hypothesis of learning styles

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Am J Pharm Educ
v.73(1); 2009 Feb 19

Learning Styles: A Review of Theory, Application, and Best Practices

Much pedagogical research has focused on the concept of “learning styles.” Several authors have proposed that the ability to typify student learning styles can augment the educational experience. As such, instructors might tailor their teaching style so that it is more congruent with a given student's or class of students' learning style. Others have argued that a learning/teaching style mismatch encourages and challenges students to expand their academic capabilities. Best practice might involve offering courses that employ a variety of teaching styles. Several scales are available for the standardization of learning styles. These scales employ a variety of learning style descriptors and are sometimes criticized as being measures of personality rather than learning style. Learning styles may become an increasingly relevant pedagogic concept as classes increase in size and diversity. This review will describe various learning style instruments as well as their potential use and limitations. Also discussed is the use of learning style theory in various concentrations including pharmacy.

INTRODUCTION

The diversity of students engaged in higher education continues to expand. Students come to colleges with varied ethnic and cultural backgrounds, from a multitude of training programs and institutions, and with differing learning styles. 1 Coupled with this increase in diversification has been a growth in distance education programs and expansions in the types of instructional media used to deliver information. 2 , 3 These changes and advances in technology have led many educators to reconsider traditional, uniform instruction methods and stress the importance of considering student learning styles in the design and delivery of course content. 4 - 5 Mismatches between an instructor's style of teaching and a student's method of learning have been cited as potential learning obstacles within the classroom and as a reason for using a variety of teaching modalities to deliver instruction. 6 - 8 The concept of using a menu of teaching modalities is based on the premise that at least some content will be presented in a manner suited to every type of learner within a given classroom or course. Some research has focused on profiling learning types so that instructors have a better understanding of the cohort of students they are educating. 7 - 8 This information can be used to guide the selection of instruction modalities employed in the classroom. Limited research has also focused on describing and characterizing composite learning styles and patterns for students in various concentrations of study (eg, medicine, engineering). 5 , 6 , 9 This review will describe the potential utility and limitations in assessing learning styles.

LEARNING STYLES

A benchmark definition of “learning styles” is “characteristic cognitive, effective, and psychosocial behaviors that serve as relatively stable indicators of how learners perceive, interact with, and respond to the learning environment. 10 Learning styles are considered by many to be one factor of success in higher education. Confounding research and, in many instances, application of learning style theory has begat the myriad of methods used to categorize learning styles. No single commonly accepted method currently exists, but alternatively several potential scales and classifications are in use. Most of these scales and classifications are more similar than dissimilar and focus on environmental preferences, sensory modalities, personality types, and/or cognitive styles. 11 Lack of a conceptual framework for both learning style theory and measurement is a common and central criticism in this area. In 2004 the United Kingdom Learning and Skills Research Center commissioned a report intended to systematically examine existing learning style models and instruments. In the commission report, Coffield et al identified several inconsistencies in learning style models and instruments and cautioned educators with regards to their use. 12 The authors also outlined a suggested research agenda for this area.

Alternatively, many researchers have argued that knowledge of learning styles can be of use to both educators and students. Faculty members with knowledge of learning styles can tailor pedagogy so that it best coincides with learning styles exhibited by the majority of students. 4 Alternatively, students with knowledge of their own preferences are empowered to use various techniques to enhance learning, which in turn may impact overall educational satisfaction. This ability is particularly critical and useful when an instructor's teaching style does not match a student's learning style. Compounding the issue of learning styles in the classroom has been the movement in many collegiate environments to distance and/or asynchronous education. 2 , 3 This shift in educational modality is inconsistent with the learning models with which most older students and adult learners are accustomed from their primary and high school education. 3 , 13 , 14 Alternatively, environmental influences and more widespread availability of technological advances (eg, personal digital assistants, digital video, the World Wide Web, wireless Internet) may make younger generations of students more comfortable with distance learning. 15 - 17

LEARNING STYLES INSTRUMENTS

As previously stated, several models and measures of learning styles have been described in the literature. Kolb proposed a model involving a 4-stage cyclic structure that begins with a concrete experience, which lends to a reflective observation and subsequently an abstract conceptualization that allows for active experimentation. 18 Kolb's model is associated with the Learning Style Inventory instrument (LSI). The LSI focuses on learner's preferences in terms of concrete versus abstract, and action versus reflection. Learners are subsequently described as divergers, convergers, assimilators, or accommodators.

Honey and Mumford developed an alternative instrument known as the Learning Style Questionnaire (LSQ). 6 Presumably, the LSQ has improved validity and predictive accuracy compared to the LSI. The LSQ describes 4 distinct types of learners: activists (learn primarily by experience), reflectors (learn from reflective observation), theorists (learn from exploring associations and interrelationships), and pragmatics (learn from doing or trying things with practical outcomes). The LSQ has been more widely used and studied in management and business settings and its applicability to academia has been questioned. 6 An alternative to the LSQ, the Canfield Learning Style Inventory (CLSI) describes learning styles along 4 dimensions. 19 These dimensions include conditions for learning, area of interest, mode of learning, and conditions for performance. Analogous to the LSQ, applicability of the CLSI to academic settings has been questioned. Additionally, some confusion surrounding scoring and interpretation of certain result values also exists.

Felder and Silverman introduced a learning style assessment instrument that was specifically designed for classroom use and was first applied in the context of engineering education. 20 The instrument consists of 44 short items with a choice between 2 responses to each sentence. Learners are categorized in 4 dichotomous areas: preference in terms of type and mode of information perception (sensory or intuitive; visual or verbal), approaches to organizing and processing information (active or reflective), and the rate at which students progress towards understanding (sequential or global). The instrument associated with the model is known as the Index of Learning Survey (ILS). 21 The ILS is based on a 44-item questionnaire and outputs a preference profile for a student or an entire class. The preference profile is based on the 4 previously defined learning dimensions. The ILS has several advantages over other instruments including conciseness and ease of administration (in both a written and computerized format). 20 , 21 No published data exist with regards to the use of the ILS in populations of pharmacy students or pharmacists. Cook described a study designed to examine the reliability of the ILS for determining learning styles among a population of internal medicine residents. 20 The researchers administered the ILS twice and the Learning Style Type Indicator (LSTI) once to 138 residents (86 men, 52 women). The LSTI has been previously compared to the ILS by several investigators. 8 , 19 Cook found that the Cronbach's alpha scores for the ILS and LSTI ranged from 0.19 to 0.69. They preliminarily concluded that the ILS scores were reliable and valid among this cohort of residents, particularly within the active-reflective and sensing-intuitive domains. In a separate study, Cook et al attempted to evaluate convergence and discrimination among the ILS, LSI, and another computer-based instrument known as the Cognitive Styles Analysis (CSA). 11 The cohort studied consisted of family medicine and internal medicine residents as well as first- and third-year medical students. Eighty-nine participants completed all 3 instruments, and responses were analyzed using calculated Pearson's r and Cronbach's α. The authors found that the ILS active-reflective and sensing-intuitive scores as well as the LSI active-reflective scores were valid in determining learning styles. However, the ILS sequential-global domain failed to correlate well with other instruments and may be flawed, at least in this given population. The authors advised the use of caution when interpreting scores without a strong knowledge of construct definitions and empirical evidence.

Several other instruments designed to measure personality indexes or psychological types may overlap and describe learning styles in nonspecific fashions. One example of such an indicator is the Myers-Briggs Index. 6 While some relation between personality indexes and learning styles may exist, the use of instruments intended to describe personality to characterize learning style has been criticized by several authors. Therefore, the use of these markers to measure learning styles is not recommended. 6 The concept of emotional intelligence is another popular way to characterize intellect and learning capacity but similarly should not be misconstrued as an effective means of describing learning styles. 23

Several authors have proposed correlations between culture and learning styles. 6 , 24 This is predicated on the concept that culture influences environmental perceptions which, in turn, to some degree determine the way in which information is processed and organized. The storage, processing, and assimilation methods for information contribute to how new knowledge is learned. Culture also plays a role in conditioning and reinforcing learning styles and partially explains why teaching methods used in certain parts of the world may be ineffective or less effective when blindly transplanted to another locale. 6 , 24 Teachers should be aware of this phenomenon and the influence it has on the variety of learning styles that are present in classrooms. This is especially true in classrooms that have a large contingency of international students. Such classrooms are becoming increasingly common as more and more schools expand their internationalization efforts. 25

The technological age may also be influencing the learning styles of younger students and emerging generations of learners. The Millennial Generation has been described as more technologically advanced than their Generation X counterparts, with higher expectations for the use of computer-aided media in the classroom. 15 , 16 , 26 Younger students are accustomed to enhanced visual images associated with various computer- and television-based games and game systems. 16 , 26 Additionally, video technology is increasingly becoming “transportable” in the way of mobile computing, MP3 devices, personal digital video players, and other technologies. 26 All of these advances have made visual images more pervasive and common within industrialized nations.

APPLYING LEARNING STYLES TO THE CLASSROOM

As class sizes increase, so do the types and numbers of student learning styles. Also, as previously mentioned, internationalization and changes in the media culture may affect the spectrum of classroom learning styles as well. 24 , 25 Given the variability in learning styles that may exist in a classroom, some authors suggested that students should adapt their learning styles to coincide with a given instruction style. 6 , 27 This allows instructors to dictate the methods used to instruct in the classroom. This approach also allows instructors to “teach from their strengths,” with little consideration to other external factors such as learning style of students. While convenient, this unilateral approach has been criticized for placing all of the responsibility for aligning teaching and learning on the student. When the majority of information is presented in formats that are misaligned with learning styles, students may spend more time manipulating material than they do in comprehending and applying the information. Additionally, a unilaterally designed classroom may reinforce a “do nothing” approach among faculty members. 6 Alternatively, a teaching style-learning style mismatch might challenge students to adjust, grow intellectually, and learn in more integrated ways. However, it may be difficult to predict which students have the baseline capacity to adjust, particularly when significant gaps in knowledge of a given subject already exist or when the learner is a novice to the topic being instructed. 6 , 27 This might be especially challenging within professional curricula where course load expectations are significant.

Best practice most likely involves a teaching paradigm which addresses and accommodates multiple dimensions of learning styles that build self-efficacy. 27 Instructing in a way that encompasses multiple learning styles gives the teacher an opportunity to reach a greater extent of a given class, while also challenging students to expand their range of learning styles and aptitudes at a slower pace. This may avoid lost learning opportunities and circumvent unnecessary frustration from both the teacher and student. For many instructors, multi-style teaching is their inherent approach to learning, while other instructors more commonly employ unilateral styles. Learning might be better facilitated if instructors were cognizant of both their teaching styles and the learning styles of their students. An understanding and appreciation of a given individual's teaching style requires self-reflection and introspection and should be a component of a well-maintained teaching portfolio. Major changes or modifications to teaching styles might not be necessary in order to effectively create a classroom atmosphere that addresses multiple learning styles or targets individual ones. However, faculty members should be cautious to not over ambitiously, arbitrarily, or frivolously design courses and activities with an array of teaching modalities that are not carefully connected, orchestrated, and delivered.

Novice learners will likely be more successful when classrooms, either by design or by chance, are tailored to their learning style. However, the ultimate goal is to instill within students the skills to recognize and react to various styles so that learning is maximized no matter what the environment. 28 This is an essential skill for an independent learner and for students in any career path.

Particular consideration of learning styles might be given to asynchronous courses and other courses where a significant portion of time is spent online. 29 As technology advances and classroom sizes in many institutions become increasingly large, asynchronous instruction is becoming more pervasive. In many instances, students who have grown accustomed to technological advances may prefer asynchronous courses. Online platforms may inherently affect learning on a single dimension (visual or auditory). Most researchers who have compared the learning styles of students enrolled in online versus traditional courses have found no correlations between the learning styles and learning outcomes of cohorts enrolled in either course type. Johnson et al compared learning style profiles to student satisfaction with either online or face-to-face study groups. 30 Forty-eight college students participated in the analysis. Learning styles were measured using the ILS. Students were surveyed with regard to their satisfaction with various study group formats. These results were then correlated to actual performance on course examinations. Active and visual learners demonstrated a significant preference for face-to-face study groups. Alternatively, students who were reflective learners demonstrated a preference for online groups. Likely due to the small sample size, none of these differences achieved statistical significance. The authors suggest that these results are evidence for courses employing hybrid teaching styles that reach as many different students as possible. Cook et al studied 121 internal medicine residents and also found no association (p > 0.05) between ILS-measured learning styles and preferences for learning formats (eg, Web-based versus paper-based learning modules). 31 Scores on assessment questions related to learning modules administered to the residents were also not statistically correlated with learning styles.

Cook et al examined the effectiveness of adapting Web-based learning modules to a given learner's style. 32 The investigators created 2 versions of a Web-based instructional module on complementary and alternative medications. One version of the modules directed the learner to “active” questions that provided learners immediate and comprehensive feedback, while the other version involved “reflective” questions that directed learners back to the case content for answers. Eighty-nine residents were randomly matched or mismatched based on their active-reflective learning styles (as determined by ILS) to either the “active” or “reflective” test version. Posttest scores for either question type among mismatched subjects did not differ significantly ( p = 0.97), suggesting no interaction between learning styles and question types. The authors concluded from this small study that learning styles had no influence on learning outcomes. The study was limited in its lack of assessment of baseline knowledge, motivation, or other characteristics. Also, the difficulty of the assessment may not have been sufficient enough to distinguish a difference and/or “mismatched” learners may have automatically adapted to the information they received regardless of type.

STUDIES OF PHARMACY STUDENTS

There are no published studies that have systematically examined the learning styles of pharmacy students. Pungente et al collected some learning styles data as part of a study designed to evaluate how first-year pharmacy students' learning styles influenced preferences toward different activities associated with problem-based learning (PBL). 33 One hundred sixteen first-year students completed Kolb's LSI. Learning styles were then matched to responses from a survey designed to assess student preferences towards various aspects of PBL. The majority of students were classified by the LSI as being accommodators (36.2%), with a fairly even distribution of styles among remaining students (19.8% assimilators, 22.4% convergers, 21.6% divergers). There was a proportional distribution of learning styles among a convenience sample of pharmacy students. Divergers were the least satisfied with the PBL method of instruction, while convergers demonstrated the strongest preference for this method of learning. The investigators proposed that the next step might be to correlate learning styles and PBL preferences with actual academic success.

