhythms on the basis of meter (Hannon and Johnson, 2005 ), and 9-month old infants can more readily notice small timing discrepancies in strongly metrical than in non-metrical rhythms (Bergeson and Trehub, 2006 ).

The theory of dynamic attending suggests that rhythmical patterns in music can only be perceived because of a synchronization of attentional processes which entrain to the periodicities contained in the auditory rhythm (Jones and Boltz, 1989 ). In fact, neuronal populations in the visual cortex entrain to the regular rhythm of stimulus presentation which constitutes a mechanism of attentional selection (Lakatos et al., 2008 ). It has therefore been suggested that musical activities that imply perception and production of rhythms train attentional processes which benefits also other cognitive functions. Indeed, a recent study with children showed that musical activities increase the accuracy of produced rhythms (Slater et al., 2013 ), while adult musicians are significantly more accurate in reproducing rhythmic intervals (Chen et al., 2008 ), detecting metrical irregularities (James et al., 2012 ) and maintaining the rhythm when none is externally provided (Baer et al., 2013 ).

Entrainment is in fact a physical principle which describes the adaptation of at least two oscillating agents toward a common phase and period, which could eventually lead to perfect synchronicity between the oscillators (Rosenblum and Pikovsky, 2003 ). In this sense also the adjustment of behavior (own musical output, in ensemble playing, or movements, as in dance) to the perceived regular rhythm or extracted pulse can be regarded as entrainment (Fitch, 2013 ). Humans can also entrain multiple motor modalities, including for example body or limb motions, vocalization and even breathing and heart rate (Müller and Lindenberger, 2011 ; Trost and Vuilleumier, 2013 ). Neural populations can also be entrained by sensory stimulation (Gander et al., 2010 ) or motion, such as being rocked (Bayer et al., 2011 ).

Research on subcortical brain plasticity has used the frequency following response (FFR) as an indicator of perceptual acuity (Moreno and Bidelman, 2013 ). The FFR is a component of the auditory brainstem response (Tzounopoulos and Kraus, 2009 ) that is phase- and frequency-locked to the acoustic parameters of an auditory stimulus. In this sense the FFR represents evidence of direct neural entrainment to the sound, be it music or speech. Several studies have used this method to test training-derived plasticity in the perceptual processing of musical and vocal parameters or speech, demonstrating faster response in musical experts (Tzounopoulos and Kraus, 2009 ; Chandrasekaran and Kraus, 2010 ).

Furthermore, there is a close link between language and reading skills and the ability to perceive and produce rhythm, as widely documented by studies in children with dyslexia (Huss et al., 2011 ; Goswami, 2012 ), or with attention deficits as for example attention-deficit-hyperactivity disorder (Ben-Pazi et al., 2003 ), who show difficulties in rhythmic tasks. In fact, priming with a rhythmic sequence facilitates speech processing (Cason and Schon, 2012 ), and performance on perceptual discrimination in all sensory domains as well as motor response tasks is better when stimuli are presented isochronously (Nobre et al., 2007 ).

It appears thus that in musical education, daily training of the temporal processing mechanisms has a beneficial effect on other cognitive functions, such as reading, in which attention has to be guided in a specific manner. Moreover, a study by Tierney and Kraus ( 2013 ) showed that the ability to tap to a beat was associated with better performance not only in reading but also in other attention-demanding tasks which are purportedly at the basis of executive functions. Tapping to, producing or merely perceiving a rhythm in any sensory domain leads to formation of expectations that facilitates orienting of attentional resources (Bolger et al., 2013 ) and entrainment of various bodily and neural functions. There is also evidence that timing or temporal processing is a skill partially explaining individual variability in cognitive-speed and non-verbal ability measures—findings based on the isochronous serial interval production task (Sheppard and Vernon, 2008 ; Holm et al., 2011 ; Loras et al., 2013 ). And it may even support superior auditory verbal memory in musicians (Jakobson et al., 2003 ).

Being able to tap to an acoustic beat may be important for executive function (Tierney and Kraus, 2013 ) and implies coordination of movements, anticipation and sensorimotor integration. Being able to synchronize to an external rhythm while playing an instrument requires not only fine motor skills but also good auditory-motor coordination and sensorimotor integration—capacities that are also vital in planning and executing movements in general. Indeed, the functional neuroimaging signature of sensorimotor integration is increased in musicians performing a temporal synchronization task and involves increases in brain network interaction including premotor cortex, posterior parietal cortex and thalamus (Krause et al., 2010 ), which are also involved in attentional processes and motor planning (Coull, 2004 ). Furthermore, this ability of locking into temporal patterns is a skill that is useful in social communication, in which reciprocity and turn taking is essential.

The mentioned aspects of attentional guiding, forming temporal expectations, auditory-motor integration, coordination of movements and social interaction have all in common that they are based on a synchronization and adaptation of internal processes to the external rhythm of the music, or the actions of other musicians (Trost and Vuilleumier, 2013 ). We therefore suggest that rhythmic entrainment and ensuing honing of temporal processing play a key role in the beneficial influence of music education on the development of executive functions and far transfer effects.

Rhythmic entrainment has also been suggested as an emotion induction mechanism (Juslin et al., 2010 ). According to Juslin and colleagues the process where internal bodily rhythms such as respiration adapt to the external rhythm of the music contributes to the induction of an emotional reaction. Being in synchrony with the music or with the other musicians would therefore represent an emotional and often rewarding experience. As pointed out in the previous section on the influence of motivation, positive emotional experiences that activate the reward system modulate memory formation and favor brain plasticity.

Furthermore, musical activities are often social. Indeed, it has been proposed that the evolutionary function of music has always been to increase cooperation, coordination, communication, co-pathy, contact, social cognition and cohesion between the members of a group (Koelsch, 2010 ). It seems that one of these effects is the fact that a certain form of social synchronization is instilled, implying the respect of and adaptation to each other. In fact, in empirical studies it has often been described that acting in synchrony with a partner may increase prosocial commitment (Kokal et al., 2011 ), social affiliation (Hove and Risen, 2009 ), trust (Launay et al., 2013 ), cooperation (Wiltermuth and Heath, 2009 ) and feelings of compassion (Valdesolo et al., 2010 ; Valdesolo and Desteno, 2011 ). When playing music in a group one has to automatically synchronize to the other musicians. The state of synchrony is therefore generated naturally and is possible already in pre-school children who synchronize their drumming more easily in a social context (Kirschner and Tomasello, 2009 ). Learning to perform an activity in synchrony together with others is supported by the activation of the mirror neuron system (Tognoli et al., 2007 ; Overy and Molnar-Szakacs, 2009 ). We therefore suggest that this social aspect of musical training may add to the role of reward and motivation in shaping a developing brain. Moreover, learning of some skills (singing in a choir, playing in an ensemble) sets musical training apart from other social activities that do not require synchronization of actions with other group members specifically thanks to the engagement of the mirror neuron system.

Another piece of evidence for brain plasticity induced directly via rhythmic entrainment comes from the rehabilitation literature. Rhythmic auditory stimulation (RAS) is an important method in brain stimulation, which may induce short but also long term plasticity in a damaged brain (Thaut and Abiru, 2010 ). For example, in Parkinson patients stimulating the dopaminergic circuits in the basal ganglia leads to a reduction of movement disorder symptoms (Thaut et al., 1996 ; Pacchetti et al., 2000 ). In other neurologic diseases or acquired brain injury RAS also has beneficial effects, as the synchronization to an external beat helps to recover the coordination of movements via the stimulation of auditory-motor and sensorimotor integration (Bradt et al., 2010 ; Rodriguez-Fornells et al., 2012 ).

Neuronal populations in various areas of the brain can be entrained by external stimulation (Gomez-Ramirez et al., 2011 ; Nozaradan et al., 2011 ; Thut et al., 2011 ), and temporal prediction underlying the capacity for rhythmic sensorimotor synchronization (Repp, 2005 ; Repp and Su, 2013 ) has been suggested to play a general role in efficient behavior (Schwartze and Kotz, 2013 ). Specifically, maintenance of rhythmic regularity of neural oscillations within particular frequency bands has been suggested as a mechanism of communication across distant neural areas (Canolty and Knight, 2010 ; Grahn, 2012 ), as well as of sensory perception (Thut and Miniussi, 2009 ), and memory consolidation, particularly during sleep (Fell and Axmacher, 2011 ). Rhythmic entrainment can be conditioned using e.g., sensorimotor stimulation (Schabus et al., 2013 ), or induced in a sleeping brain using electric (Marshall et al., 2006 ) or auditory (Ngo et al., 2013 ) stimulation, which results in increased amount of slow waves, improved quality of sleep and better declarative memory.