Limited research correlating learning styles to learning outcomes has hampered the application of learning style theory to actual classroom settings. Complicating research is the plethora of different learning style measurement instruments available. Despite these obstacles, efforts to better define and utilize learning style theory is an area of growing research. A better knowledge and understanding of learning styles may become increasingly critical as classroom sizes increase and as technological advances continue to mold the types of students entering higher education. While research in this area continues to grow, faculty members should make concentrated efforts to teach in a multi-style fashion that both reaches the greatest extent of students in a given class and challenges all students to grow as learners.

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Learning Styles: Concepts and Evidence

Affiliations.

1 University of California, San Diego [email protected].
2 Washington University in St. Louis.
3 University of South Florida.
4 University of California, Los Angeles.
PMID: 26162104
DOI: 10.1111/j.1539-6053.2009.01038.x

The term "learning styles" refers to the concept that individuals differ in regard to what mode of instruction or study is most effective for them. Proponents of learning-style assessment contend that optimal instruction requires diagnosing individuals' learning style and tailoring instruction accordingly. Assessments of learning style typically ask people to evaluate what sort of information presentation they prefer (e.g., words versus pictures versus speech) and/or what kind of mental activity they find most engaging or congenial (e.g., analysis versus listening), although assessment instruments are extremely diverse. The most common-but not the only-hypothesis about the instructional relevance of learning styles is the meshing hypothesis, according to which instruction is best provided in a format that matches the preferences of the learner (e.g., for a "visual learner," emphasizing visual presentation of information). The learning-styles view has acquired great influence within the education field, and is frequently encountered at levels ranging from kindergarten to graduate school. There is a thriving industry devoted to publishing learning-styles tests and guidebooks for teachers, and many organizations offer professional development workshops for teachers and educators built around the concept of learning styles. The authors of the present review were charged with determining whether these practices are supported by scientific evidence. We concluded that any credible validation of learning-styles-based instruction requires robust documentation of a very particular type of experimental finding with several necessary criteria. First, students must be divided into groups on the basis of their learning styles, and then students from each group must be randomly assigned to receive one of multiple instructional methods. Next, students must then sit for a final test that is the same for all students. Finally, in order to demonstrate that optimal learning requires that students receive instruction tailored to their putative learning style, the experiment must reveal a specific type of interaction between learning style and instructional method: Students with one learning style achieve the best educational outcome when given an instructional method that differs from the instructional method producing the best outcome for students with a different learning style. In other words, the instructional method that proves most effective for students with one learning style is not the most effective method for students with a different learning style. Our review of the literature disclosed ample evidence that children and adults will, if asked, express preferences about how they prefer information to be presented to them. There is also plentiful evidence arguing that people differ in the degree to which they have some fairly specific aptitudes for different kinds of thinking and for processing different types of information. However, we found virtually no evidence for the interaction pattern mentioned above, which was judged to be a precondition for validating the educational applications of learning styles. Although the literature on learning styles is enormous, very few studies have even used an experimental methodology capable of testing the validity of learning styles applied to education. Moreover, of those that did use an appropriate method, several found results that flatly contradict the popular meshing hypothesis. We conclude therefore, that at present, there is no adequate evidence base to justify incorporating learning-styles assessments into general educational practice. Thus, limited education resources would better be devoted to adopting other educational practices that have a strong evidence base, of which there are an increasing number. However, given the lack of methodologically sound studies of learning styles, it would be an error to conclude that all possible versions of learning styles have been tested and found wanting; many have simply not been tested at all. Further research on the use of learning-styles assessment in instruction may in some cases be warranted, but such research needs to be performed appropriately.

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Kolb’s Experiential Learning Theory & Learning Styles

The humanistic and constructivist approaches to education, which emphasize that learning occurs naturally, include David Kolb’s Theory of Experiential Learning. Kolb proposed that experience was critical in the development of knowledge construction, as learning occurs through discovery and active participation. Kolb defined leaning as “the process whereby knowledge is created through the transformation of experience” (Kolb, 1984).

There are two parts to Kolb’s Experiential Learning Theory. The first is that learning follows a four-stage cycle, as outlined below. Kolb believed that, ideally, learners progressed through the stages to complete a cycle, and, as a result, transformed their experiences into knowledge. The second part to Kolb’s Theory focused on learning styles, or the cognitive processes that occurred in order for acquire knowledge. Essentially, Kolb believed that individuals could demonstrate their knowledge, or the learning that occurred, when they were able to apply abstract concepts to new situations.

Completion of all stages of the cycle allows the transformation of experience to knowledge to occur. Kolb’s entire theory is based on this idea of converting experience into knowledge. With each new experience, the learner is able to integrate new observations with their current understanding. Ideally, learners should have the opportunity to pass through each stage.

Experiences are central to Kolb’s theory, as he viewed it as a process by which something must be changed or transformed. Memorization or recollection of ideas taught does not equal learning, as no value has been added to the learner. Kolb’s model acknowledges that something must be generated from the experience in order for it to be defined as learning.

See also: Robert Gagné’s Taxonomy of Learning

Kolb’s Four Stages of Learning:

Kolb’s Learning Cycle is based on the Jean Piaget ’s focus on the fact that learners create knowledge through interactions with the environment. See also: Social Learning Theory: Albert Bandura

1. Concrete Experience:

Kolb’s learning process cycle begins with a concrete experience. This can either be a completely new experience or a reimagined experience that already happened. In a concrete experience, each learner engages in an activity or task. Kolb believed that the key to learning is involvement. It is not enough for learners to just read about it or watch it in action. In order to acquire new knowledge, learners must actively engage in the task.

2. Reflective Observation:

After engaging in the concrete experience, the learner steps back to reflect on the task. This stage in the learning cycle allows the learner to ask questions and discuss the experience with others. Communication at this stage is vital, as it allows the learner to identify any discrepancies between their understanding and the experience itself. Good vocabulary also allows a solid review of the events that occurred.

3. Abstract Conceptualization:

The next step in the learning cycle is to make sense of these events. The learner attempts to draw conclusions of the experience by reflecting on their prior knowledge, using ideas with which they are familiar or discussing possible theories with peers. The learner moves from reflective observation to abstract conceptualization when they begin to classify concepts and form conclusions on the events that occurred. This involves interpreting the experience and making comparisons to their current understanding on the concept. Concepts need not be “new”; learners can analyze new information and modify their conclusions on already existing ideas.

See also: Stages of Moral Development – Lawrence Kohlberg

4. Active Experimentation:

This stage in the cycle is the testing stage. Learners return to participating in a task, this time with the goal of applying their conclusions to new experiences. They are able to make predictions, analyze tasks, and make plans for the acquired knowledge in the future. By allowing learners to put their knowledge into practice and showing how it is relevant to their lives, you are ensuring that the information is retained in the future.

As Kolb’s learning theory is cyclical, one can enter the process at any stage in the cycle. However, the cycle should then be completed in entirety to ensure that effective learning has taken place. Each stage is dependent on the others and all must be completed to develop new knowledge.

Although the stages work together to create a learning process, some individuals may prefer some components over others. While one may depend heavily on concrete and reflective experiences, they may choose to spend less time on the abstract and active stages. Because of this, Roger Fry worked with Kolb to identify four unique learning styles:

Kolb’s Learning Styles

Kolb's learning cycle and experiential learning styles.

1. Diverging (concrete experience/reflective observation)

This learning style takes an original and creative approach. Rather than examining concrete experiences by the actions taken, individuals tend to assess them from various perspectives. They value feelings and take an interest in others. Individuals who prefer this learning style tend to enjoy tasks such as brainstorming ideas and working collaboratively in groups.

There are a few instructional techniques that Divergers prefer:

Hands-on activities and the opportunity to explore
Classic teacher-class lecture that highlights how to use a system as well as its strengths and weaknesses.

2. Assimilating (abstract conceptualization/reflective observation)

This learning style emphasizes reasoning. Individuals who demonstrate this learning style are able to review the facts and assess the experience as a whole. They tend to enjoy designing experiments and working on projects from start to completion.

There are a few instructional techniques that Assimilators prefer:

Independent, prepared exercises that the learner can complete without the instructor
Classic teacher-class lecture supported by an audio or video presentation
Private exploration or demonstration that follows a tutorial, with answers provided.

See also: Lev Vygotsky – Sociocultural Theory of Cognitive Development

3. Converging (abstract conceptualization/active experimentation)

This learning style highlights problem solving as an approach to learning. Individuals who prefer this learning style are able to make decisions and apply their ideas to new experiences. Unlike Divergers, they tend to avoid people and perceptions, choosing instead to find technical solutions.

There are a few instructional techniques that Convergers prefer:

Workbooks or worksheets that provide problems sets
Tasks that are computer-based
Interactive activities.

4. Accommodating (concrete experience/active experimentation)

This learning style is adaptable and intuitive. These individuals use trial and error to guide their experiences, preferring to discover the answers for themselves. They are able to alter their path based on the circumstance and generally have good people skills.

There are a few instructional techniques that Accommodators prefer:

Activities that allow them to be actively engaged
Exploration and instructor support for deeper questioning, such as “ what if? ” or “ why not? ”
Tasks that promote independent discovery.

See also: Andragogy Theory – Malcolm Knowles

Application

Generally, teachers are able to identify learning styles by observing their students in the classroom. Students begin to show their preference for particular styles through presentations, discussions, and collaborative activities. When delivering courses online, it is important for the instructor to engage with the students throughout the entire learning cycle in order to reveal their preferences. As a rule, best teaching practices always include a wide range of learning activities in order to reach all learning styles. A variety of experiences supports all learners regardless of preferred style, as it helps them develop skills in specific areas and creates a more flexible, well-rounded learner.

Kolb’s theory of experiential learning includes learning as a whole process. All stages can be included throughout the experiences. For example, a classic teacher-student lecture may be both a concrete and an abstract experience, based on how the learner interacts with it. This also means that the learner could view strong and emotional reflection as a concrete experience, or completing a computer-based task as an abstract experience. Additionally, a learner may develop their own abstract model to better understand a concrete experience or task. It is important not to limit learning experiences to the stage that you perceive them to be.

See also: Howard Gardner’s Theory of Multiple Intelligences

References:

Kolb, D.A. (1984). Experiential learning: experience as the source of learning and development. Englewood Cliffs, NJ: Prentice Hall.

I am a professor of Educational Technology. I have worked at several elite universities. I hold a PhD degree from the University of Illinois and a master's degree from Purdue University.

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Learning Styles: Fact or Fiction? What This Decades-Old Theory Can Teach Us

By andy minshew.

October 30, 2019

Do you know whether you’re a visual, auditory, reading/writing, or kinaesthetic learner? What about your students? If you’ve been in the educational field long enough, you’ve probably come across Fleming’s learning styles theory. For over forty years, researchers have sought to classify how different students learn and what teachers can do to support them.

While many teachers feel that taking a student’s learning style into account can help them succeed, others feel that this theory is about as scientific as a horoscope.[2] Whether the evidence points for or against learning styles, however, this theory can remind us that some students may need individualized instruction or resources to best help them learn.

In this article, we’ll provide an overview of learning styles and a fair presentation of the research for and against the theory. Then, we’ll discuss how to best incorporate what the learning styles theory teaches about individualized learning in the classroom.

What Are the Four Learning Styles?

In talking about the learning styles theory, we’re defining it here as the educational model proposed by Neil Fleming in 1984 that presents four types of learning styles.[18] While educators have researched different models and provided various examples of learning styles since the 1970s, Fleming’s model is one that gained a lot of traction among teachers.

Also known as the VARK model, this theory presents four distinct ways that students might best process information:[16]

Visual : information presented through images, like graphs or illustrations
Auditory : information presented through sounds, like audiobooks or songs
Reading/writing : information presented through written words, like books or articles
Kinaesthetic : information presented through experience or touch, like models or simulations

According to the learning styles theory, every student has certain learning styles that work better for them than others. An auditory learner, for example, may focus better while listening to an audiobook than they would while reading a book. These preferences don’t indicate abilities but only differences in processing information.[8]

How would you discover a student’s learning style? Measuring learning styles often involves an assessment such as this one from the Georgia Department of Education. Based on a student’s answers to the questionnaire, teachers can choose to provide them with resources or supplementary information that corresponds to their learning style.

Is There Evidence to Support Different Types of Learning Styles?

Researchers, however, have sought to measure how effective the theory is and uncovered some positive evidence. One study conducted in Taiwan, for example, came to the conclusion that educating students on learning styles can encourage self-reflection—always a positive thing.[9] In another study, a professor at Auburn University found that using learning styles to teach concepts to individual students can improve information recall.[15] This suggests that educating students about learning styles and using them in class may help students pay attention and reflect on their academic needs.

Learning styles can also be a gateway for teachers to implement personalized learning in class.[14] A study conducted in 2013 found that personalized learning can improve motivation as well as academic achievement.[13] Regardless of whether the learning styles theory accurately reflects how people learn, personalization is a great way to support students with unique needs in the classroom.

Have Learning Styles Been Debunked? The Growing Research Against the Theory

While there’s plenty of support for this theory among teachers who find it helpful, research surrounding learning styles is sparse and sometimes critical. It’s true that many students have a preference for one of the four learning styles. As of yet, however, no link has been found between a preferred learning style and academic achievement.[5]

This means that while students may have personal preferences, they don’t actually learn better using one style over another.[1] Because the theories have been around for decades and still fail to produce a measurable effect, an increasing number of educators question the extent to which they should deliberately use them in class.[7]

Rather than an educational theory, some researchers view learning styles as more of a personality test.[2] It can be useful to know which learning medium your students like the most to help them engage more with your lessons. But if you’re looking for a theory that models how the brain actually works, learning styles may not be it.

Ultimately, the choice to use learning styles in class is up to individual teachers. Some may find it a helpful reminder to provide individualized resources, while others may see learning styles as a myth that doesn’t actually help their students. Either way, the debate a great reminder for teachers to research and test an educational theory for themselves before accepting it as fact.[12]

So, What Can We Conclusively Take Away from the Learning Styles Theory?

As you can see, the educational field is split between proponents and critics of the learning styles theory. While it’s often helpful to use different ways of learning in class, the research doesn’t conclusively suggest the theory to be an accurate model of how our brains work. Like the multiple intelligences theory , learning styles can be a practical tool but not a fully-backed theory.

It may also be possible that different learning styles exist and researchers haven’t yet pinpointed what they are.[17] For the most part, the VARK model of learning styles appears to be a personal preference. But in future studies, researchers may discover a new model that accurately represents different ways students learn in class.

Where does this leave learning styles in terms of practical use? The one thing all educators can agree on is that every student has unique needs that may change your teaching approach for that student.[1] A child with dyslexia, for example, would have different needs from a child that simply has trouble learning to read. Some strategies or resources might help certain students more than they do others. Instead of planning activities based on learning styles, teachers may want to opt for a different, but better researched technique: differentiated instruction.

How to Shift from Learning Styles to Differentiated Instruction

What is differentiated instruction and how does it differ from learning styles? Differentiated instruction is when educators provide individualized lesson plans, accommodations, or other resources to students based on their needs. Rather than theming support around a certain learning style, teachers get to know their students to determine what needs they have and how to fill them.

Perhaps because differentiated instruction is more tailored to individuals than to categories of learning styles, its effects are more measurable in the classroom—particularly for students with disabilities.[19] If learning styles piqued your interest in personalized learning, differentiated instruction can provide a more effective framework.

Instead of providing the four-pronged cookie-cutter approach that learning styles suggest, teachers focus on individual students and observe what helps them learn.[5] As they get to know their students and discuss their unique needs with parents, teachers can use differentiated instruction strategies to support academic growth.

In conclusion: generally, the learning styles theory has the right idea.[6] Students learn best when teachers provide resources that meet their personal needs. But rather than categorizing students as a “kinaesthetic” or “visual” learner, they might be better served if teachers work to provide individualized support for different assignments.[4]

Riener, C. & Willingham, D. The Myth of Learning Styles . Change: The Magazine of Higher Learning, 2010, 42(5), pp. 32-35.
Romanelli, F., Bird, E., and Ryan, M. Learning Styles: A Review of Theory, Application, and Best Practices . American Journal of Pharmaceutical Education, 2009, 73(1).
Curry, L. A Critique of the Research on Learning Styles. Educational Leadership, October 1990, 48(2), pp. 50-56.
Reynolds, M. Learning Styles: A Critique . Management Learning, June 1997, 28(2), pp. 115-133.
An, D., and Carr, M. Learning styles theory fails to explain learning and achievement: Recommendations for alternative approaches. Personality and Individual Differences, October 2017, 116(1), pp. 410-416.
Smith, J. Learning Styles: Fashion Fad or Lever for Change? The Application of Learning Style Theory to Inclusive Curriculum Delivery . Innovations in Education and Teaching International, 2002, 39(1), pp. 63-70.
Scott, C. The Enduring Appeal of ‘Learning Styles.” Australian Journal of Education, April 2010, 54(1), pp. 5-17.
Willingham, D.T., Hughes, E.M., and Dobolyi, D.G. The Scientific Status of Learning Styles Theories. Teaching of Psychology, June 2015, 42(3), pp. 266-271.
Hsieh, S., Jang, Y., Hwang, G., and Chen, N. Effects of teaching and learning styles on students’ reflection levels for ubiquitous learning. Computers & Education, August 2011, 57(1), pp. 1194-1201.
Hilgersom-Volk, K. Celebrating Students’ Diversity through Learning Styles. OOSC Bulletin, May 1987, 30(9), pp. 1-32.
Kirschner, P.A. Stop propagating the learning styles myth . Computers & Education, March 2017, 106, pp. 166-171.
Dembo, M., and Howard, K. Advice about the Use of Learning Styles: A Major Myth in Education. Journal of College Reading and Learning, 2007, 37(2), pp. 101-109.
Hwang, G., Sung, H., Hung, C., Huang, I., and Tsai, C. Development of a personalized educational computer game based on students’ learning styles . Educational Technology Research and Development, August 2012, 60(4), pp. 623-638.
Horton, C.B., and Oakland, T. Temperament-based learning styles as moderators of academic achievement. Adolescence, 1997, 32(125), 131-141.
Davis, S.E. Learning Styles and Memory . Institute for Learning Styles Journal, 2007, 1, pp. 46-51.
Wilfrid Laurier University. Understanding Your Learning Style. Retrieved from wlu.ca: https://web.wlu.ca/learning_resources/pdfs/Learning_Styles.pdf.
Pashler, H., McDaniel, M., Rohrer, D., and Bjork, R. Learning Styles: Concepts and Evidence . Psychological Science in the Public Interest, 2009, 9(3), pp. 105-119.
Leite, W.L., Svinicki, M., and Shi, Y. Attempted Validation of the Scores of the VARK: Learning Styles Inventory With Multitrait–Multimethod Confirmatory Factor Analysis Models . Educational and Psychological Measurement, 2010, 70(2), pp. 323-339.
Darrow, A. Differentiated Instruction for Students With Disabilities: Using DI in the Music Classroom. General Music Today, 2015, 28(2), pp.29-32.

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Overview of VARK Learning Styles

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VARK Learning Styles

Find Your Style
Kinesthetic

Why It Matters

Frequently asked questions.

Learning styles are a popular concept in psychology and education and are intended to identify how people learn best. VARK learning styles suggest that there are four main types of learners: visual, auditory, reading/writing, and kinesthetic.

The idea that students learn best when teaching methods and school activities match their learning styles, strengths, and preferences grew in popularity in the 1970s and 1980s. However, most evidence suggests that personal learning preferences have little to no actual influence on learning outcomes.

While the existing research has found that matching teaching methods to learning styles has no influence on educational outcomes, the concept of learning styles remains extremely popular.

There are many different ways of categorizing learning styles , but Neil Fleming's VARK model is one of the most popular. Fleming introduced an inventory in 1987 that was designed to help students and others learn more about their individual learning preferences.

According to the VARK model, learners are identified by whether they have a preference for:

Visual learning (pictures, movies, diagrams)
Auditory learning (music, discussion, lectures)
Reading and writing (making lists, reading textbooks, taking notes)
Kinesthetic learning (movement, experiments, hands-on activities)

The VARK model refers to the four sensory modalities that describe different learning preferences. The model suggests that these modalities reflect how students learn best.

What Type of Learner Are You?

In order to identify which type of learner people are, Fleming developed a self-report inventory that posed a series of situations. Respondents select the answers that best match their preferred approach to learning.

Imagine that you are learning how to perform a new physical skill such as riding a bike or dancing a certain style of dance. In which way would you learn this skill the best?

Look at pictures of people performing the skill. (Visual)
Listen to an expert explain how to do the task. (Auditory)
Read about how to perform the task in a book. (Reading/Writing)
Watch someone else perform the skill and then trying it yourself. (Kinesthetic)

Visual Learners

Visual learners learn best by seeing. Graphic displays such as charts, diagrams, illustrations, handouts, and videos are all helpful learning tools for visual learners.

Visual learners prefer this type of learning would rather see information presented in a visual rather than in written form.

Do you think you might be a visual learner? Then consider the following questions:

Are art, beauty, and aesthetics important to you?
Does visualizing information in your mind help you remember it better?
Do you have to see information in order to remember it?
Do you pay close attention to body language ?

If you can answer yes to most of these questions, chances are good that you have a visual learning style. You may find it helpful to incorporate things like pictures and graphs when you are learning new information.

Aural Learners

Aural (or auditory) learners learn best by hearing information. They tend to get a great deal out of lectures and are good at remembering things they are told.

Are you an auditory learner? Consider the following questions:

Do you create songs to help remember information?
Does reading out loud help you remember information better?
Do you prefer to listen to class lectures rather than reading from the textbook?
Would you prefer to listen to a recording of your class lectures or a podcast rather than going over your class notes?

If you answered yes to most of these questions, then you are probably an auditory learner. You might find things like audiobooks and podcasts helpful for learning new things.

Reading and Writing Learners

Reading and writing learners prefer to take in information that is displayed as words and text. Could you be a reading and writing learner? Read through the following questions and think about whether they might apply to you.

Do you enjoy making lists, reading definitions, and creating presentations?
Do you find reading your textbook to be a great way to learn new information?
Do you take a lot of notes during class and while reading textbooks?
Do you prefer it when teachers make use of overheads and handouts?

If you answered yes to these questions, it is likely that you have a strong preference for the reading and writing style of learning. You might find it helpful to write down information in order to help you learn and remember it.

Kinesthetic Learners

Kinesthetic (or tactile) learners learn best by touching and doing. Hands-on experience is important for kinesthetic learners.

Not sure if you're a kinesthetic learner? Answer these questions to find out:

Are you good at applied activities such as painting, cooking, mechanics, sports, and woodworking?
Do you enjoy performing tasks that involve directly manipulating objects and materials?
Do you have to actually practice doing something in order to learn it?
Is it difficult for you to sit still for long periods of time?

If you responded yes to these questions, then you are most likely a kinesthetic learner. Taking classes that give you practical, hands-on experience may be helpful when you want to acquire a new skill.

The validity of the VARK model as well as other learning style theories has been questioned and criticized extensively. Some critics have suggested that labeling students as having one specific learning style can actually be a hindrance to learning.

One large-scale look at learning style models suggested that the instruments designed to assess individual learning styles were questionable.

The VARK model remains fairly popular among both students and educators despite these criticisms. Students may feel drawn to a particular learning style. Others may find that their learning preferences lie somewhere in the middle, such as finding both visual and auditory learning equally appealing.

People might find that understanding their own learning preferences can be helpful. If you know that visual learning appeals to you most, using visual study strategies in conjunction with other learning methods might help you remember and enjoy your studies more.

If no single learning preference calls out to you or you change preferences based on the situation or the type of information you are learning, you probably have what is known as a multimodal style .

For example, you might rely on your reading and writing preferences when you are dealing with a class that requires a great deal of book reading and note-taking, such as a history of psychology course. During an art class, you might depend more on your visual and kinesthetic preferences as you take in pictorial information and learn new techniques.

The four VARK learning styles are visual learners, aural learners, reading and writing learners, and kinesthetic learners.

According to some data, the most common is a multimodal learning style referred to as VARK Type Two, which involves exhibiting a range of learning preferences. People with this learning style tend to collect information more slowly and take time to make decisions.

In terms of single preferences, kinesthetic is by far the most common, accounting for 22.8% of respondents.

Pashler H, Mcdaniel M, Rohrer D, Bjork R. Learning styles: concepts and evidence . Psychol Sci Public Interest . 2008;9(3):105-19. doi:10.1111/j.1539-6053.2009.01038.x

VARK Learn Limited. VARK research - what do we know about VARK ?

Fleming N. Introduction to Vark .

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Center for Teaching

Learning styles.

Chick, N. (2010). Learning Styles. Vanderbilt University Center for Teaching. Retrieved [todaysdate] from https://cft.vanderbilt.edu/guides-sub-pages/learning-styles-preferences/.

What are Learning Styles?

Why are they so popular.

The term learning styles is widely used to describe how learners gather, sift through, interpret, organize, come to conclusions about, and “store” information for further use. As spelled out in VARK (one of the most popular learning styles inventories), these styles are often categorized by sensory approaches: v isual, a ural, verbal [ r eading/writing], and k inesthetic. Many of the models that don’t resemble the VARK’s sensory focus are reminiscent of Felder and Silverman’s Index of Learning Styles , with a continuum of descriptors for how learners process and organize information: active-reflective, sensing-intuitive, verbal-visual, and sequential-global.

There are well over 70 different learning styles schemes (Coffield, 2004), most of which are supported by “a thriving industry devoted to publishing learning-styles tests and guidebooks” and “professional development workshops for teachers and educators” (Pashler, et al., 2009, p. 105).

Despite the variation in categories, the fundamental idea behind learning styles is the same: that each of us has a specific learning style (sometimes called a “preference”), and we learn best when information is presented to us in this style. For example, visual learners would learn any subject matter best if given graphically or through other kinds of visual images, kinesthetic learners would learn more effectively if they could involve bodily movements in the learning process, and so on. The message thus given to instructors is that “optimal instruction requires diagnosing individuals’ learning style[s] and tailoring instruction accordingly” (Pashler, et al., 2009, p. 105).

Despite the popularity of learning styles and inventories such as the VARK, it’s important to know that there is no evidence to support the idea that matching activities to one’s learning style improves learning . It’s not simply a matter of “the absence of evidence doesn’t mean the evidence of absence.” On the contrary, for years researchers have tried to make this connection through hundreds of studies.

In 2009, Psychological Science in the Public Interest commissioned cognitive psychologists Harold Pashler, Mark McDaniel, Doug Rohrer, and Robert Bjork to evaluate the research on learning styles to determine whether there is credible evidence to support using learning styles in instruction. They came to a startling but clear conclusion: “Although the literature on learning styles is enormous,” they “found virtually no evidence” supporting the idea that “instruction is best provided in a format that matches the preference of the learner.” Many of those studies suffered from weak research design, rendering them far from convincing. Others with an effective experimental design “found results that flatly contradict the popular” assumptions about learning styles (p. 105). In sum,

“The contrast between the enormous popularity of the learning-styles approach within education and the lack of credible evidence for its utility is, in our opinion, striking and disturbing” (p. 117).