Even in the animal literature interesting effect of rhythmic entrainment can be found (Rickard et al., 2005 ). For example, based on a series of experiments on neonate chicks, Rickard and colleagues observed that complex rhythmic auditory stimuli enhance memory by promoting moderate levels of physiological arousal through noradrenergic modulation of the memory systems (Toukhsati and Rickard, 2001 ; Rickard et al., 2005 ; Rickard, 2009 ). The authors concluded that it is important that the auditory stimulus contains a certain rhythmical complexity as simple metronomic beats of non-metrical rhythms do not have any memory enhancing effect (Toukhsati and Rickard, 2001 ). Furthermore, non-metrical (that is, consisting of tones that are not aligned with the dominant beat) music produced learning and memory deficits on a maze task in mice, while rats exposed to non-rhythmic music performed poorly in a spatial learning task (Schreckenberg and Bird, 1987 ; Rauscher et al., 1998 ). This suggests that auditory stimulations with a non-metrical rhythmical structure could compromise memory processes. Furthermore, these studies show that even in a passive listening condition performance could be improved in animals. However, we would like to emphasize that an active involvement in a rhythmic activity could amplify the effects even more, as it is known that active participation in a musical activity compared to passive listening only has a stronger effect on, e.g., executive function in the elderly (Bugos, 2010 ).

We propose that active engagement involving a synchronized production of motor responses is necessary for the facilitatory effect on attentional resources, movement control, auditory working memory and other functions that rely on temporal processing, as well as social synchrony. These particular aspects of musical training, absent in visual arts or theater training, contribute to the wide development of cognitive abilities and render it very different from other forms of artistic expression. We therefore suggest that a proper control group comparing music as a form of childhood intervention would be to use a group activity characterized by both rhythmic entrainment and social synchrony, such as for example team sport lessons (e.g., rowing, badminton or volleyball).

In this review of the literature we show that musical training in childhood not only enhances many cognitive functions but is accompanied by neuroplastic changes in brain structure and function. Although this influence appears to be strongly potentiated when musical training takes place during sensitive periods, we have given some examples that music-induced brain plasticity does occur also later in life. In this article we wanted to point to specific factors affecting the relative value of musical education in comparison to other types of longitudinal training in childhood that require similar engagement of cognitive resources and demand a significant overall time investment. These factors include the importance of motivation, affect and social communication in music learning, as well as the potential role of rhythmic entrainment. Consequently, several issues, which have been treated in other recent review papers, remained beyond the scope of this review. Musical training results in better achievement in domains other than mere music performance, such as verbal abilities, second language learning, non-verbal reasoning and general intelligence. The advice for parents and educators is therefore clear: promote instrumental training in early childhood, as it may result in life-long advantages. However, the precise timing of the “windows of opportunity” within which particular environmental stimulation should be provided to a child to have the strongest impact, is probably before the age of seven, although the auditory system could benefit from earlier commencement, by the age of five (or even 2, Skoe and Kraus, 2013 ), while other structures, such as for example the white matter tracts, remain plastic well into the adulthood.

Notably, the aspect of motivation is underrepresented in the existing literature on musical training. The link between reward system activity and various forms of learning is well known: for instance, hippocampal learning (spatial, semantic, and episodic memory) is enhanced with the simultaneous activity of the reward system (involving the dopaminergic neural pathway from the ventral tegmental area and the ventral striatum Lisman and Grace, 2005 ), not to mention the simple reward conditioning mechanism underlying many well-documented tasks where learning and memory enhancements have been observed (e.g., Delgado and Dickerson, 2012 ).

Furthermore, in this review we have proposed rhythmic entrainment as a major underlying mechanism that is responsible for the beneficial effect of musical training on cognitive functions, especially with regard to the executive functions. Musical rhythms help to orient attentional processes in time, which implies a benefit for preparation and control of motor actions, and honing of temporal processing of information. We have reported several studies suggesting that these processes are also linked to cognitive functions such as reading ability and attentional focus. Furthermore, rhythmic entrainment is also regarded as an emotion induction mechanism, which again hints at the pleasurable aspect of musical activities.

On the other hand, music performance is frequently associated with a neglected and less glamorous aspect of a musician's life—performance anxiety (Kenny and Osborne, 2006 ). Stress also plays a role in learning, with moderate levels enhancing learning and high levels inhibiting it (Joëls et al., 2006 ; Howland and Wang, 2008 ). Unlike in a typical primary school curriculum, formal music education exposes a child to many opportunities for live solo performances, which may be stressful at times, not to mention the felt intrinsic pressure to perform well while the whole audience is listening for a possible error (Stoeber and Eismann, 2007 ). Naturally, exposure to such stress enables the individual to learn to overcome its disempowering impact with time, but it may be a source of childhood stress, which should be taken into consideration especially in case of highly sensitive individuals. Therefore, future studies of any long-term training intervention should ideally take into account individual differences in motivation to learn to play an instrument, as well as of subjective stress levels associated with the learning activity, as factors potentially modulating the investigated effects.

In Figure ​ Figure1 1 we provide a summary of the near- and far-transfer skills promoted by musical training, according to the literature reviewed in section Effects on Cognitive Functions. We designate skills that are closely linked to the musical training domain, such as fine motor skills and listening as near-transfer skills. In particular, we identify temporal processing and orienting of attention in time as an ability that is perfected in musicians but that has not been explicitly described as a transfer skill. Instead, we point that this particular skill—processing of temporal information—likely underlies farther transfer skills such as reading and verbal memory. Far-transfer skills include abilities unrelated to the context of playing an instrument but that generalize to other domains, such as executive functions and linguistic skills. In the center, we listed the variables modulating the effects of musical training. These include, firstly, genetically determined predispositions (musicality, personality and motivational disposition; section Genetic Predispositions), and age at commencement (section Critical and Sensitive Periods). Secondly, we list the degree of intrinsic motivation and its affective quality (associated with punishment or reward), and the role of parents and teachers (section Motivation and the Rewarding Power of Music). And thirdly, two factors that modulate brain development through musical training itself: rhythmic entrainment and music-induced rewarding emotions (sections Rhythm and Entrainment & Motivation and the Rewarding Power of Music).

Learning to play an instrument offers a child the opportunity for creative self-expression and the development of an identity. Furthermore, musical training can be a leisure activity and a possibility to learn a form of discipline outside of the frame of the school curriculum, which gives the opportunity for rewarding experiences of self-achievement and positive reinforcement. Moreover, music education in preschool children, or first years of instrumental classes, as well as singing in a choir, has an important social component. Learning to make music together requires the respect of others and teaches implicit communicative rules and skills. In fact, it has been suggested that making music in a group might have served an evolutionary purpose of increasing communication, coordination, cooperation and even empathy within a group (Koelsch, 2010 ). This notion emphasizes the fact that making music in a group can be a very rewarding activity. Furthermore, social context and well-being also have a decisive influence on brain plasticity (Davidson and McEwen, 2012 ), which suggests that well-being induced by musical activities would in turn help shape brain functions via the mediating influence of the reward system. Therefore, we conclude that musical education starting already early in childhood offers the opportunity to tune and train the brain for important cognitive and possibly also social functions. Furthermore, it provides the child with techniques and foundations, which will probably serve as a benefit for the entire lifetime; not to mention that having learned to play an instrument in childhood may be a great source of pleasure later on in life.

Author contributions

Ewa A. Miendlarzewska and Wiebke J. Trost have both contributed to the writing of the manuscript.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors thank the National Center of Competence in Research (NCCR) in Affective Sciences (No. 51NF40-104897) at the University of Geneva for supporting this publication. Ewa A. Miendlarzewska would like to thank Carlo V. Cannistraci for inspiring this review.