Pashler and his colleagues point to some reasons to explain why learning styles have gained—and kept—such traction, aside from the enormous industry that supports the concept. First, people like to identify themselves and others by “type.” Such categories help order the social environment and offer quick ways of understanding each other. Also, this approach appeals to the idea that learners should be recognized as “unique individuals”—or, more precisely, that differences among students should be acknowledged —rather than treated as a number in a crowd or a faceless class of students (p. 107). Carried further, teaching to different learning styles suggests that “ all people have the potential to learn effectively and easily if only instruction is tailored to their individual learning styles ” (p. 107).

There may be another reason why this approach to learning styles is so widely accepted. They very loosely resemble the concept of metacognition , or the process of thinking about one’s thinking. For instance, having your students describe which study strategies and conditions for their last exam worked for them and which didn’t is likely to improve their studying on the next exam (Tanner, 2012). Integrating such metacognitive activities into the classroom—unlike learning styles—is supported by a wealth of research (e.g., Askell Williams, Lawson, & Murray-Harvey, 2007; Bransford, Brown, & Cocking, 2000; Butler & Winne, 1995; Isaacson & Fujita, 2006; Nelson & Dunlosky, 1991; Tobias & Everson, 2002).

Importantly, metacognition is focused on planning, monitoring, and evaluating any kind of thinking about thinking and does nothing to connect one’s identity or abilities to any singular approach to knowledge. (For more information about metacognition, see CFT Assistant Director Cynthia Brame’s “ Thinking about Metacognition ” blog post, and stay tuned for a Teaching Guide on metacognition this spring.)

There is, however, something you can take away from these different approaches to learning—not based on the learner, but instead on the content being learned . To explore the persistence of the belief in learning styles, CFT Assistant Director Nancy Chick interviewed Dr. Bill Cerbin, Professor of Psychology and Director of the Center for Advancing Teaching and Learning at the University of Wisconsin-La Crosse and former Carnegie Scholar with the Carnegie Academy for the Scholarship of Teaching and Learning. He points out that the differences identified by the labels “visual, auditory, kinesthetic, and reading/writing” are more appropriately connected to the nature of the discipline:

“There may be evidence that indicates that there are some ways to teach some subjects that are just better than others , despite the learning styles of individuals…. If you’re thinking about teaching sculpture, I’m not sure that long tracts of verbal descriptions of statues or of sculptures would be a particularly effective way for individuals to learn about works of art. Naturally, these are physical objects and you need to take a look at them, you might even need to handle them.” (Cerbin, 2011, 7:45-8:30 )

Pashler and his colleagues agree: “An obvious point is that the optimal instructional method is likely to vary across disciplines” (p. 116). In other words, it makes disciplinary sense to include kinesthetic activities in sculpture and anatomy courses, reading/writing activities in literature and history courses, visual activities in geography and engineering courses, and auditory activities in music, foreign language, and speech courses. Obvious or not, it aligns teaching and learning with the contours of the subject matter, without limiting the potential abilities of the learners.

Askell-Williams, H., Lawson, M. & Murray, Harvey, R. (2007). ‘ What happens in my university classes that helps me to learn?’: Teacher education students’ instructional metacognitive knowledge. International Journal of the Scholarship of Teaching and Learning , 1. 1-21.
Bransford, J. D., Brown, A. L. & Cocking, R. R., (Eds.). (2000). How people learn: Brain, mind, experience, and school (Expanded Edition). Washington, D.C.: National Academy Press.
Butler, D. L., & Winne, P. H. (1995) Feedback and self-regulated learning: A theoretical synthesis . Review of Educational Research , 65, 245-281.
Cerbin, William. (2011). Understanding learning styles: A conversation with Dr. Bill Cerbin . Interview with Nancy Chick. UW Colleges Virtual Teaching and Learning Center .
Cofﬁeld, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning. A systematic and critical review . London: Learning and Skills Research Centre.
Isaacson, R. M. & Fujita, F. (2006). Metacognitive knowledge monitoring and self-regulated learning: Academic success and reflections on learning . Journal of the Scholarship of Teaching and Learning , 6, 39-55.
Nelson, T.O. & Dunlosky, J. (1991). The delayed-JOL effect: When delaying your judgments of learning can improve the accuracy of your metacognitive monitoring. Psychological Science , 2, 267-270.
Pashler, Harold, McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence . Psychological Science in the Public Interest . 9.3 103-119.
Tobias, S., & Everson, H. (2002). Knowing what you know and what you don’t: Further research on metacognitive knowledge monitoring . College Board Report No. 2002-3 . College Board, NY.

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Learning Styles: What are They, Models and Discussion

Learning Styles. Learning is a massive part of everyone’s life. From childhood to adolescence, we go to school for hours daily to learn about various subjects. Outside of schooling, we continue to learn in everyday life — including how to perform better in the workplace, how to work through interpersonal issues, or how to fix practical household dilemmas. But does everyone learn in the same way? That doesn’t seem to be the case. There is no one-size-fits-all method of learning. To learn and teach most effectively, we must know an individual’s preferred learning styles .

Different Learning Styles?

It is often recognized that there are differences in the ways individuals learn. Even at a very young age, a child will prefer certain subjects and teachers over others. They may be excited at their performance on a math assignment, but spend their time in history class doodling. Alternatively, a child may be an enthusiastic art student under the guidance of one teacher, and then lose interest when that teacher is replaced. These are the consequences of a child’s unique learning style.

In the classroom, teachers will notice that students vary remarkably in the pace and manner by which they pick up new ideas and information. This same concept carries into the workplace, where employers notice that employees learn and perform better under different conditions. Conversely, each teacher has their own preferred method of teaching. Each teacher has their particular style and then so does each learner. Problems can occur when teachers and learners don’t match.

Models of Learning Styles

Since the 1970s, researchers have theorized models to describe individual differences in learning. Everyone has a mix of preferred learning styles. These preferences guide the way we learn. They determine the way an individual mentally represents and recalls information. Research shows that different learning styles involve different parts of the brain . Unfortunately, there is no universally accepted model of learning styles. Rather there are dozens of competing models. The most widely recognized model, “The Seven Learning Styles”, as well as David Kolb’s and Neil Fleming’s models are discussed below.

The Seven Learning Styles

Known simply as “The Seven Learning Styles”, this is the most commonly accepted model of learning styles. It is referenced by researchers and teachers alike. To find out which of the seven learning styles apply to you, fill out this questionnaire. This is an unofficial inventory of the Seven Learning Styles provided by Memletics (care for the pop-ups!). The Seven Learning styles are as follows:

Visual (Spatial)

Visual learners have an ability to perceive the visual. They p refer to learn through pictures and images and are good at spatial understanding (relating to a given space and the relationship of objects within it). They create vivid mental images to remember information and enjoy viewing pictures, videos, maps, and charts.

Interpreting and manipulating images
Drawing and painting
Charting and graphing
Good sense of direction
Creating visual analogies and metaphors
Puzzle Building
Constructing
Designing and fixing objects
Use images, pictures, and other visuals to learn
Pay attention to color, layout, and spatial organization
Make use of ‘visual words’ when speaking
Use ‘mind maps’ (diagrams used to visually organize information)

Aural (Auditory/Musical)

Aural learners p refer to learn through sounds and music and a re able to produce and appreciate music. They tend to think in rhythms and patterns, and are particularly sensitive to sounds in the immediate environment.

Singing and whistling
Playing musical instruments
Writing music
Recognizing melodies and tonal patterns
Understanding rhythm and structure of music
Use mnemonics , rhyming, and rhythm to memorize new ideas
Ambient recordings can increase concentration
Music can inspire certain feelings and emotional states. Make use of music to anchor your emotions.

Verbal (Linguistic)

Verbal learners have an ability to use words and language. While many people think in pictures, these learners think in words. They tend to be elegant speakers, with highly developed auditory skills.

Storytelling
Analyzing language
Read content aloud, and try to make it dramatic and varied to aid recall
Verbal role-playing can aid in understanding concepts
Make use of techniques such as assertion and scripting
Record your scripts and listen back

Physical (Kinaesthetic)

Physical learners prefer learning with their body and sense of touch. They are adept art controlling their bodies and handling objects. Information is processed by interacting with the space around them. A good sense of balance and hand-eye coordination is common.

Physical coordination
Working with hands
Using body language

Learning tips:

Use hands-on activities to learn
Describe the physical sensations of an experience with verbs and adverbs
Use physical objects as much as possible, including flash cards and miniature models
Writing and drawing diagrams may help, as these are physical activities

The Seven Learning Styles is the most popular model.

Logical (Mathematical)

Logical learners are able to use reason, logic, and numbers. They think in terms of systems, patterns, and concepts. These learners also seek to understand the reasoning or the “why” behind each new concept and like to experiment.

Categorization
Problem solving
Complex mathematical calculations
Connecting concepts
Making logical conclusions from long chains of reasoning
Experimentation
Focus on exploring connections between ideas
Make lists of key concepts from material
Think in terms of procedures
Think in terms of systems
Thinking in terms of systems may help you understand the “big picture”
Create diagrams that outline entire systems

Social (Interpersonal)

Social learners have an ability to relate to and understand others. These learners are good at sensing the feelings, intentions, and motivations of others. They are also able to see things from multiple perspectives. These learners are often good at encouraging cooperation, but sometimes their abilities enable them to manipulate others.

Communication, both verbal and non-verbal
Conflict resolution
Establishing relations with others
Building trust
Noticing the feelings, moods, intentions, and motivations of others
Work with others as much as possible
Use one-on-one or group roleplaying
Share what you have learned with others, including associations and visualizations you have made
Learn from others’ practices, associations, and visualizations
Learn from others’ mistakes

Solitary (Intrapersonal)

These learners like to introspect and self-reflect. This gives them a keen awareness of their own inner state of being. They understand their own inner desires, motivations , feelings, strengths, and weaknesses.

Self-awareness
Self-analysis
Evaluating one’s own thoughts and emotions
Understanding one’s role in relationships with others
Study in private
Try to invest yourself personally in your work
Adjust your goals to fit your personal values. This maximizes motivation.
Keep a journal to record thoughts and observations
Focus on what you would be feeling or thinking about when you associate or visualize
Train your brain cognitively, with training programs such as CogniFit which is a leading company in cognitive brain training programs . You can register here.

David Kolb’s Model of Learning Styles

“Learning is the process whereby knowledge is created through the transformation of experience.” – David A. Kolb

David A. Kolb’s model is outlined his book “Experiential Learning”, published in 1984. In this book, Kolb speaks of a four-stage cycle of learning as well as four independent learning styles. According to Kolb, all four stages of the learning cycle will be engaged in a complete learning process. The four stages are described below.

Concrete Experience – This occurs when a new experience, or a reinterpretation of an existing experience, is encountered.
Reflective Observation – This occurs when the experience is reviewed or reflected upon, with the goal of achieving a consistent understanding.
Abstract Conceptualization – This occurs when a new idea or concept arises from reflection.
Active Experimentation – This occurs when new ideas are applied to the world and the results are observed.

David Kolb’s four learning styles are built upon this four-stage learning cycle. An individual will naturally prefer one of these styles over the others. This preference is influenced by social and educational environments as well as cognitive structures. Although everyone will occasionally need the stimulus of all four of these learning styles, it is useful to know your personal orientation.

Learning Styles: Diverging

This style corresponds with the first two stages and involves watching and feeling. People who are oriented towards diverging are able to see things from many different perspectives. They gather information by watching rather than doing and use their imagination to solve problems. This means that they are good at brainstorming and other methods of generating ideas. Diverging thinkers tend to have an open mind and broad interests. They tend to be imaginative and emotional and can be talented in the arts.

Learning Styles: Assimilating

This style corresponds with the second and third stages. It involves watching and thinking. People who prefer assimilating have a concise, logical approach to processing information. To them, ideas and concepts are primary, while people and practical applications are secondary. Information should be organized in a clear logical format. Because of their preference for the abstract, these learners tend to prefer reading, lectures, and analyzing concepts.

Learning Styles: Converging

This styles corresponds with the last two stages and involves doing and thinking. These learners strive for practical, “hands-on” solutions. They excel at technical work, finding practical uses for ideas and theories, and are less concerned with the interpersonal. Problem-solving comes most naturally to these learners. They like to experiment with new ideas and find practical applications. This allows for great technical and specialist abilities.

Learning Styles: Accommodating

This style corresponds with the fourth and first stages. It involves doing and feeling. Much like converging learners, accommodating learners are “hands on”. They rely on intuition rather than logic, and their strength lies in imaginative ability and discussion. “Gut” instinct is primary. They do not shy away from an interpersonal approach, often relying on others for information or analysis. New challenges and experiences excite these learners.

Neil Fleming’s Model of Learning Styles

Dr. Neil Fleming identified four learning styles in the 1980’s. These four styles came to be known as the “VARK” model of learning styles. This model describes the sensory preferences of learning. It is built on earlier notions of sensory processing, such the VAK model. This is perhaps the most straightforward of models. It is simple yet insightful.

Visual – You learn best from images, pictures, symbols, charts, graphs, diagrams and other forms of spatial organization.
Auditory – You learn best from sound, rhythm, music, speaking and listening.
Reading and Writing – You learn best from reading and writing.
Kinesthetic – You learn best from interacting with their physical surroundings, making use of your body and sense of touch.

Learning Styles: A myth?

There has been recent controversy regarding the subject of learning styles. Although the idea has a lot of intuitive appeals, many disagree with it altogether. There are some problems that can be easily identified.

The first is that there is no agreed-upon model for learning styles. Over 70 different models have been identified, including The Seven Learning Styles, David Kolb’s model, Neil Fleming’s model, “right” and “left” brain model, “holistic” vs. “serialist” model, and so on. All of these models have very little research that supports their validity over others — some are merely more popular than others.

The second and most important problem is that there is no research to support the effectiveness of teaching to an individual’s learning style. A major premise of the theory of learning styles is that individuals learn better when the material is matched to their learning style. Unfortunately, studies have shown either no evidence or weak evidence to support this. On the other hand, studies do show that individuals will learn better if they reflect on their own learning style. This alone lends credence to the theory of learning styles. While it may not be useful to teach to individual learning styles, it is useful to reflect on your own preferences.

Some argue that the lack of evidence means that learning styles don’t exist. Many agree that they do exist, but are simply difficult to measure. Regardless of the extent of their validity, it is always interesting to learn more about yourself.

Category: Brain Health & Neuroscience , Educators
Tag: cognition , Improve memory , learning , memory , visual memory

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Michele Marenus is a Ph.D. candidate at the University of Michigan with over seven years of experience in psychology research.

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Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Howard Gardner first proposed the theory of multiple intelligences in his 1983 book “Frames of Mind”, where he broadens the definition of intelligence and outlines several distinct types of intellectual competencies.

Gardner developed a series of eight inclusion criteria while evaluating each “candidate” intelligence that was based on a variety of scientific disciplines.

He writes that we may all have these intelligences, but our profile of these intelligences may differ individually based on genetics or experience.

Gardner defines intelligence as a “biopsychological potential to process information that can be activated in a cultural setting to solve problems or create products that are of value in a culture” (Gardner, 2000, p.28).

What is Multiple Intelligences Theory?

Howard Gardner’s theory of multiple intelligences proposes that people are not born with all of the intelligence they will ever have.
This theory challenged the traditional notion that there is one single type of intelligence, sometimes known as “g” for general intelligence, that only focuses on cognitive abilities.
To broaden this notion of intelligence, Gardner introduced eight different types of intelligences consisting of: Linguistic, Logical/Mathematical, Spatial, Bodily-Kinesthetic, Musical, Interpersonal, Intrapersonal, and Naturalist.
Gardner notes that the linguistic and logical-mathematical modalities are most typed valued in school and society.
Gardner also suggests that there may other “candidate” intelligences—such as spiritual intelligence, existential intelligence, and moral intelligence—but does not believe these meet his original inclusion criteria. (Gardner, 2011).

Inclusion Criteria to be Categorized as a Multiple Intelligence
Potential of isolation by brain damage.
Evolutionary history and evolutionary plausibility.
Identifiable core operations or set of operations.
Susceptibility to encoding in a symbol system.
Distinct developmental history and definable set of expert “end state” performances.
Existence of savants, prodigies, and other exceptional people.
Support from experimental psychological tasks.
Support from psychometric findings.

Linguistic Intelligence (word smart)

Linguistic Intelligence is a part of Howard Gardner’s multiple intelligence theory that deals with sensitivity to the spoken and written language, ability to learn languages, and capacity to use language to accomplish certain goals.

Linguistic intelligence involves the ability to use language masterfully to express oneself rhetorically or poetically. It includes the ability to manipulate syntax, structure, semantics, and phonology of language.

People with linguistic intelligence, such as William Shakespeare and Oprah Winfrey, have the ability to analyze information and create products involving oral and written language, such as speeches, books, and memos.

Potential Career Choices

Careers you could dominate with your linguistic intelligence:

Lawyer Speaker / Host Author Journalist Curator

Logical-Mathematical Intelligence (number/reasoning smart)

Logical-mathematical intelligence refers to the capacity to analyze problems logically, carry out mathematical operations, and investigate issues scientifically.

Logical-mathematical intelligence involves the ability to use logic, abstractions, reasoning, and critical thinking to solve problems. It includes the capacity to understand the underlying principles of some kind of causal system.

People with logical-mathematical intelligence, such as Albert Einstein and Bill Gates, have an ability to develop equations and proofs, make calculations, and solve abstract problems.

Careers you could dominate with your logical-mathematical intelligence:

Mathematician Accountant Statistician Scientist Computer Analyst

Spatial Intelligence (picture smart)

Spatial intelligence involves the ability to perceive the visual-spatial world accurately. It includes the ability to transform, modify, or manipulate visual information. People with high spatial intelligence are good at visualization, drawing, sense of direction, puzzle building, and reading maps.

Spatial intelligence features the potential to recognize and manipulate the patterns of wide space (those used, for instance, by navigators and pilots) as well as the patterns of more confined areas, such as those of importance to sculptors, surgeons, chess players, graphic artists, or architects.

People with spatial intelligence, such as Frank Lloyd Wright and Amelia Earhart, have the ability to recognize and manipulate large-scale and fine-grained spatial images.

Careers you could dominate with your spatial intelligence:

Pilot Surgeon Architect Graphic Artist Interior Decorator

Bodily-Kinesthetic Intelligence (body smart)

Bodily-kinesthetic intelligence is the potential of using one’s whole body or parts of the body (like the hand or the mouth) to solve problems or to fashion products.

Bodily-kinesthetic intelligence involves using the body with finesse, grace, and skill. It includes physical coordination, balance, dexterity, strength, and flexibility. People with high bodily-kinesthetic intelligence are good at sports, dance, acting, and physical crafts.

People with bodily-kinesthetic intelligence, such as Michael Jordan and Simone Biles, can use one’s own body to create products, perform skills, or solve problems through mind–body union.

Careers you could dominate with your bodily-kinesthetic intelligence:

Dancer Athlete Surgeon Mechanic Carpenter Physical Therapist

Musical Intelligence (music smart)

Musical intelligence refers to the skill in the performance, composition, and appreciation of musical patterns.

Musical intelligence involves the ability to perceive, discriminate, create, and express musical forms. It includes sensitivity to rhythm, pitch, melody, and tone color. People with high musical intelligence are good at singing, playing instruments, and composing music.

People with musical intelligence, such as Beethoven and Ed Sheeran, have the ability to recognize and create musical pitch, rhythm, timbre, and tone.

Careers you could dominate with your musical intelligence:

Singer Composer DJ Musician

Interpersonal Intelligence (people smart)

Interpersonal intelligence is the capacity to understand the intentions, motivations, and desires of other people and, consequently, to work effectively with others.

Interpersonal intelligence involves the ability to understand and interact effectively with others. It includes sensitivity to other people’s moods, temperaments, motivations, and desires. People with high interpersonal intelligence communicate well and can build rapport.

People with interpersonal intelligence, such as Mahatma Gandhi and Mother Teresa, have the ability to recognize and understand other people’s moods, desires, motivations, and intentions.

Careers you could dominate with your interpersonal intelligence:

Teacher Psychologist Manager Salespeople Public Relations

Intrapersonal Intelligence (self-smart)

Intrapersonal intelligence is the capacity to understand oneself, to have an effective working model of oneself, including one’s desires, fears, and capacities—and to use such information effectively in regulating one’s own life.

It includes self-awareness, personal cognizance, and the ability to refine, analyze, and articulate one’s emotional life.

People with intrapersonal intelligence, such as Aristotle and Maya Angelou, have the ability to recognize and understand his or her own moods, desires, motivations, and intentions.

This type of intelligence can help a person understand which life goals are important and how to achieve them.

Careers you could dominate with your intrapersonal intelligence:

Therapist Psychologist Counselor Entrepreneur Clergy

Naturalist intelligence (nature smart)

Naturalist intelligence involves the ability to recognize, categorize, and draw upon patterns in the natural environment. It includes sensitivity to the flora, fauna, and phenomena in nature. People with high naturalist intelligence are good at classifying natural forms.

Naturalistic intelligence involves expertise in recognizing and classifying the numerous species—the flora and fauna—of his or her environment.

People with naturalistic intelligence, such as Charles Darwin and Jane Goddall, have the ability to identify and distinguish among different types of plants, animals, and weather formations that are found in the natural world.

Careers you could dominate with your naturalist intelligence:

Botanist Biologist Astronomer Meteorologist Geologist

Critical Evaluation

Most resistance to multiple intelligences theory has come from cognitive psychologists and psychometricians. Cognitive psychologists such as Waterhouse (2006) claimed that there is no empirical evidence to the validity of the theory of multiple intelligences.

Psychometricians, or psychologists involved in testing, argue that intelligence tests support the concept for a single general intelligence, “g”, rather than the eight distinct competencies (Gottfredson, 2004). Other researchers argue that Gardner’s intelligences comes second or third to the “g” factor (Visser, Ashton, & Vernon, 2006).

Some responses to this criticism include that the multiple intelligences theory doesn’t dispute the existence of the “g” factor; it proposes that it is equal along with the other intelligences. Many critics overlook the inclusion criteria Gardner set forth.

These criteria are strongly supported by empirical evidence in psychology, biology, neuroscience, among others. Gardner admits that traditional psychologists were valid in criticizing the lack of operational definitions for the intelligences, that is, to figure out how to measure and test the various competencies (Davis et al., 2011).

Gardner was surprised to find that Multiple Intelligences theory has been used most widely in educational contexts. He developed this theory to challenge academic psychologists, and therefore, he did not present many educational suggestions. For this reason, teachers and educators were able to take the theory and apply it as they saw fit.

As it gained popularity in this field, Gardner has maintained that practitioners should determine the theory’s best use in classrooms. He has often declined opportunities to aid in curriculum development that uses multiple intelligences theory, opting to only provide feedback at most (Gardner, 2011).

Most of the criticism has come from those removed from the classroom, such as journalists and academics. Educators are not typically tied to the same standard of evidence and are less concerned with abstract inconsistencies, which has given them the freedom to apply it with their students and let the results speak for itself (Armstrong, 2019).

Shearer (2020) provides extensive empirical evidence from neuroscience research supporting MI theory.

Shearer reviewed evidence from over 500 functional neuroimaging studies that associate patterns of brain activation with the cognitive components of each intelligence.

The visual network was associated with the visual-spatial intelligence, somatomotor networks with kinesthetic intelligence, fronto-parietal networks with logical and general intelligence, auditory networks with musical intelligence, and default mode networks with intra- and interpersonal intelligences. The coherence and distinctiveness of these networks provides robust support for the neural validity of MI theory

He concludes that human intelligence is best characterized as being multiple rather than singular, with each person possessing unique neural potentials aligned with specific intelligences.

Implications for Learning

The most important educational implications of the theory of multiple intelligences can be summed up through individuation and pluralization. Individuation posits that because each person differs from other another there is no logical reason to teach and assess students identically.

Individualized education has typically been reserved for the wealthy and others who could afford to hire tutors to address individual student’s needs.

Technology has now made it possible for more people to access a variety of teachings and assessments depending on their needs. Pluralization, the idea that topics and skills should be taught in more than one way, activates an individual’s multiple intelligences.

Presenting a variety of activities and approaches to learning helps reach all students and encourages them to be able to think about the subjects from various perspectives, deepening their knowledge of that topic (Gardner, 2011b).

A common misconception about the theory of multiple intelligences is that it is synonymous with learning styles. Gardner states that learning styles refer to the way an individual is most comfortable approaching a range of tasks and materials.

Multiple intelligences theory states that everyone has all eight intelligences at varying degrees of proficiency and an individual’s learning style is unrelated to the areas in which they are the most intelligent.

For example, someone with linguistic intelligence may not necessarily learn best through writing and reading. Classifying students by their learning styles or intelligences alone may limit their potential for learning.

Research shows that students are more engaged and learn best when they are given various ways to demonstrate their knowledge and skills, which also helps teachers more accurately assess student learning (Darling-Hammond, 2010).

Therapeutic Benefits of Incorporating Multiple Intelligences Within Therapy

Pearson et al. (2015) investigated the experiences of 8 counselors who introduced multiple intelligences (MI) theory and activities into therapy sessions with adult clients. The counselors participated in a 1-day MI training intervention and were interviewed 3 months later about their experiences using MI in practice.

The major themes that emerged from qualitative analysis of the interviews were:

MI helped enhance therapeutic alliances. Counselors felt incorporating MI strengthened their connections with clients, increased counselor and client comfort, and reduced client suspicion/resistance.
MI led to more effective professional work. Counselors felt MI provided more tools and flexibility in responding to clients. This matches findings from education research on the benefits of MI.
Clients responded positively to identifying strengths through MI. The MI survey helped clients recognize talents/abilities, which counselors saw as identity-building. This aligns with the literature on strength-based approaches.
Clients appreciated the MI preference survey. It provided conversation starters, increased self-reflection, and was sometimes a catalyst for using music therapeutically.
Counselors felt comfortable with MI. They experienced increased confidence and professional comfort. Counselor confidence contributes to alliance building (Ackerman & Hilsenroth, 2003).
Music use stood out as impactful. In-session and extratherapeutic music use improved client well-being after identifying musicality through the MI survey. This matches the established benefits of music therapy (Koelsch, 2009).
MI training opened up therapeutic possibilities. Counselors valued the experiential MI training. MI appeared to expand their skills and activities.

The authors conclude that MI may enhance alliances, effectiveness, and counselor confidence. They recommend further research on long-term impacts and optimal training approaches. Counselor education could teach MI theory, assessment, and tailored interventions.

Frequently Asked Questions

How can understanding the theory of multiple intelligences contribute to self-awareness and personal growth.

Understanding the theory of multiple intelligences can contribute to self-awareness and personal growth by providing a framework for recognizing and valuing different strengths and abilities.

By identifying their own unique mix of intelligences, individuals can gain a greater understanding of their own strengths and limitations and develop a more well-rounded sense of self.

Additionally, recognizing and valuing the diverse strengths and abilities of others can promote empathy , respect, and cooperation in personal and professional relationships.

Why is multiple intelligence theory important?

Understanding multiple intelligences is important because it helps individuals recognize that intelligence is not just about academic achievement or IQ scores, but also includes a range of different abilities and strengths.

By identifying their own unique mix of intelligences, individuals can develop a greater sense of self-awareness and self-esteem, as well as pursue career paths that align with their strengths and interests.

Additionally, understanding multiple intelligences can promote more inclusive and personalized approaches to education and learning that recognize and value the diverse strengths and abilities of all students.

Are certain types of intelligence more valued or prioritized in society than others?

Yes, certain types of intelligence, such as linguistic and logical-mathematical intelligence, are often prioritized in traditional education and assessment methods.

However, the theory of multiple intelligences challenges this narrow definition of intelligence and recognizes the value of a diverse range of strengths and abilities.

By promoting a more inclusive and personalized approach to education and learning, the theory of multiple intelligences can help individuals recognize and develop their unique mix of intelligences, regardless of whether they align with traditional societal expectations.