• Alain C., Zendel B. R., Hutka S., Bidelman G. M. (2013). Turning down the noise: the benefit of musical training on the aging auditory brain . Hear. Res . [Epub ahead of print]. 10.1016/j.heares.2013.06.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Amer T., Kalender B., Hasher L., Trehub S. E., Wong Y. (2013). Do older professional musicians have cognitive advantages? PLoS ONE 8 :e71630 10.1371/journal.pone.0071630 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Anvari S. H., Trainor L. J., Woodside J., Levy B. A. (2002). Relations among musical skills, phonological processing, and early reading ability in preschool children . J. Exp. Child Psychol . 83 , 111–130 10.1016/S0022-0965(02)00124-8 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baer L. H., Thibodeau J. L., Gralnick T. M., Li K. Z., Penhune V. B. (2013). The role of musical training in emergent and event-based timing . Front. Hum. Neurosci . 7 : 191 10.3389/fnhum.2013.00191 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Banich M. T. (2009). Executive function the search for an integrated account . Curr. Dir. Psychol. Sci . 18 , 89–94 10.1111/j.1467-8721.2009.01615.x [ CrossRef ] [ Google Scholar ]
• Barrett K. C., Ashley R., Strait D. L., Kraus N. (2013). Art and science: how musical training shapes the brain . Front. Psychol . 4 : 713 10.3389/fpsyg.2013.00713 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bava S., Thayer R., Jacobus J., Ward M., Jernigan T. L., Tapert S. F. (2010). Longitudinal characterization of white matter maturation during adolescence . Brain Res . 1327 , 38–46 10.1016/j.brainres.2010.02.066 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bavelier D., Green C. S., Dye M. W. G. (2010). Children, wired: for better and for worse . Neuron 67 , 692–701 10.1016/j.neuron.2010.08.035 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bayer L., Constantinescu I., Perrig S., Vienne J., Vidal P.-P., Mühlethaler M., et al. (2011). Rocking synchronizes brain waves during a short nap . Curr. Biol . 21 , R461–R462 10.1016/j.cub.2011.05.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bengtsson S. L., Nagy Z., Skare S., Forsman L., Forssberg H., Ullén F. (2005). Extensive piano practicing has regionally specific effects on white matter development . Nat. Neurosci . 8 , 1148–1150 10.1038/nn1516 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ben-Pazi H., Gross-Tsur V., Bergman H., Shalev R. S. (2003). Abnormal rhythmic motor response in children with attention-deficit-hyperactivity disorder . Dev. Med. Child Neurol . 45 , 743–745 10.1111/j.1469-8749.2003.tb00883.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bergeson T. R., Trehub S. E. (2006). Infants perception of rhythmic patterns . Music Percept . 23 , 345–360 10.1525/mp.2006.23.4.345 [ CrossRef ] [ Google Scholar ]
• Besson M., Chobert J., Marie C. (2011). Transfer of training between music and speech: common processing, attention, and memory . Front. Psychol . 2 : 94 10.3389/fpsyg.2011.00094 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Besson M., Schon D., Moreno S., Santos A. E., Magne C. (2007). Influence of musical expertise and musical training on pitch processing in music and language . Restor. Neurol. Neurosci . 25 , 399–410 [ PubMed ] [ Google Scholar ]
• Bialystok E., Depape A. M. (2009). Musical expertise, bilingualism, and executive functioning . J. Exp. Psychol. Hum. Percept. Perform . 35 , 565–574 10.1037/a0012735 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Blood A. J., Zatorre R. J. (2001). Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion . Proc. Natl. Acad. Sci. U.S.A . 98 , 11818–11823 10.1073/pnas.191355898 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bolger D., Trost W., Schön D. (2013). Rhythm implicitly affects temporal orienting of attention across modalities . Acta Psychol . 142 , 238–244 10.1016/j.actpsy.2012.11.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bradt J., Magee W. L., Dileo C., Wheeler B. L., McGilloway E. (2010). Music therapy for acquired brain injury . Cochrane Database Syst. Rev . 7 :CD006787 10.1002/14651858.CD006787.pub2 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bugos J. A. (2010). The benefits of music instruction on processing speed, verbal fluency, and cognitive control in aging . Music Educ. Res. Int . 4 , 1–9 [ Google Scholar ]
• Bugos J. A., Perlstein W. M., McCrae C. S., Brophy T. S., Bedenbaugh P. H. (2007). Individualized piano instruction enhances executive functioning and working memory in older adults . Aging Ment. Health 11 , 464–471 10.1080/13607860601086504 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Butzlaff R. (2000). Can music be used to teach reading? J. Aesthet. Educ . 34 , 167–178 10.2307/3333642 [ CrossRef ] [ Google Scholar ]
• Canolty R. T., Knight R. T. (2010). The functional role of cross-frequency coupling . Trends Cogn. Sci . 14 , 506–515 10.1016/j.tics.2010.09.001 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cason N., Schon D. (2012). Rhythmic priming enhances the phonological processing of speech . Neuropsychologia 50 , 2652–2658 10.1016/j.neuropsychologia.2012.07.018 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chan A. S., Ho Y., Cheung M. (1998). Music training improves verbal memory . Nature 396 , 128 10.1038/24075 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chandrasekaran B., Kraus N. (2010). Music, noise-exclusion, and learning . Music Percept . 27 , 297–306 10.1525/mp.2010.27.4.297 [ CrossRef ] [ Google Scholar ]
• Chen J. L., Penhune V. B., Zatorre R. J. (2008). Moving in time: brain networks for auditory-motor synchronization are modulated by rhythm complexity and musical training . J. Cogn. Neurosci . 20 , 226–239 10.1162/jocn.2008.20018 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chobert J., Francois C., Velay J. L., Besson M. (2012). Twelve months of active musical training in 8- to 10-year-old children enhances the preattentive processing of syllabic duration and voice onset time . Cereb Cortex . [Epub ahead of print]. 10.1093/cercor/bhs377 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Corrigall K. A., Schellenberg E. G., Misura N. M. (2013). Music training, cognition, and personality . Front. Psychol . 4 : 222 10.3389/fpsyg.2013.00222 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Corrigall K. A., Trainor L. J. (2009). Effects of musical training on key and harmony perception . Ann. N.Y. Acad. Sci . 1169 , 164–168 10.1111/j.1749-6632.2009.04769.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Costa-Giomi E. (1999). The effects of three years of piano instruction on children's cognitive development . J. Res. Music Educ . 47 , 198–212 10.2307/3345779 [ CrossRef ] [ Google Scholar ]
• Coull J. T. (2004). fMRI studies of temporal attention: allocating attention within, or towards, time . Cogn. Brain Res . 21 , 216–226 10.1016/j.cogbrainres.2004.02.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dalla Bella S., Peretz I., Aronoff N. (2003). Time course of melody recognition: a gating paradigm study . Percept. Psychophys . 65 , 1019–1028 10.3758/BF03194831 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Davidson R. J., McEwen B. S. (2012). Social influences on neuroplasticity: stress and interventions to promote well-being . Nat. Neurosci . 15 , 689–695 10.1038/nn.3093 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Deary I. J., Penke L., Johnson W. (2010). The neuroscience of human intelligence differences . Nat. Rev. Neurosci . 11 , 201–211 10.1038/nrn2793 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• de Bezenac C., Swindells R. (2009). No pain, no gain? Motivation and self-regulation in music learning . Int. J. Educ. Arts . 10 , 1–34 [ Google Scholar ]
• Degé F., Kubicek C., Schwarzer G. (2011). Music lessons and intelligence: a relation mediated by executive functions . Music Percept . 29 , 195–201 10.1525/mp.2011.29.2.195 [ CrossRef ] [ Google Scholar ]
• Delgado M. R., Dickerson K. C. (2012). Reward-related learning via multiple memory systems . Biol. Psychiatry 72 , 134–141 10.1016/j.biopsych.2012.01.023 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• de Manzano O., Theorell T., Harmat L., Ullen F. (2010). The psychophysiology of flow during piano playing . Emotion 10 , 301–311 10.1037/a0018432 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• DeYoung C. G., Cicchetti D., Rogosch F. A., Gray J. R., Eastman M., Grigorenko E. L. (2011). Sources of cognitive exploration: genetic variation in the prefrontal dopamine system predicts Openness/Intellect . J. Res. Pers . 45 , 364–371 10.1016/j.jrp.2011.04.002 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Diamond A., Lee K. (2011). Interventions shown to aid executive function development in children 4 to 12 years old . Science 333 , 959–964 10.1126/science.1204529 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Drayna D., Manichaikul A., de Lange M., Snieder H., Spector T. (2001). Genetic correlates of musical pitch recognition in humans . Science 291 , 1969–1972 10.1126/science.291.5510.1969 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Erickson K. I., Weinstein A. M., Lopez O. L. (2012). Physical activity, brain plasticity, and Alzheimer's disease . Arch. Med. Res . 43 , 615–621 10.1016/j.arcmed.2012.09.008 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fell J., Axmacher N. (2011). The role of phase synchronization in memory processes . Nat. Rev. Neurosci . 12 , 105–118 10.1038/nrn2979 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fitch W. T. (2013). Rhythmic cognition in humans and animals: distinguishing meter and pulse perception . Front. Syst. Neurosci . 