What is the difference between multiple intelligences and learning styles?

The theory of multiple intelligences proposes that individuals possess a range of different types of intelligence. In contrast, learning styles refer to an individual’s preferred way of processing information, such as visual, auditory, or kinesthetic.

While both theories emphasize the importance of recognizing and valuing individual differences in learning and development, multiple intelligence theory proposes a broader and more diverse range of intelligences beyond traditional academic abilities, while learning styles are focused on preferences for processing information.

Armstrong, T. (2009). Multiple intelligences in the classroom . Ascd.

Darling-Hammond, L. (2010). Performance Counts: Assessment Systems That Support High-Quality Learning . Council of Chief State School Officers .

Davis, K., Christodoulou, J., Seider, S., & Gardner, H. E. (2011). The theory of multiple intelligences. Davis, K., Christodoulou, J., Seider, S., & Gardner, H.(2011). The theory of multiple intelligences . In RJ Sternberg & SB Kaufman (Eds.), Cambridge Handbook of Intelligence , 485-503.

Edutopia. (2013, March 8). Multiple Intelligences: What Does the Research Say? https://www.edutopia.org/multiple-intelligences-research

Gardner, H. E. (2000). Intelligence reframed: Multiple intelligences for the 21st century . Hachette UK.

Gardner, H. (2011a). Frames of mind: The theory of multiple intelligences . Hachette Uk.

Gardner, H. (2011b). The theory of multiple intelligences: As psychology, as education, as social science. Address delivered at José Cela University on October, 29, 2011.

Gottfredson, L. S. (2004). Schools and the g factor . The Wilson Quarterly (1976-), 28 (3), 35-45.

Pearson, M., O’Brien, P., & Bulsara, C. (2015). A multiple intelligences approach to counseling: Enhancing alliances with a focus on strengths. Journal of Psychotherapy Integration, 25 (2), 128–142

Shearer, C. B. (2020). A resting state functional connectivity analysis of human intelligence: Broad theoretical and practical implications for multiple intelligences theory. Psychology & Neuroscience, 13 (2), 127–148.

Visser, B. A., Ashton, M. C., & Vernon, P. A. (2006). Beyond g: Putting multiple intelligences theory to the test . Intelligence, 34 (5), 487-502.

Waterhouse, L. (2006). Inadequate evidence for multiple intelligences, Mozart effect, and emotional intelligence theories . Educational Psychologist, 41 (4), 247-255.

Further Information

Multiple Intelligences Criticisms
The Theory of Multiple Intelligences
Multiple Intelligences FAQ
“In a Nutshell,” the first chapter of Multiple Intelligences: New Horizons
Multiple Intelligences After Twenty Years”
Intelligence: Definition, Theories and Testing
Fluid vs Crystallized Intelligence

7 types of learning styles and how you can to teach them

We all absorb and retain information in different ways. Some people learn faster and more efficiently when content includes visuals like charts, photos, or videos. Others prefer to read and write to retain information. Course creators that know the different types of learning styles can use them to improve student experiences and outcomes. If you know your students and the ways they learn, you can adapt your teaching styles to suit them better.

Creating courses with learning and teaching styles in mind will set you and your students up for success. We’ll go over each learning method and how to identify them. Plus, we’ll provide examples and tips on how to create courses, coaching, and other educational products for each learning style.

What are learning styles?

Learning styles are the methods that people use to understand and remember information. By identifying your students’ learning styles, you can create course materials that suit their preferences.

There is some debate over how many types of learning exist. Most agree that there are four to seven learning styles. We’ll go over each in detail below.

It’s also important to note that one person can have multiple styles. These are known as multimodal learners. They retain information and may thrive using more than one learning style.

The seven types of learning

New Zealand educator Neil Fleming developed the VARK model in 1987. It’s one of the most common methods to identify learning styles. Fleming proposed four primary learning preferences—visual, auditory, reading/writing, and kinesthetic. The first letter of each spells out the acronym (VARK).

We’ll go over the VARK learning styles and three others that researchers and educators have identified below.

1. Visual learning

When you create a course curriculum , consider how many and what type of visuals to include. A 2019 study claims that around 65% of people are visual learners . In other words, visual learners make up the majority of the population. You’ll likely have several in your courses, so keep that in mind when creating materials for it.

To learn best, visual learners need graphs, illustrations, diagrams, videos, and other visuals. You can also teach visual learners better by incorporating these into your lessons.

Infographics
Illustrations
Photographs
Flashcards with images
Virtual whiteboards

Using all of these visuals at once will overwhelm your students. Instead, identify opportunities to display information as a visual and choose the best method for it.

Video courses are the best way to help visual learners. If you’re new to recording videos, you can take a course or watch a tutorial on making high-quality videos for your courses. You can also design and voiceover slideshows.

Visual learners also read and write like other students but may add images to notes, highlight sentences, or draw graphs. It can be helpful to provide them with downloadable versions of course materials so that they can take notes. If you use Teachable, you can easily add digital downloads to your website and courses.

Teachable creator Lauren Hom’s lettering course combines visual and other types of learning styles. Course lessons include videos, live drawing practice, and printable workbooks.

2. Auditory learning

In the same study, researchers found that around 30% of people are auditory learners. Auditory learners like to listen to absorb information. Auditory learners may listen to lectures, podcasts, music, and videos.

They also tend to read their notes aloud to help them understand and retain information or listen to music to study.

You can cater to auditory learners by:

Using music and songs to remember information
Providing audio versions of notes
Encouraging discussions of learning materials

In addition to adjusting your teaching methods to different types of learners, you should also consider the subject.

For example, if you teach guitar online , it will naturally have an audio element. However, you may combine the sounds of different guitar strings with images and videos of them. When you combine different teaching methods, you can cater to multiple learning styles.

3. Reading and writing

Learners who prefer reading and writing thrive with traditional textbooks, handouts, and written assignments. Reading and writing learners are similar to visual learners because they like to see the information on a page.

To teach reading and writing learners, try to present information in one of these forms:

Written instructions
Written assignments

You could also consider creating an ebook to supplement your course material. So if you have a video course, add transcripts to your lessons so students can read along and take notes.

4. Kinesthetic

The kinesthetic learning style is learning by doing. And people who are kinesthetic learners learn better when they’re physically moving and getting hands-on experience.

Kinesthetic learners prefer playing games or doing puzzles as part of the learning process. They tend to enjoy problem-solving and trying new activities to build skills.

Many people associate kinesthetic learning with physical activities and in-person learning environments. However, you can still cater to kinesthetic learners when you create an online course .

For example, many developer courses include coding challenges, hackathons, and other activities where students learn by doing.

Here are some ideas to help you teach kinesthetic learners:

Schedule short breaks for live courses longer than 30 minutes.
Add real-life assignments. For example, a course about plants may add a practical element where students transplant and care for a houseplant.
Create project briefs based on real-life scenarios, so students can practice.
Add physical activity. Some online courses—meditation, yoga, and fitness—will naturally be more interactive.

If you want to add a more physical element, you can also include printables and supplies. Another option is to send materials to students in the mail.

There are many ways to teach kinesthetic learners. One example is the Hands-on Kids Activities Club (HOKA), a membership club for teachers. Every month, teachers get downloadable printables and other resources to create hands-on learning experiences. In one bundle, students learn about an artist and do an art project in that artist’s style.

5. Verbal or linguistic learning

Verbal learners or linguistic learners retain information best by hearing and envisioning words. You may also hear this called verbal-linguistic learning. Similar to an auditory learner, a verbal learner speaks aloud to memorize information better. They tend to be avid readers and may be talented storytellers or poets.

Any of these can help a verbal learner:

Presentations
Flashcards with words
Word games and puzzles

This type of learning is also common in language courses. If you teach students how to speak Spanish, English, French, or another language, verbal learning will come in handy. They’ll want to hear how you pronounce words and practice speaking them on their own.

6. Social or interpersonal learning

Some students learn better alone and others learn better while in groups. Social, also called interpersonal, learners thrive in group discussions and group coaching.

They enjoy speaking in front of groups and asking questions. A social learner will like to give and receive feedback from other students and bounce ideas off others.

Interpersonal learners prefer these types of activities:

Group discussions and activities
Public speaking—presenting their work
Working with a partner
Studying flashcards with a partner
Team-building exercises

7. Solitary or intrapersonal learning

Solitary learners prefer to learn on their own rather than with groups of peers. The word intrapersonal is similar to introvert—they can feel drained from social activities.

These students don’t enjoy group work and would rather get a list of items to study and work independently. Instead of getting ideas and feedback from other students, solitary learners are more introspective. They can get lost in their work and are more hesitant to ask for feedback or ideas from others.

Here are some ideas to help teach solitary learners:

Ask questions to build trust and learn more about them.
Give them space to work independently.
Explain the why behind projects. Solitary learners focus on the future and outcomes, so they like to know the importance of learning different concepts.

Solitary learners are self-starters, so they usually have the determination to complete a course. Even though they prefer learning independently, learning from others has many benefits too.

Sometimes getting a solitary learner to open up more, ask for feedback, and challenge themselves can improve their learning. You could also offer solitary learners coaching or feedback sessions with you to help them develop their learning in a one-on-one environment.

How to identify student learning styles

Most adults have a sense of their preferred learning style. You can ask students or coaching clients which methods they prefer via an intake form when they sign up for your courses or coaching.

To identify learning styles, you can:

Include an intake form on your sign-up pages
Ask new students about their preferred learning styles directly
Observe your students throughout the course
Use assessments to help students figure out the learning style they like best

You can also use an online quiz like the VARK questionnaire to understand new students better. Another option is to create your own assessment and tailor it to your teaching style and course topic. Some sample questions you can use to create a quiz or questionnaire to identify learning styles are:

Do you prefer to work alone or in groups?
Would diagrams and illustrations make it easier to understand a concept?
Is it easier to remember something in words or images?
To understand how a machine works, would you take the machine apart yourself?
Do you remember facts and figures more by hearing them spoken or reading them?

Let students know this is the kind of quiz with no wrong answer. You’ll use the answers to understand what type of learning style they prefer and tailor your teaching to better suit them.

Note that this type of questionnaire works best with coaching or online courses that use cohorts with specific start dates. You can use it to fine-tune your course curriculum for each cohort or personalize coaching sessions.

How to teach different types of learning

As you plan your course, think about how you can accommodate each learning style. For example, auditory learners usually thrive on discussion. On the other hand, learners who prefer to read and write might struggle with group discussions or debates. Discussions can be harder for them because they like to write their thoughts down first before speaking.

To accommodate different types of learning styles, provide several options. In the example above, you could give your students a discussion prompt ahead of time. Reading and writing learners can write talking points down before and auditory learners get the benefits of learning through discussion.

The ui.dev online courses are perfect examples of how to consider different types of learning. Looking at their React coding course, you can see that they provide lessons in two forms—video and text. This way visual, auditory, and reading and writing learners can refer to the materials that they understand best. It also includes kinesthetic learning with practice coding activities and projects where students build real-world applications.

Share your knowledge online

No two students are exactly alike—a learning style that works for some students might not work for others. You can still offer your students or clients a meaningful learning experience.

Identifying how your students learn best helps you teach them in ways that will be the most successful. It also shows them that you care about their learning experience and outcomes. So by considering all the different learning styles, you’ll create an online course that appeals to a larger pool of people.

If you’re ready to share your knowledge with all types of learning styles, you can easily create a course on Teachable . And then you can create online courses, coaching services, and even digital downloads. To get started, sign up for free or choose from one of the paid plans .

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Home > Blog > Tips for Online Students > How to Apply Constructivism Learning Theory in Your Studies

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How to Apply Constructivism Learning Theory in Your Studies

Updated: August 31, 2024

Published: August 30, 2024

college students running toward the college building

Have you ever wondered why some people remember things better when they experience them rather than just read about them? This idea is at the heart of constructivism learning theory. Unlike traditional methods, where students are passive listeners, constructivism emphasizes active learning. It’s all about building knowledge through experiences and reflection. Instead of just memorizing facts, you engage with material, participate in projects, and learn by doing. This method can lead to a deeper understanding of subjects and help you retain information longer.

The beauty of constructivism lies in its flexibility. Whether you’re tackling a science experiment, engaging in a group project, or solving real-world problems, this approach allows you to connect new ideas to what you already know. By actively participating, you’re not just absorbing information but constructing your understanding.

a female college student earning her degree online

What is Constructivism Learning Theory?

Constructivism learning theory is all about how we actively construct our understanding of the world. Rather than just soaking up facts, we build knowledge through experiences, reflection, and social interaction. Imagine learning as a hands-on journey where you connect new ideas to what you already know. This approach emphasizes personal experiences and encourages students to explore, ask questions, and discover new insights on their own. It shifts the focus from memorizing to truly understanding and applying what you’ve learned.

In a constructivist classroom, the student becomes the central figure in the learning process. Instead of just listening to lectures, you’re encouraged to engage with the material, collaborate with peers, and reflect on your understanding. This method not only makes learning more meaningful but also helps you develop critical thinking skills and the ability to apply knowledge in real-world situations. By embracing constructivism, you can transform your educational experience into an active and dynamic journey of discovery.

Who Developed the Constructivism Learning Theory?

The roots of constructivism can be traced back to several influential thinkers. Jean Piaget, a Swiss psychologist, was one of the first to explore how children construct knowledge through interaction with their environment. He believed that learning is a process of building mental models based on experiences. Lev Vygotsky, a Russian psychologist, expanded on this by emphasizing the social aspects of learning, arguing that we learn best through interactions with others.

American philosopher John Dewey also contributed to constructivism by advocating for experiential learning. He believed education should be rooted in real-life experiences and problem-solving. Together, these theorists laid the groundwork for constructivism as we know it today. Their ideas have shaped modern educational practices, encouraging a shift from traditional, teacher-centered methods to a more student-centered approach that values exploration and personal growth. By understanding the contributions of these key figures, we can better appreciate the principles of constructivism and its impact on learning.