7 : 68 10.3389/fnsys.2013.00068 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Forgeard M., Winner E., Norton A., Schlaug G. (2008). Practicing a musical instrument in childhood is associated with enhanced verbal ability and nonverbal reasoning . PLoS ONE 3 :e3566 10.1371/journal.pone.0003566 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Francois C., Chobert J., Besson M., Schon D. (2013). Music training for the development of speech segmentation . Cereb. Cortex . 23 , 2038–2043 10.1093/cercor/bhs180 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Frank M. J., Fossella J. A. (2010). Neurogenetics and pharmacology of learning, motivation, and cognition . Neuropsychopharmacology 36 , 133–152 10.1038/npp.2010.96 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fransson A. (1977). On qualitative differences in learning: iv—effects of intrinsic motivation and extrinsic test anxiety on process and outcome . Br. J. Educ. Psychol . 47 , 244–257 10.1111/j.2044-8279.1977.tb02353.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gander P. E., Bosnyak D. J., Roberts L. E. (2010). Acoustic experience but not attention modifies neural population phase expressed in human primary auditory cortex . Hear. Res . 269 , 81–94 10.1016/j.heares.2010.07.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gerry D., Unrau A., Trainor L. J. (2012). Active music classes in infancy enhance musical, communicative and social development . Dev. Sci . 15 , 398–407 10.1111/j.1467-7687.2012.01142.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gold B. P., Frank M. J., Bogert B., Brattico E. (2013). Pleasurable music affects reinforcement learning according to the listener . Front. Psychol . 4 : 541 10.3389/fpsyg.2013.00541 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gomez-Ramirez M., Kelly S. P., Molholm S., Sehatpour P., Schwartz T. H., Foxe J. J. (2011). Oscillatory sensory selection mechanisms during intersensory attention to rhythmic auditory and visual inputs: a human electrocorticographic investigation . J. Neurosci . 31 , 18556–18567 10.1523/JNEUROSCI.2164-11.2011 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goswami U. (2012). Entraining the brain: applications to language research and links to musical entrainment . Empir. Musicol. Rev . 7 , 57–63 [ Google Scholar ]
• Grahn J. A. (2012). Neural mechanisms of rhythm perception: current findings and future perspectives . Top. Cogn. Sci . 4 , 585–606 10.1111/j.1756-8765.2012.01213.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Green C., Bavelier D. (2008). Exercising your brain: a review of human brain plasticity and training-induced learning . Psychol. Aging 23 , 692 10.1037/a0014345 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Green C. S., Bavelier D. (2012). Learning, attentional control, and action video games . Curr. Biol . 22 , R197–R206 10.1016/j.cub.2012.02.012 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gregersen P., Kowalsky E., Kohn N., Marvin E. (2000). Early childhood music education and predisposition to absolute pitch: teasing apart genes and environment . Am. J. Med. Genet . 98 , 280–282 10.1002/1096-8628(20010122)98:3<280::AID-AJMG1083>3.0.CO;2-6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Haimson J., Swain D., Winner E. (2011). Do mathematicians have above average musical skill? Music Percept . 29 , 203–213 10.1525/mp.2011.29.2.203 [ CrossRef ] [ Google Scholar ]
• Hanna-Pladdy B., Mackay A. (2011). The relation between instrumental musical activity and cognitive aging . Neuropsychology 25 , 378–386 10.1037/a0021895 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hannon E. E., Trainor L. J. (2007). Music acquisition: effects of enculturation and formal training on development . Trends Cogn. Sci . 11 , 466–472 10.1016/j.tics.2007.08.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hannon E., Johnson S. (2005). Infants use meter to categorize rhythms and melodies: implications for musical structure learning . Cogn. Psychol . 50 , 354–377 10.1016/j.cogpsych.2004.09.003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hansen M., Wallentin M., Vuust P. (2012). Working memory and musical competence of musicians and non-musicians . Psychol. Music 41 , 779–793 10.1177/0305735612452186 [ CrossRef ] [ Google Scholar ]
• Hensch T. K. (2004). Critical period regulation . Annu. Rev. Neurosci . 27 , 549–579 10.1146/annurev.neuro.27.070203.144327 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Herdener M., Esposito F., Di Salle F., Boller C., Hilti C. C., Habermeyer B., et al. (2010). Musical training induces functional plasticity in human hippocampus . J. Neurosci . 30 , 1377–1384 10.1523/JNEUROSCI.4513-09.2010 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Herholz S. C., Zatorre R. J. (2012). Musical training as a framework for brain plasticity: behavior, function, and structure . Neuron 76 , 486–502 10.1016/j.neuron.2012.10.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hetland L. (2000). Learning to make music enhances spatial reasoning . J. Aesthet. Educ . 34 , 179–238 10.2307/3333643 [ CrossRef ] [ Google Scholar ]
• Ho Y., Cheung M., Chan A. S. (2003). Music training improves verbal but not visual memory: crosssectional and longitudinal explorations in children . Neuropsychology 17 , 439–450 10.1037/0894-4105.17.3.439 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Holm L., Ullén F., Madison G. (2011). Intelligence and temporal accuracy of behaviour: unique and shared associations with reaction time and motor timing . Exp. Brain Res . 214 , 175–183 10.1007/s00221-011-2817-6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hove M. J., Risen J. L. (2009). It's all in the timing: interpersonal synchrony increases affiliation . Soc. Cogn . 27 , 949–960 10.1521/soco.2009.27.6.949 [ CrossRef ] [ Google Scholar ]
• Howland J. G., Wang Y. T. (2008). Chapter 8 Synaptic plasticity in learning and memory: stress effects in the hippocampus . Prog. Brain Res . 169 , 145–158 10.1016/S0079-6123(07)00008-8 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Huss M., Verney J. P., Fosker T., Mead N., Goswami U. (2011). Music, rhythm, rise time perception and developmental dyslexia: perception of musical meter predicts reading and phonology . Cortex 47 , 674–689 10.1016/j.cortex.2010.07.010 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hyde K. L., Lerch J., Norton A., Forgeard M., Winner E., Evans A. C., et al. (2009). The neurosciences and music III: disorders and plasticity: the effects of musical training on structural brain development a longitudinal study . Ann. N.Y. Acad. Sci . 1169 , 182–186 10.1111/j.1749-6632.2009.04852.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Irvine S. H. (1998). Innate talents: a psychological tautology? Behav. Brain Sci . 21 , 419–419 10.1017/S0140525X98361233 [ CrossRef ] [ Google Scholar ]
• Jakobson L. S., Cuddy L. L., Kilgour A. R. (2003). Time tagging: a key to musicians' superior memory . Music Percept . 20 , 307–313 10.1525/mp.2003.20.3.307 [ CrossRef ] [ Google Scholar ]
• James C. E., Michel C. M., Britz J., Vuilleumier P., Hauert C. A. (2012). Rhythm evokes action: early processing of metric deviances in expressive music by experts and laymen revealed by ERP source imaging . Hum. Brain Mapp . 33 , 2751–2767 10.1002/hbm.21397 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jentschke S., Koelsch S. (2009). Musical training modulates the development of syntax processing in children . Neuroimage 47 , 735–744 10.1016/j.neuroimage.2009.04.090 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Joëls M., Pu Z., Wiegert O., Oitzl M. S., Krugers H. J. (2006). Learning under stress: how does it work? Trends Cogn. Sci . 10 , 152–158 10.1016/j.tics.2006.02.002 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jolles D., Crone E. A. (2012). Training the developing brain: a neurocognitive perspective . Front. Hum. Neurosci . 6 : 76 10.3389/fnhum.2012.00076 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jones M. R., Boltz M. (1989). Dynamic attending and responses to time . Psychol. Rev . 96 , 459–491 10.1037/0033-295X.96.3.459 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Juslin P. N., Liljeström S., Västfjäll D., Lundqvist L.-O. (2010). How does music evoke emotions? Exploring the underlying mechanisms , in Handbook of Music and Emotion: Theory, Research, Applications , eds Juslin P. N., Sloboda J. (Oxford: Oxford University Press; ), 605–642 [ Google Scholar ]
• Kenny D. T., Osborne M. S. (2006). Music performance anxiety: new insights from young musicians . Adv. Cogn. Psychol . 2 , 103–112 10.2478/v10053-008-0049-5 [ CrossRef ] [ Google Scholar ]
• Keshavan M. S., Diwadkar V. A., DeBellis M., Dick E., Kotwal R., Rosenberg D. R., et al. (2002). Development of the corpus callosum in childhood, adolescence and early adulthood . Life Sci . 70 , 1909–1922 10.1016/S0024-3205(02)01492-3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kinney H. C., Kloman A. S., Gilles F. H. (1988). Sequence of central nervous system myelination in human infancy. II. Patterns of myelination in autopsied infants . J. Neuropathol. Exp. Neurol . 47 , 217–234 10.1097/00005072-198805000-00003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kirschner S., Tomasello M. (2009). Joint drumming: social context facilitates synchronization in preschool children . J. Exp. Child Psychol . 102 , 299–314 10.1016/j.jecp.2008.07.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Knudsen E. I. (2004). Sensitive periods in the development of the brain and behavior . J. Cogn. Neurosci . 16 , 1412–1425 10.1162/0898929042304796 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Koelsch S. (2010). Towards a neural basis of music-evoked emotions . Trends Cogn. Sci . 14 , 131–137 10.1016/j.tics.2010.01.002 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Koelsch S., Fritz T., V., Cramon D. Y., Muller K., Friederici A. D. (2006). Investigating emotion with music: an fMRI study . Hum. Brain Mapp . 27 , 239–250 10.1002/hbm.20180 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kokal I., Engel A., Kirschner S., Keysers C. (2011). Synchronized drumming enhances activity in the caudate and facilitates prosocial commitment—if the rhythm comes easily . PLoS ONE 6 :e27272 10.1371/journal.pone.0027272 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kral A., Eggermont J. J. (2007). What's to lose and what's to learn: development under auditory deprivation, cochlear implants and limits of cortical plasticity . Brain Res. Rev . 56 , 259–269 10.1016/j.brainresrev.2007.07.021 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kral A., Sharma A. (2012). Developmental neuroplasticity after cochlear implantation . Trends Neurosci . 35 , 111–122 10.1016/j.tins.2011.09.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Krause V., Schnitzler A., Pollok B. (2010). Functional network interactions during sensorimotor synchronization in musicians and non-musicians . Neuroimage 52 , 245–251 10.1016/j.neuroimage.2010.03.081 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kuhl P. K. (2007). Is speech learning ‘gated’by the social brain? Dev. Sci . 10 , 110–120 10.1111/j.1467-7687.2007.00572.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kuhl P. K., Tsao F.-M., Liu H.-M. (2003). Foreign-language experience in infancy: effects of short-term exposure and social interaction on phonetic learning . Proc. Natl. Acad. Sci. U.S.A . 100 , 9096–9101 10.1073/pnas.1532872100 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lakatos P., Karmos G., Mehta A. D., Ulbert I., Schroeder C. E. (2008). Entrainment of neuronal oscillations as a mechanism of attentional selection . Science 320 , 110–113 10.1126/science.1154735 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Launay J., Dean R. T., Bailes F. (2013). Synchronization can influence trust following virtual interaction . Exp. Psychol . 60 , 53–63 10.1027/1618-3169/a000173 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lenroot R. K., Giedd J. N. (2006). Brain development in children and adolescents: insights from anatomical magnetic resonance imaging . Neurosci. Biobehav. Rev . 30 , 718–729 10.1016/j.neubiorev.2006.06.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lepine J. A., Lepine M. A., Jackson C. L. (2004). Challenge and hindrance stress: relationships with exhaustion, motivation to learn, and learning performance . J. Appl. Psychol . 89 , 883–891 10.1037/0021-9010.89.5.883 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Levitin D. J. (2012). What does it mean to be musical? Neuron 73 , 633–637 10.1016/j.neuron.2012.01.017 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Levitin D. J., Cole K., Chiles M., Lai Z., Lincoln A., Bellugi U. (2004). Characterizing the musical phenotype in individuals with williams syndrome . Child Neuropsychol . 10 , 223–247 10.1080/09297040490909288 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lima C. F., Castro S. L. (2011). Speaking to the trained ear: musical expertise enhances the recognition of emotions in speech prosody . Emotion 11 , 1021–1031 10.1037/a0024521 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lisman J., Grace A. (2005). The hippocampal-VTA loop: controlling the entry of information into long-term memory . Neuron 46 , 703–713 10.1016/j.neuron.2005.05.002 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• London J. (2004). Hearing in Time: Psychiological Aspects of Musical Meter . Oxford: Oxford University Press; 10.1093/acprof:oso/9780195160819.001.0001 [ CrossRef ] [ Google Scholar ]
• Loras H., Stensdotter A. K., Ohberg F., Sigmundsson H. (2013). Individual differences in motor timing and its relation to cognitive and fine motor skills . PLoS ONE 8 :e69353 10.1371/journal.pone.0069353 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Luo C., Guo Z.-W., Lai Y.-X., Liao W., Liu Q., Kendrick K. M., et al. (2012). Musical training induces functional plasticity in perceptual and motor networks: insights from resting-state FMRI . PLoS ONE 7 :e36568 10.1371/journal.pone.0036568 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Marshall L., Helgadóttir H., Mölle M., Born J. (2006). Boosting slow oscillations during sleep potentiates memory . Nature 444 , 610–613 10.1038/nature05278 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Merrett D. L., Peretz I., Wilson S. J. (2013). Moderating variables of music training-induced neuroplasticity: a review and discussion . Front. Psychol . 4 : 606 10.3389/fpsyg.2013.00606 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Milovanov R., Huotilainen M., Valimaki V., Esquef P. A., Tervaniemi M. (2008). Musical aptitude and second language pronunciation skills in school-aged children: neural and behavioral evidence . Brain Res . 1194 , 81–89 10.1016/j.brainres.2007.11.042 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moore J. K., Guan Y. L. (2001). Cytoarchitectural and axonal maturation in human auditory cortex . J. Assoc. Res. Otolaryngol . 2 , 297–311 10.1007/s101620010052 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moore J. K., Linthicum F. H., Jr. (2007). The human auditory system: a timeline of development . Int. J. Audiol . 46 , 460–478 10.1080/14992020701383019 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moreno S., Bialystok E., Barac R., Schellenberg E. G., Cepeda N. J., Chau T. (2011). Short-term music training enhances verbal intelligence and executive function . Psychol. Sci . 22 , 1425–1433 10.1177/0956797611416999 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moreno S., Bidelman G. M. (2013). Examining neural plasticity and cognitive benefit through the unique lens of musical training . Hear. Res . [Epub ahead of print]. 10.1016/j.heares.2013.09.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moreno S., Marques C., Santos A., Santos M., Castro S. L., Besson M. (2009). Musical training influences linguistic abilities in 8-year-old children: more evidence for brain plasticity . Cereb. Cortex 19 , 712–723 10.1093/cercor/bhn120 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Müller V., Lindenberger U. (2011). Cardiac and respiratory patterns synchronize between persons during choir singing . PLoS ONE 6 :e24893 10.1371/journal.pone.0024893 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Munte T., Altenmuller E., Jancke L. (2002). The musician's brain as a model of neuroplasticity . Nat. Rev. Neurosci . 3 , 473–478 10.1038/nrn843 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Murty V. P., Labar K. S., Hamilton D. A., Adcock R. A. (2011). Is all motivation good for learning? Dissociable influences of approach and avoidance motivation in declarative memory . Learn. Mem . 18 , 712–717 10.1101/lm.023549.111 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nagy Z., Westerberg H., Klingberg T. (2004). Maturation of white matter is associated with the development of cognitive functions during childhood . J. Cogn. Neurosci . 16 , 1227–1233 10.1162/0898929041920441 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nakahara H., Furuya S., Masuko T., Francis P. R., Kinoshita H. (2011). Performing music can induce greater modulation of emotion-related psychophysiological responses than listening to music . Int. J. Psychophysiol . 81 , 152–158 10.1016/j.ijpsycho.2011.06.003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ngo H. V., Martinetz T., Born J., Molle M. (2013). Auditory closed-loop stimulation of the sleep slow oscillation enhances memory . Neuron 78 , 545–553 10.1016/j.neuron.2013.03.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nobre A., Correa A., Coull J. (2007). The hazards of time . Curr. Opin. Neurobiol . 17 , 465–470 10.1016/j.conb.2007.07.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Norton A., Winner E., Cronin K., Overy K., Lee D. J., Schlaug G. (2005). Are there pre-existing neural, cognitive, or motoric markers for musical ability? Brain Cogn . 59 , 124–134 10.1016/j.bandc.2005.05.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nozaradan S., Peretz I., Missal M., Mouraux A. (2011). Tagging the neuronal entrainment to beat and meter . J. Neurosci . 31 , 10234–10240 10.1523/JNEUROSCI.0411-11.2011 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Oechslin M. S., Descloux C., Croquelois A., Chanal J., Van De Ville D., Lazeyras F., et al. (2013). Hippocampal volume predicts fluid intelligence in musically trained people . Hippocampus 23 , 552–558 10.1002/hipo.22120 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Overy K., Molnar-Szakacs I. (2009). Being together in time: musical experience and the mirror neuron system . Music Percept . 26 , 489–504 10.1525/mp.2009.26.5.489 [ CrossRef ] [ Google Scholar ]
• Pacchetti C., Mancini F., Aglieri R., Fundaro C., Martignoni E., Nappi G. (2000). Active music therapy in Parkinson's disease: an integrative method for motor and emotional rehabilitation . Psychosom. Med . 62 , 386–393 [ PubMed ] [ Google Scholar ]
• Parbery-Clark A., Skoe E., Lam C., Kraus N. (2009). Musician enhancement for speech-in-noise . Ear Hear . 30 , 653–661 10.1097/AUD.0b013e3181b412e9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Parbery-Clark A., Strait D. L., Kraus N. (2011). Context-dependent encoding in the auditory brainstem subserves enhanced speech-in-noise perception in musicians . Neuropsychologia 49 , 3338–3345 10.1016/j.neuropsychologia.2011.08.007 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Patel A. (2008). Music, Language, and the Brain . New York, NY: Oxford University Press [ Google Scholar ]