How Does Constructivism Differ from Traditional Learning Approaches?

Constructivism and traditional learning methods differ significantly in their approach to education. In constructivism, learning is viewed as an active, personal process where students build their own understanding and knowledge through experiences and interactions. In contrast, traditional learning often involves a more passive approach, where students receive information from a teacher or textbook. Here’s a closer look at these differences:

Knowledge Acquisition

In traditional education, knowledge is often seen as something that is transmitted from the teacher to the student. The student is expected to memorize facts and figures. Constructivism, however, treats knowledge as something actively constructed by the learner. Instead of just absorbing information, students engage with new material and relate it to their existing knowledge, leading to deeper understanding.

Role of the Learner and Educator

In a traditional classroom, the teacher is the authority figure, directing the learning process, while students are passive receivers of information. Constructivism flips this dynamic. Students take on an active role, exploring and discovering new concepts. The teacher acts as a guide or facilitator, supporting students as they navigate their learning journey and encouraging them to ask questions and explore topics in depth.

Error Handling

In traditional settings, errors are often seen as failures to be corrected. Constructivism views mistakes as a natural part of the learning process, offering valuable opportunities for growth and understanding. When students make mistakes, they are encouraged to analyze and learn from them, leading to more effective problem-solving skills and a greater understanding of the material.

Learning Environment

Traditional classrooms are usually structured, focusing on lectures and individual work. Constructivist classrooms, on the other hand, promote exploration, collaboration, and hands-on experiences. Students are encouraged to work together, discuss ideas, and engage in projects that foster a deeper understanding of the subject matter.

Assessment Methods

Traditional assessments often focus on standardized tests and memorization. Constructivist assessments, however, emphasize authentic, process-oriented tasks that reflect real-world applications. Students might be evaluated based on projects, presentations, or portfolios demonstrating their understanding and ability to apply what they have learned.

college students discussing contrivism learning theory in a group

How to Effectively Apply Constructivism Learning Theory in Your Studies

Applying constructivism in your studies can be a game-changer, allowing you to engage more deeply with the material. Here are some practical strategies to incorporate constructivist principles into your learning:

Set Personal Learning Goals

Start by setting specific, achievable learning objectives that align with your interests and prior knowledge. These goals will guide your learning journey and help you focus on areas that matter most to you.

Engage in Active Learning

Active learning is key to constructivism. Participate in hands-on experiments, engage in discussions, and work on projects that allow you to apply new concepts. This engagement helps you understand the material better and makes learning more enjoyable and memorable.

Connect New Information to Prior Knowledge

When learning something new, try to relate it to what you already know. Connecting new concepts and existing knowledge can enhance comprehension and help you retain information longer. This approach encourages you to see learning as an interconnected web of knowledge rather than isolated facts.

Challenges You Might Face When Applying Constructivism to Studying

While constructivism offers many benefits, it can also present challenges for learners. Here are some potential obstacles you might encounter and how to address them:

One challenge is the initial discomfort of taking greater responsibility for your learning. Unlike traditional methods, where teachers direct the learning process, constructivism requires you to be proactive and self-directed. This shift can be daunting at first but ultimately leads to more meaningful learning experiences.

Another challenge is connecting new information to prior knowledge. Finding relevant connections between what you already know and new material can be difficult, especially if the subject matter is unfamiliar. To overcome this, try to identify common themes or underlying principles that link the two. Don’t hesitate to seek help or resources to fill in any gaps in your understanding.

Finally, constructivist learning strategies can be time-intensive. Active learning, problem-solving, and reflection require more time and effort than simply memorizing facts. However, the deeper understanding and retention of knowledge gained through these methods are well worth the investment.

To manage your time effectively , prioritize tasks, set realistic goals, and break your studies into manageable chunks. Persistence and adaptability are key to overcoming these challenges and fully benefiting from constructivism.

How Can You Create a Constructivist Learning Environment for Yourself?

Creating a constructivist learning environment at home or wherever you study is all about setting up a space that encourages exploration, reflection, and hands-on experiences. Start by designing a study area that’s comfortable and free from distractions. Fill it with diverse learning resources, like books, videos, and interactive tools that spark curiosity and encourage you to dive deeper into topics. Incorporating technology, like educational apps and online courses, can provide interactive and engaging ways to explore new concepts and practice skills.

Next, establish routines that support active learning and reflection. Set aside time for hands-on projects or experiments that let you apply what you’ve learned in a practical context. Encourage social interactions by joining study groups, engaging in online forums , or discussing ideas with friends and family. This collaborative approach allows you to share perspectives and gain new insights, enhancing your understanding and retention of knowledge. By creating a supportive and dynamic learning environment, you’ll be better equipped to embrace constructivist principles and achieve a deeper, more meaningful understanding of your studies.

a male college student earning his degree online

Getting the Most Out of Constructivism Learning Theory

To truly benefit from Constructivism Learning Theory, embracing its core principles and incorporating them into your study routine is essential. Start by actively engaging with the material through hands-on activities, discussions, and problem-solving exercises. This active approach makes learning more enjoyable and helps reinforce your understanding and retention of new information. Additionally, focus on personal meaning-making by connecting new concepts to your existing knowledge and experiences. This connection enhances comprehension and makes learning more relevant and memorable.

Reflective practices are also crucial for maximizing the benefits of constructivism. Regularly evaluate your learning process, identifying strengths, challenges, and areas for improvement. This self-awareness allows you to adjust your study strategies and continue growing as a learner. Remember, persistence, creativity, and adaptability are key to overcoming initial challenges and fully leveraging the potential of constructivist learning. By embracing these principles, you’ll better understand your studies and cultivate lifelong learning skills that will serve you well in any field or pursuit.

What are the key principles of constructivism in education?

Constructivism focuses on active learning, where students build knowledge through experiences and reflection. Key principles include learner-centered approaches, prior knowledge, social interaction, and the teacher’s role as a facilitator.

What role does prior knowledge play in constructivist learning?

Prior knowledge serves as a foundation for new learning. Constructivism emphasizes connecting new concepts to what students already know, enhancing understanding and retention.

What are some effective constructivist learning activities for self-study?

Effective activities include hands-on projects, problem-based learning, discussions, and reflective journaling. These methods encourage active engagement and deeper understanding.

What are some examples of constructivist learning strategies?

Examples include collaborative group work, inquiry-based learning, real-world problem-solving, and integrating technology for interactive learning experiences.

What is the role of mistakes in constructivist learning?

Mistakes are seen as opportunities for growth. Constructivism encourages learning from errors, fostering resilience and problem-solving skills.

How can I create meaningful learning experiences using constructivist principles?

To create meaningful experiences, set personal goals, engage in active learning, connect new information to prior knowledge, and reflect on your learning process. This approach fosters deeper understanding and retention.

In this article

At UoPeople, our blog writers are thinkers, researchers, and experts dedicated to curating articles relevant to our mission: making higher education accessible to everyone. Read More

Open access
Published: 30 August 2024

Enhanced “learning to learn” through a hierarchical dual-learning system: the case of action video game players

Yu-Yan Gao ORCID: orcid.org/0000-0002-9410-8922 1 , 2 , 3 , 4 ,
Zeming Fang ORCID: orcid.org/0000-0002-8091-4413 2 ,
Qiang Zhou ORCID: orcid.org/0000-0002-3045-0198 4 na1 &
Ru-Yuan Zhang ORCID: orcid.org/0000-0002-0654-715X 1 , 2 na1

BMC Psychology volume 12 , Article number: 460 ( 2024 ) Cite this article

In contrast to conventional cognitive training paradigms, where learning effects are specific to trained parameters, playing action video games has been shown to produce broad enhancements in many cognitive functions. These remarkable generalizations challenge the conventional theory of generalization that learned knowledge can be immediately applied to novel situations (i.e., immediate generalization). Instead, a new “learning to learn” theory has recently been proposed, suggesting that these broad generalizations are attained because action video game players (AVGPs) can quickly acquire the statistical regularities of novel tasks in order to increase the learning rate and ultimately achieve better performance. Although enhanced learning rate has been found for several tasks, it remains unclear whether AVGPs efficiently learn task statistics and use learned task knowledge to guide learning. To address this question, we tested 34 AVGPs and 36 non-video game players (NVGPs) on a cue-response associative learning task. Importantly, unlike conventional cognitive tasks with fixed task statistics, in this task, cue-response associations either remain stable or change rapidly (i.e., are volatile) in different blocks. To complete the task, participants should not only learn the lower-level cue-response associations through explicit feedback but also actively estimate the high-level task statistics (i.e., volatility) to dynamically guide lower-level learning. Such a dual learning system is modelled using a hierarchical Bayesian learning framework, and we found that AVGPs indeed quickly extract the volatility information and use the estimated higher volatility to accelerate learning of the cue-response associations. These results provide strong evidence for the “learning to learn” theory of generalization in AVGPs. Taken together, our work highlights enhanced hierarchical learning of both task statistics and cognitive abilities as a mechanism underlying the broad enhancements associated with action video game play.

Peer Review reports

Introduction

Humans possess impressive and adaptable learning abilities, as evidenced by the rapid learning of diverse cognitive tasks and the flexible application of learned knowledge to unfamiliar scenarios. Optimizing learning and facilitating generalization has been a fundamental challenge in cognitive science. Traditional cognitive training often exhibits specificity to the training settings (tasks or parameters)–-the improvement in learning are greatly reduced in previously unseen situations [ 1 , 2 ]. If the benefits of cognitive training cannot efficiently generalize across different application situations, its real-world applicability is significantly diminished. Action video game training has been shown a unique training regime that can overcome such limitations. A large body of cognitive science research have shown that playing action video games can directly enhance a wide range of seemingly unrelated cognitive functions, such as attention [ 2 , 3 ], memory [ 4 , 5 , 6 ], perception [ 2 , 7 , 8 ], and reasoning [ 9 ]. Importantly, players are not directly trained on these specific cognitive tasks when playing action video games. Because of these astonishingly broad generalizations, action video games have also been suggested as a useful paradigm for cognitive training [ 2 ] and even for therapeutic purposes [ 10 ]. As generalization is the key for observers to learn infinite knowledge based on finite learning samples, it is of paramount importance to understand the neurocomputational mechanisms of broad generalization induced by action video game play.

Why can action video game play lead to broad generalization in stark contrast to conventional training approaches? Classic theories of learning generalization postulate that an observer generalizes learned knowledge to novel cases by inferring the common constructs between training and application situations [ 11 , 12 , 13 ]. This view assumes that, once common constructs are identified, improvement on novel tasks is immediately achievable. This classic view is often referred to as “immediate generalization” [ 14 , 15 ]. More recently, a new mechanism of generalization has been proposed, which suggests that action video game play induces broad generalizations by enabling observers to “learning to learn” [ 10 ]. In contrast to the “immediate generalization” theory, the “learning to learn” theory predicts that avid action video game players (AVGPs) can quickly capture the underlying structural knowledge of new tasks and thus accelerate learning. Faster learning (i.e., taking less time to achieve good performance) on new tasks, as a hallmark of “learning to learn”, has been found in several recent studies of action video games [ 16 ] and classical perceptual learning [ 7 , 8 , 9 ].

Although “learning to learn” is an elegant theory that can potentially explain the remarkable generalization afforded by action video game play, two issues remain unresolved. First, in addition to predicting faster learning of novel tasks, the “learning to learn” theory has two other key predictions — (1) action video game players (AVGPs) can estimate and understand task statistics more quickly and accurately, and (2) the learned task statistics can in turn guide faster learning of a task. However, the enhanced ability of AVGPs to learn the statistical structure of tasks has not been directly investigated. Second, the “learning to learn” theory also implicitly assumes that, even in an apparently simple task, a hierarchical dual learning system operates: a high-level system for learning task statistics and a lower-level system for learning appropriate responses. Previous studies only assessed observers’ learning behavior as a result of the low-level learning system. It remains unclear whether a high-level learning system exists and how it supports the lower-level response learning. To address these two questions directly, two factors should be considered. First, to demonstrate the superior ability of AVGPs to extract task statistics, we need a task with systematic variation in stimulus statistical regularities and test whether AVGPs are indeed sensitive to such variation. Second, the “learning to learn” ability should be explicitly formulated. In other words, a computational framework is needed to explicitly specify how the correct decisions emerge according to the interactions within the dual-learning system in an online fashion.

In this study, we aim to directly test the “learning to learn” theory using a volatile reversal learning task [ 17 , 18 ]. In this task, participants learn the associations between a visual cue and its corresponding response through trial-by-trial feedback. Importantly, such cue-response associations either remain stable over several trials (i.e., stable block) or change rapidly on other trials (i.e., volatile block, see Methods for details). This volatility variation allows us to assess participants’ ability to learn such task statistics, and, unlike classic learning tasks [ 19 , 20 , 21 ], such an associative learning task also allows us to explicitly estimate participants’ learning rate at both levels. Furthermore, we used the Hierarchical Gaussian Filter (HGF, [ 22 ]) to formulate the “learning to learn” process. In particular, unlike classical reinforcement learning models that only formulate the learning of cue-response associations [ 23 , 24 ], the HGF also specifies a high-level learning process of task statistics (i.e., association volatility). Importantly, changes in the lower-level cue-response associations lead to trial-by-trial updates in the high-level belief of association volatility, and the high-level estimates of association volatility in turn adjust the rate of the lower-level association learning. These bidirectional interactions between a hierarchical dual-learning systems exactly corresponds to the “learning to learn” hypothesis.

Our results show that AVGPs display higher learning rates in the volatile reversal learning task, consistent with previous studies. Most importantly, this higher learning rate is a result of an efficient representation of the association volatility, as evidenced by a higher estimate of association volatility in the AVGPs. All these results are consistent with the “learning to learn” theory of action video game play.

Materials and methods

Ethics and participants.

All experimental protocols were approved by the institutional review board of Shanghai Jiao Tong University. All research was conducted in accordance with relevant guidelines and regulations. Informed written consent was obtained from all participants.