• Patel A. D. (2011). Why would musical training benefit the neural encoding of speech? The OPERA Hypothesis . Front. Psychol . 2 : 142 10.3389/fpsyg.2011.00142 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Patel A. D. (2013). Can nonlinguistic musical training change the way the brain processes speech? The expanded OPERA hypothesis . Hear. Res . [Epub ahead of print]. 10.1016/j.heares.2013.08.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Patel A. D., Iversen J. R. (2007). The linguistic benefits of musical abilities . Trends Cogn. Sci . 11 , 369–372 10.1016/j.tics.2007.08.003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Paus T., Zijdenbos A., Worsley K., Collins D. L., Blumenthal J., Giedd J. N., et al. (1999). Structural maturation of neural pathways in children and adolescents: in vivo study . Science 283 , 1908–1911 10.1126/science.283.5409.1908 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Penhune V. B. (2011). Sensitive periods in human development: evidence from musical training . Cortex 47 , 1126–1137 10.1016/j.cortex.2011.05.010 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Perani D., Saccuman M. C., Scifo P., Spada D., Andreolli G., Rovelli R., et al. (2010). Functional specializations for music processing in the human newborn brain . Proc. Natl. Acad. Sci. U.S.A . 107 , 4758–4763 10.1073/pnas.0909074107 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Peretz I., Zatorre R. (2005). Brain organization for music processing . Annu. Rev. Psychol . 56 , 89–114 10.1146/annurev.psych.56.091103.070225 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Petrides K. V., Niven L., Mouskounti T. (2006). The trait emotional intelligence of ballet dancers and musicians . Psicothema 18 ( Suppl. ), 101–107 [ PubMed ] [ Google Scholar ]
• Pitkala K., Savikko N., Poysti M., Strandberg T., Laakkonen M. L. (2013). Efficacy of physical exercise intervention on mobility and physical functioning in older people with dementia: a systematic review . Exp. Gerontol . 48 , 85–93 10.1016/j.exger.2012.08.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rauscher F. H., Robinson K. D., Jens J. J. (1998). Improved maze learning through early music exposure in rats . Neurol. Res . 20 , 427–432 [ PubMed ] [ Google Scholar ]
• Rauscher F. H., Shaw G. L., Ky K. N. (1993). Music and spatial task performance . Nature 365 , 611 10.1038/365611a0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Raven J. (1976). Coloured Progressive Matrices. Standard Progressive Matrices. Advanced Progressive Matrices . Oxford: Oxford Psychologists Press [ Google Scholar ]
• Raz N., Rodrigue K. M. (2006). Differential aging of the brain: patterns, cognitive correlates and modifiers . Neurosci. Biobehav. Rev . 30 , 730–748 10.1016/j.neubiorev.2006.07.001 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Repp B. H. (2005). Sensorimotor synchronization: a review of the tapping literature . Psychon. Bull. Rev . 12 , 969–992 10.3758/BF03206433 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Repp B. H., Su Y. H. (2013). Sensorimotor synchronization: a review of recent research (2006-2012) . Psychon. Bull. Rev . 20 , 403–452 10.3758/s13423-012-0371-2 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Reuben A., Brickman A. M., Muraskin J., Steffener J., Stern Y. (2011). Hippocampal atrophy relates to fluid intelligence decline in the elderly . J. Int. Neuropsychol. Soc . 17 , 56–61 10.1017/S135561771000127X [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rickard N. S. (2009). Defining the rhythmicity of memory-enhancing acoustic stimuli in the young domestic chick (Gallus gallus) . J. Comp. Psychol . 123 , 217–221 10.1037/a0015117 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rickard N. S., Toukhsati S. R., Field S. E. (2005). The effect of music on cognitive performance: insight from neurobiological and animal studies . Behav. Cogn. Neurosci. Rev . 4 , 235–261 10.1177/1534582305285869 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Robbins T. W. (2005). Chemistry of the mind: neurochemical modulation of prefrontal cortical function . J. Comp. Neurol . 493 , 140–146 10.1002/cne.20717 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Roden I., Grube D., Bongard S., Kreutz G. (2013). Does music training enhance working memory performance? Findings from a quasi-experimental longitudinal study . Psychol. Music . 10.1177/0305735612471239 [ CrossRef ] [ Google Scholar ]
• Rodriguez-Fornells A., Rojo N., Amengual J. L., Ripolles P., Altenmuller E., Munte T. F. (2012). The involvement of audio-motor coupling in the music-supported therapy applied to stroke patients . Ann. N.Y. Acad. Sci . 1252 , 282–293 10.1111/j.1749-6632.2011.06425.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rosenblum M., Pikovsky A. (2003). Synchronization: from pendulum clocks to chaotic lasers and chemical oscillators . Contemp. Phys . 44 , 401–416 10.1080/00107510310001603129 [ CrossRef ] [ Google Scholar ]
• Rossato J., Bevilaqua L., Izquierdo I., Medina J., Cammarota M. (2009). Dopamine controls persistence of long-term memory storage . Science 325 , 1017–1020 10.1126/science.1172545 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Salimpoor V. N., Benovoy M., Larcher K., Dagher A., Zatorre R. J. (2011). Anatomically distinct dopamine release during anticipation and experience of peak emotion to music . Nat. Neurosci . 14 , 257–262 10.1038/nn.2726 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Salimpoor V. N., van den Bosch I., Kovacevic N., McIntosh A. R., Dagher A., Zatorre R. J. (2013). Interactions between the nucleus accumbens and auditory cortices predict music reward value . Science 340 , 216–219 10.1126/science.1231059 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schabus M., Heib D. P., Lechinger J., Griessenberger H., Klimesch W., Pawlizki A., et al. (2013). Enhancing sleep quality and memory in insomnia using instrumental sensorimotor rhythm conditioning . Biol. Psychol . [Epub ahead of print]. 10.1016/j.biopsycho.2013.02.020 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schellenberg E. (2006). Long-term positive associations between music lessons and IQ . J. Educ. Psychol . 98 , 457–468 10.1037/0022-0663.98.2.457 [ CrossRef ] [ Google Scholar ]
• Schellenberg E. G. (2004). Music lessons enhance IQ . Psychol. Sci . 15 , 511–514 10.1111/j.0956-7976.2004.00711.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schellenberg E. G. (2011a). Examining the association between music lessons and intelligence . Br. J. Psychol . 102 , 283–302 10.1111/j.2044-8295.2010.02000.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schellenberg E. G. (2011b). Music lessons, emotional intelligence, and IQ . Music Percept . 29 , 185–194 10.1525/mp.2011.29.2.185 [ CrossRef ] [ Google Scholar ]
• Schellenberg E. G., Mankarious M. (2012). Music training and emotion comprehension in childhood . Emotion 12 , 887–891 10.1037/a0027971 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schlaug G., Norton A., Overy K., Winner E. (2005). Effects of music training on the child's brain and cognitive development . Ann. N.Y. Acad. Sci . 1060 , 219–230 10.1196/annals.1360.015 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schott B. H., Seidenbecher C. I., Fenker D. B., Lauer C. J., Bunzeck N., Bernstein H.-G., et al. (2006). The dopaminergic midbrain participates in human episodic memory formation: evidence from genetic imaging . J. Neurosci . 26 , 1407–1417 10.1523/JNEUROSCI.3463-05.2006 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schreckenberg G. M., Bird H. H. (1987). Neural plasticity of Mus musculus in response to disharmonic sound . Bull. N.J. Acad. Sci . 32 , 77–86 [ Google Scholar ]
• Schwabe L., Wolf O. T. (2010). Learning under stress impairs memory formation . Neurobiol. Learn. Mem . 93 , 183–188 10.1016/j.nlm.2009.09.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schwabe L., Wolf O. T., Oitzl M. S. (2010). Memory formation under stress: quantity and quality . Neurosci. Biobehav. Rev . 34 , 584–591 10.1016/j.neubiorev.2009.11.015 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schwartze M., Kotz S. A. (2013). A dual-pathway neural architecture for specific temporal prediction . Neurosci. Biobehav. Rev . 37 , 2587–2596 10.1016/j.neubiorev.2013.08.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sheppard L. D., Vernon P. A. (2008). Intelligence and speed of information-processing: a review of 50 years of research . Pers. Individ. Dif . 44 , 535–551 10.1016/j.paid.2007.09.015 [ CrossRef ] [ Google Scholar ]
• Skoe E., Kraus N. (2013). Musical training heightens auditory brainstem function during sensitive periods in development . Front. Psychol . 4 : 622 10.3389/fpsyg.2013.00622 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Slater J., Tierney A., Kraus N. (2013). At-risk elementary school children with one year of classroom music instruction are better at keeping a beat . PLoS ONE 8 :e77250 10.1371/journal.pone.0077250 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Slevc L. R., Miyake A. (2006). Individual differences in second-language proficiency: does musical ability matter? Psychol. Sci . 17 , 675–681 10.1111/j.1467-9280.2006.01765.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sloboda J. (1993). Musical ability . Ciba Found. Symp . 178 , 106–118 [ PubMed ] [ Google Scholar ]
• Sluming V., Barrick T., Howard M., Cezayirli E., Mayes A., Roberts N. (2002). Voxel-based morphometry reveals increased gray matter density in Broca's area in male symphony orchestra musicians . Neuroimage 17 , 1613–1622 10.1006/nimg.2002.1288 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Southgate D. E., Roscigno V. J. (2009). The impact of music on childhood and adolescent achievement * . Soc. Sci. Q . 90 , 4–21 10.1111/j.1540-6237.2009.00598.x [ CrossRef ] [ Google Scholar ]
• Sowell E. R., Thompson P. M., Leonard C. M., Welcome S. E., Kan E., Toga A. W. (2004). Longitudinal mapping of cortical thickness and brain growth in normal children . J. Neurosci . 24 , 8223–8231 10.1523/JNEUROSCI.1798-04.2004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Stoeber J., Eismann U. (2007). Perfectionism in young musicians: relations with motivation, effort, achievement, and distress . Pers. Individ. Dif . 43 , 2182–2192 10.1016/j.paid.2007.06.036 [ CrossRef ] [ Google Scholar ]
• Strait D., Kraus N. (2011). Playing music for a smarter ear: cognitive, perceptual and neurobiological evidence . Music Percept . 29 :133 10.1525/mp.2011.29.2.133 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Strait D. L., Kraus N. (2013). Biological impact of auditory expertise across the life span: musicians as a model of auditory learning . Hear. Res . [Epub ahead of print]. 10.1016/j.heares.2013.08.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Strait D. L., Kraus N., Skoe E., Ashley R. (2009). Musical experience and neural efficiency: effects of training on subcortical processing of vocal expressions of emotion . Eur. J. Neurosci . 29 , 661–668 10.1111/j.1460-9568.2009.06617.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Strait D. L., O'Connell S., Parbery-Clark A., Kraus N. (2013). Musicians' enhanced neural differentiation of speech sounds arises early in life: developmental evidence from ages 3 to 30 . Cereb. Cortex . [Epub ahead of print]. 10.1093/cercor/bht103 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Strait D. L., Parbery-Clark A., Hittner E., Kraus N. (2012). Musical training during early childhood enhances the neural encoding of speech in noise . Brain Lang . 123 , 191–201 10.1016/j.bandl.2012.09.001 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Studer R., Gomez P., Hildebrandt H., Arial M., Danuser B. (2011). Stage fright: its experience as a problem and coping with it . Int. Arch. Occup. Environ. Health 84 , 761–771 10.1007/s00420-010-0608-1 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Thaut M. H., Abiru M. (2010). Rhythmic auditory stimulation in rehabilitation of movement disorders: a review of current research . Music Percept . 27 , 263–269 10.1525/mp.2010.27.4.263 [ CrossRef ] [ Google Scholar ]
• Thaut M. H., McIntosh G. C., Rice R. R., Miller R. A., Rathbun J., Brault J. M. (1996). Rhythmic auditory stimulation in gait training for Parkinson's disease patients . Mov. Disord . 11 , 193–200 10.1002/mds.870110213 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Theusch E., Basu A., Gitschier J. (2009). Genome-wide study of families with absolute pitch reveals linkage to 8q24.21 and locus heterogeneity . Am. J. Hum. Genet . 85 , 112–119 10.1016/j.ajhg.2009.06.010 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Thut G., Miniussi C. (2009). New insights into rhythmic brain activity from TMS–EEG studies . Trends Cogn. Sci . 13 , 182–189 10.1016/j.tics.2009.01.004 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Thut G., Schyns P. G., Gross J. (2011). Entrainment of perceptually relevant brain oscillations by non-invasive rhythmic stimulation of the human brain . Front. Psychol . 2 : 170 10.3389/fpsyg.2011.00170 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tierney A. T., Kraus N. (2013). The ability to tap to a beat relates to cognitive, linguistic, and perceptual skills . Brain Lang . 124 , 225–231 10.1016/j.bandl.2012.12.014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tognoli E., Lagarde J., Deguzman G. C., Kelso J. A. S. (2007). The phi complex as a neuromarker of human social coordination . Proc. Natl. Acad. Sci. U.S.A . 104 , 8190–8195 10.1073/pnas.0611453104 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Toukhsati S. R., Rickard N. S. (2001). Exposure to a rhythmic auditory stimulus facilitates memory formation for the passive avoidance task in the day-old chick . J. Comp. Psychol . 115 , 132–139 10.1037/0735-7036.115.2.132 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Trainor L. J., Shahin A. J., Roberts L. E. (2009). Understanding the benefits of musical training . Ann. N.Y. Acad. Sci . 1169 , 133–142 10.1111/j.1749-6632.2009.04589.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Trehub S. E., Hannon E. E. (2006). Infant music perception: domain-general or domain-specific mechanisms? Cognition 100 , 73–99 10.1016/j.cognition.2005.11.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Trimmer C. G., Cuddy L. L. (2008). Emotional intelligence, not music training, predicts recognition of emotional speech prosody . Emotion 8 , 838–849 10.1037/a0014080 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Trombetti A., Hars M., Herrmann F. R., Kressig R. W., Ferrari S., Rizzoli R. (2011). Effect of music-based multitask training on gait, balance, and fall risk in elderly people: a randomized controlled trial . Arch. Intern. Med . 171 , 525–533 10.1001/archinternmed.2010.446 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Trost W., Ethofer T., Zentner M., Vuilleumier P. (2012). Mapping aesthetic musical emotions in the brain . Cereb. Cortex 22 , 2769–2783 10.1093/cercor/bhr353 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Trost W., Vuilleumier P. (2013). Rhythmic entrainment as a mechanism for emotion induction by music: a neurophysiological perspective , in The Emotional Power of Music: Multidisciplinary Perspectives on Musical Arousal, Expression, and Social Control , eds Cochrane T., Fantini B., Scherer K. R. (New York, NY: Oxford University Press; ), 213–225 10.1093/acprof:oso/9780199654888.003.0016 [ CrossRef ] [ Google Scholar ]
• Tzounopoulos T., Kraus N. (2009). Learning to encode timing: mechanisms of plasticity in the auditory brainstem . Neuron 62 , 463–469 10.1016/j.neuron.2009.05.002 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ukkola L. T., Onkamo P., Raijas P., Karma K., Ja I. (2009). Musical aptitude is associated with AVPR1A-haplotypes . PLoS One . 4 :e5534 10.1371/journal.pone.0005534 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Valdesolo P., Desteno D. (2011). Synchrony and the social tuning of compassion . Emotion 11 , 262–266 10.1037/a0021302 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Valdesolo P., Ouyang J., Desteno D. (2010). The rhythm of joint action: synchrony promotes cooperative ability . J. Exp. Soc. Psychol . 46 , 693–695 10.1016/j.jesp.2010.03.004 [ CrossRef ] [ Google Scholar ]
• Vaughn K. (2000). Music and mathematics: modest support for the oft-claimed relationship . J. Aesthet. Educ . 34 , 149–166 10.2307/3333641 [ CrossRef ] [ Google Scholar ]
• Veselka L., Schermer J. A., Petrides K., Vernon P. A. (2009). Evidence for a heritable general factor of personality in two studies . Twin Res. Hum. Genet . 12 , 254–260 10.1375/twin.12.3.254 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wächter T., Lungu O. V., Liu T., Willingham D. T., Ashe J. (2009). Differential effect of reward and punishment on procedural learning . J. Neurosci . 29 , 436–443 10.1523/JNEUROSCI.4132-08.2009 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wan C. Y., Schlaug G. (2010). Music making as a tool for promoting brain plasticity across the life span . Neuroscientist 16 , 566–577 10.1177/1073858410377805 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Weber-Fox C., Neville H. J. (2001). Sensitive periods differentiate processing of open- and closed-class words: an ERP study of bilinguals . J. Speech Lang. Hear. Res . 44 , 1338–1353 10.1044/1092-4388(2001/104) [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Weinberger N. M. (2004). Specific long-term memory traces in primary auditory cortex . Nat. Rev. Neurosci . 5 , 279–290 10.1038/nrn1366 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wiltermuth S. S., Heath C. (2009). Synchrony and cooperation . Psychol. Sci . 20 , 1–5 10.1111/j.1467-9280.2008.02253.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wimber M., Schott B. H., Wendler F., Seidenbecher C. I., Behnisch G., Macharadze T., et al. (2011). Prefrontal dopamine and the dynamic control of human long-term memory . Transl. Psychiatry 1 :e15 10.1038/tp.2011.15 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wong P. C. M., Skoe E., Russo N. M., Dees T., Kraus N. (2007). Musical experience shapes human brainstem encoding of linguistic pitch patterns . Nat. Neurosci . 10 , 420–422 10.1038/nn1872 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Young L. N., Cordes S., Winner E. (2013). Arts involvement predicts academic achievement only when the child has a musical instrument . Educ. Psychol . 1–13 10.1080/01443410.2013.785477 [ CrossRef ] [ Google Scholar ]
• Zatorre R. J., Chen J. L., Penhune V. B. (2007). When the brain plays music: auditory-motor interactions in music perception and production . Nat. Rev. Neurosci . 8 , 547–558 10.1038/nrn2152 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zatorre R. J., Salimpoor V. N. (2013). From perception to pleasure: music and its neural substrates . Proc. Natl. Acad. Sci. U.S.A . 110 ( Suppl. ), 10430–10437 10.1073/pnas.1301228110 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

September 12, 2024

Introducing OpenAI o1-preview

A new series of reasoning models for solving hard problems. Available starting 9.12

We've developed a new series of AI models designed to spend more time thinking before they respond. They can reason through complex tasks and solve harder problems than previous models in science, coding, and math. Today, we are releasing the first of this series in ChatGPT and our API. This is a preview and we expect regular updates and improvements. Alongside this release, we’re also including evaluations for the next update, currently in development.

How it works

We trained these models to spend more time thinking through problems before they respond, much like a person would. Through training, they learn to refine their thinking process, try different strategies, and recognize their mistakes. 

In our tests, the next model update performs similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology. We also found that it excels in math and coding. In a qualifying exam for the International Mathematics Olympiad (IMO), GPT-4o correctly solved only 13% of problems, while the reasoning model scored 83%. Their coding abilities were evaluated in contests and reached the 89th percentile in Codeforces competitions. You can read more about this in our technical research post .

As an early model, it doesn't yet have many of the features that make ChatGPT useful, like browsing the web for information and uploading files and images. For many common cases GPT-4o will be more capable in the near term.

But for complex reasoning tasks this is a significant advancement and represents a new level of AI capability. Given this, we are resetting the counter back to 1 and naming this series OpenAI o1.

As part of developing these new models, we have come up with a new safety training approach that harnesses their reasoning capabilities to make them adhere to safety and alignment guidelines. By being able to reason about our safety rules in context, it can apply them more effectively. 

One way we measure safety is by testing how well our model continues to follow its safety rules if a user tries to bypass them (known as "jailbreaking"). On one of our hardest jailbreaking tests, GPT-4o scored 22 (on a scale of 0-100) while our o1-preview model scored 84. You can read more about this in the system card and our research post .

To match the new capabilities of these models, we’ve bolstered our safety work, internal governance, and federal government collaboration. This includes rigorous testing and evaluations using our Preparedness Framework (opens in a new window) , best-in-class red teaming, and board-level review processes, including by our Safety & Security Committee. To advance our commitment to AI safety, we recently formalized agreements with the U.S. and U.K. AI Safety Institutes. We've begun operationalizing these agreements, including granting the institutes early access to a research version of this model. This was an important first step in our partnership, helping to establish a process for research, evaluation, and testing of future models prior to and following their public release.

Whom it’s for

These enhanced reasoning capabilities may be particularly useful if you’re tackling complex problems in science, coding, math, and similar fields. For example, o1 can be used by healthcare researchers to annotate cell sequencing data, by physicists to generate complicated mathematical formulas needed for quantum optics, and by developers in all fields to build and execute multi-step workflows. 

OpenAI o1-mini

The o1 series excels at accurately generating and debugging complex code. To offer a more efficient solution for developers, we’re also releasing OpenAI o1-mini , a faster, cheaper reasoning model that is particularly effective at coding. As a smaller model, o1-mini is 80% cheaper than o1-preview, making it a powerful, cost-effective model for applications that require reasoning but not broad world knowledge. 

How to use OpenAI o1

ChatGPT Plus and Team users will be able to access o1 models in ChatGPT starting today. Both o1-preview and o1-mini can be selected manually in the model picker, and at launch, weekly rate limits will be 30 messages for o1-preview and 50 for o1-mini. We are working to increase those rates and enable ChatGPT to automatically choose the right model for a given prompt.

ChatGPT Enterprise and Edu users will get access to both models beginning next week.  Developers who qualify for API usage tier 5 (opens in a new window) can start prototyping with both models in the API today with a rate limit of 20 RPM. We’re working to increase these limits after additional testing. The API for these models currently doesn't include function calling, streaming, support for system messages, and other features. To get started, check out the API documentation (opens in a new window) .

We also are planning to bring o1-mini access to all ChatGPT Free users . 

What’s next

This is an early preview of these reasoning models in ChatGPT and the API. In addition to model updates, we expect to add browsing, file and image uploading, and other features to make them more useful to everyone. 

We also plan to continue developing and releasing models in our GPT series, in addition to the new OpenAI o1 series. 

• Try it in ChatGPT Plus (opens in a new window)
• Try it in the API (opens in a new window)