We firstly administered the Chinese version of the Video-Game-Expertise Classification Scheme [ 25 ] to screen for action video game players (AVGPs) and non-video game players (NVGPs). Both English and Chinese versions of the video game questionnaire can be downloaded from https://www.unige.ch/fapse/brainlearning/vgq/ . The basic inclusion criteria require participants to have Chinese as a first or second language; normal or corrected-to-normal vision; no history of mental disorders; not taking significant psychiatric medications; and an age range of 18 to 40 years old. NVGPs need to meet the following criteria:(1) play first/third-person shooter, action/sports, real-time strategy/ Multiplayer Online Battle Arena (MOBA) games, or simulation games for no more than 1 h/week in the past year and the year before; (2) play any other type of games for no more than 3 h/week in the past year; (3) play any other type of games for no more than 5 h/week a year ago. AVGPs need to meet any of the following criteria:(1) play other games for no more than 3 h/week, but play action games for at least 5 h/week in the past year; (2) play action games for at least 3 h/week in the past year, with other games not exceeding 3 h/week, and play action games for at least 5 h/week a year ago; (3) play other games for no more than 3 h/week in the past year, with at least 3 h/week for action games and at least 5 h/week for sports/driving games; (4) play other games for no more than 3 h/week in the past year, with at least 3 h/week for action games and at least 5 h/week for real-time strategy/MOBA games. There exist other inclusion criteria for both groups. More detailed screening criteria can be found in the questionnaire above.

Previous studies have documented several important ingredients of AVGs that enable generalization effect, including (i) decision-making under time constraints, (ii) maintaining divided attention, and (iii) the necessity for prompt transitions between two distinct attentional states (focused and divided) [ 1 , 26 , 27 ]. These factors have also been incorporated into a number of other game genres, including sports and driving games, as well as real-time strategy and MOBA games. We thus also include these genres in the screening of AVGPs.

Based on the filtering criteria, 34 AVGPs (24 males and 10 females) and 36 NVGPs (12 males and 24 females) were recruited to participate in the formal experiment after obtaining their consents. All participants were right-handed and had normal or corrected-to-normal vision. After excluding the subjects who exhibited extreme performance (see data analysis below), data from 33 AVGPs (23 males and 10 females) and 34 NVGPs (12 males and 22 females) were included for further analysis.

Stimulus and task

This experiment was hosted on the Naodao platform ( https://www.naodao.com/ ). Participants accessed the task remotely and completed it online. They received the corresponding participant compensation after the experiment.

Both AVGPs and NVGPs performed the same volatile reversal learning task (Fig. 1 A). Each trial began with a 500 ms fixation period. A cue stimulus (i.e., a yellow or a blue window) was presented. The cue stimulus disappeared after the participant made a keypress response to predict which outcome stimulus (i.e., a cat or a dog) was more likely to appear after the cue stimulus. After the keypress response, an outcome stimulus was presented for 1000 ms. The whole experiment consists of four blocks (80 trials per block) with a total of 320 trials. In each block, the association settings between the cue and outcome stimuli were changed (Fig. 1 B).

Task design and model. A Each trial started with a fixation cross in the center of the screen. After a delay of 500 ms, a stimulus was presented on the screen. Participants were instructed to predict the animal behind the window based on the current yellow or blue window and press the ‘F’ key for a cat or the ‘J’ key for a dog. Immediate feedback and outcome stimuli were provided after each response, lasting for 1000 ms before proceeding to the next trial. B The experiment was divided into four blocks based on the probability of cue-response association: stable (trials 1–80, p = 0.75)—volatile (trials 81–160, with a switching sequence of p values: 0.2–0.8–0.2–0.8)—stable (trials 161–240, p = 0.25)—volatile (trials 241–320 with a switching sequence of p values: 0.8–0.2–0.8–0.2). The yellow line parallel to the x-axis represents trials in the stable blocks, the green line represents trials in the volatile blocks. In the stable blocks. the association probability remained constant within 80 trials, while in the volatile blocks, the probability changed every 20 trials. C Generative process of the HGF. $A$ represents action; $R$ indicates the estimated association probability between the given window cue and the corresponding animal response; $V$ represents the estimated association volatility. $t$ denotes each time point. ${A}_{t}$ depends on ${R}_{t-1}$ , ${V}_{t-1}$ , and parameters $\theta$ , ${\kappa }_{2}$ , $\omega$ . The interconnection between levels is achieved through uncertainty

Here, association is defined as the probability of a cue-response pair. For example, in the first 80 trials, the outcome stimulus cat (or dog) appeared after the cue stimulus yellow window with a probability of 0.75 (or 0.25, respectively). Similarly, the association “blue window-dog” is 0.75. The association settings changed in each block (Fig. 1 B). The key point here is that the association setting is stable (i.e., stable condition) in Block 1 (i.e., trials 1–80) and Block 3 (i.e., trials 161–240) but switches rapidly between 0.8 and 0.2 (i.e., volatile condition) in Block 2 (i.e., trials 81–160) and Block 4 (i.e., trials 241–320).

The stimulus materials for this task were created using Photoshop, and each stimulus material has a resolution of 1080 × 720. The presentation order of the stimuli was pseudorandomized and generated in MATLAB 2020a according to the number of trials in each experimental block and the four cue-response association probabilities. The presentation order of the cues within the experimental block was fixed by a predetermined shuffled order. Thus, each participant received the same stimulus sequence, allowing for a comparable learning process and model parameter estimation. The experimental procedure was developed using jsPsych-6.3.0 ( https://www.jspsych.org/6.3/ ). Participants were informed that these probabilities would change, but were not given with specific information about the four blocks and the exact values of the probabilities.

Computational modeling

The HGF [ 22 ] model is used to analyze the participants’ behavior. We plotted and compared the trial-by-trial generated data from two groups of participants. At the same time, we used t-tests to compare the parameters of the two groups of participants.

Generative model

The HGF can be understood via two distinct components: prediction and update. Briefly, this model formulates the prediction and update process in a two-level hierarchy (Fig. 2 ). The prediction (i.e., generative) process can be seen in Fig. 1 C and the left part of Fig. 2 . Specifically, the higher level of the model represents the estimated association volatility ( $V$ ) (i.e., how quickly the cue-response associations switch), which is updated by

where $\theta$ is a constant parameter which determines the variance of estimated association volatility (the high-level, $V$ ). Estimated association volatility $V$ determines the magnitude for updating the lower-level cue-response association ( $R$ , the estimated association probability between the given window cue and the corresponding behavioral choices in the logarithmic domain).

where ${\kappa }_{2}$ is the top-down influence factor that determines the coupling strength between the association probability (the low-level, $R$ ) and the estimated association volatility (the high-level, $V$ ); $\omega$ is a constant component of the association variance $\left({\kappa }_{2}*{V}_{t}+\omega \right)$ , independent of the state of the estimated association volatility (the high-level, $V$ ). The behavioral action $A$ is generated by the association probability ( $R$ ), and $\mu$ (i.e., correct or incorrect) is the actual outcome the participant received.

where the function $s(\cdot )$ is the sigmoid function with ${\kappa }_{1}$ as the inverse temperature. To simplify our modeling, we fixed the coupling factor controlling the influence of association probability (the low-level, $R$ ) on action (i.e., ${\kappa }_{1}$ ) to 1 .

Overview of the HGF model. The probability at each level is determined by the previous level and parameters. Throughout the paper, we analyzed several key variables of this model. We color labeled the variables of interest and illustrate the figure number where the group differences in the variables are compared to facilitate reading

This model has three free parameters $:$ $\theta$ , ${\kappa }_{2}$ , and $\omega$ .

Trial-by-trial update rule of model parameters

The detailed trial-by-trial update rule of model parameters in HGF has been documented in Mathys, et al. [ 22 ]. Furthermore, this update process is illustrated in the right part of Fig. 2 . Here we provide a short overview and an introduction of the variables and free parameters.

On the t -th trial, the action ( ${A}_{t}$ ) is determined by the actual outcome the subject received ( ${\mu }_{t}$ ), where ${\mu }_{t}\in \{\text{0,1}\}$ indicates the correct/incorrect feedback.

The update of estimated association probability ( ${R}_{t}$ ) depends on the association learning rate ( ${\alpha }_{t}^{R}$ ) and the association prediction errors ( ${PE}_{t}^{R}$ ).

Note that the association learning rate ( ${\alpha }_{t}^{R}$ ) varies trial-by-trial and is determined by association expectation ( ${\widehat{\alpha }}_{t}^{R}$ ) and action expectation ( ${\widehat{\alpha }}_{t}^{A}$ ). The superscript $R$ denotes the variables as the ones operating at the low-level association learning.

The association expectation ( ${\widehat{\alpha }}_{t}^{R}$ ) per se also varies trial-by-trial and is determined by the learning rate of the last trial ( ${\alpha }_{t-1}^{R}$ ) and the upper-level estimated association volatility ( ${V}_{t-1}$ ), where ${\kappa }_{2}$ and $\omega$ are free parameters.

The action expectation ( ${\widehat{\alpha }}_{t}^{A}$ ) per se also varies trial-by-trial and is determined by the action of the last trial ( ${A}_{t-1}$ ).

the association prediction errors ( ${PE}_{t}^{R}$ ) is given by:

The update of the estimated volatility ( ${V}_{t}$ ) depends on the volatility learning rate ( ${\alpha }_{t}^{V}$ ) and the volatility prediction errors ( ${PE}_{t}^{V}$ ) The superscript $V$ denotes the variables as the ones operating at the high-level volatility learning.

where the volatility learning rate ( ${\alpha }_{t}^{V}$ ) consists of three components:

Here, ${\overline{\alpha }}_{t}^{V}$ represents unweighted volatility learning rate of $V$ and varies trial-by-trial:

where $\theta$ is a free parameter. ${w}_{t}^{V}$ denotes a precision weighting factor.

the volatility prediction errors ( ${PE}_{t}^{V}$ ) is given by:

In summary, the estimated free parameters for each participant are ${\kappa }_{2}$ , $\omega$ , and $\theta$ . The variables with subscript “ t ” change from trial to trial, and the three free parameters without subscript “ t ” are fixed values that hold for all trials.

The analysis was performed using the HGF toolbox in MATLAB ( https://translationalneuromodeling.github.io/tapas ). The tapas_fitModel function was used to iteratively fit the model 100 times for each participant, using the Maximum A Posteriori (MAP) method for parameter estimation. Configuration settings, facilitated by functions such as tapas_hgf_binary_config , tapas_unitsq_sgm_config ,and tapas_quasinewton_optim_config , were used to set prior ranges for the parameters. The ranges of priors for the parameters to be fitted are as follows: top-down factor $\text{log}\left({\kappa }_{2}\right)\sim \mathcal{N}\left(\text{log}\left(1\right), 4\right)$ ; association constant uncertainty $\omega \sim \mathcal{N}\left(-3, 16\right)$ ; volatility constant uncertainty $\text{log}(\theta )\sim \mathcal{N}\left(-6, 16\right)$ . All other parameters involved in the code, including their ranges and initial values, follow the default settings in the toolbox.

Statistical analysis

Linear mixed model analysis was performed in JASP 0.18.1.0 ( https://jasp-stats.org/ ), and all multiple comparisons were corrected using the Holm correction in JASP. All t-tests were performed using the Pingouin package in Python and were all two-tailed. In this experiment, participants with an average association learning rate exceeding (or fall below) the mean plus (or minus) two standard deviations of the overall sample were excluded. A total of 4 participants met these criteria. 33 AVGPs and 34 NVGPs were included in the reported results.

Superior low-level learning rate of cue-response associations in AVGPs

Participants performed a volatile reversal learning task (Fig. 1 A). On each trial, a fixation was shown for 500 ms and followed by a cue stimulus (i.e., a yellow window or a blue window). Participants were asked to predict the subsequent outcome stimulus (i.e., a cat or a dog) associated with the cue. Following a keypress response, an outcome stimulus was presented for 1000 ms as feedback. The two cue stimuli and the two outcome stimuli were paired. For example, within a stable block, the cat (or dog) appeared after the yellow window (or blue) window in 75% (or 25%, respectively) of the trials. Such cue-response associations varied across blocks. Importantly, the task statistic is defined as the changing rate of such cue-response associations (i.e., volatility). In particular, in the two stable blocks (Block 1, trials 1–80; Block 3, trials 161–240), the cue-response association settings remained constant. In contrast, in the two volatile blocks (Block 2, trials 81–160; Block 4, trials 241–320), the cue-response associations switched between 0.8 and 0.2 every 20 trials. The key question here is whether participants can learn the stability and volatility of the associations and use this information to guide their learning. Followed by the conventional approach [ 17 , 28 ], we directly fitted computational models (see below) to represent participants’ learning process in this task.

We first asked whether we could replicate the finding that AVGPs learn a novel task faster than NVGPs [ 7 , 10 , 16 , 29 ]. Unlike the conventional reinforcement learning approach that estimates a single learning rate parameter throughout the task [ 30 , 31 ], HGF assumes that participants’ learning rate also varies from trial to trial based on updated beliefs about the task statistics (i.e., volatility). In this task, participants learned the cue-response associations. The trial-by-trial association learning rate ( ${\alpha }_{t}^{R}$ , Eqs. 6 – 8 ) in both groups is plotted as a function of trials in Fig. 3 A.

Comparison of association learning rate between two groups. A The log association learning rate ( ${\alpha }_{t}^{R}$ , Eqs. 7 , 8 , 9 ) required for updating the estimated association probability for each participant. The x-axis represents the trial sequence ( t ), and the y-axis illustrates participants’ log association learning rate ( ${\alpha }_{t}^{R}$ ). The red line represents AVGPs, and the blue line represents NVGPs. The shaded area represents S.E.M across all participants within each group (33 AVGPs, 34 NVGPs). Significance symbol conventions is **: p < 0.01. B Two groups’ association prediction errors ( ${PE}_{t}^{R}$ , Eqs. 6 & 10 ) across trials. The x-axis represents the trial sequence ( t ), the y-axis illustrates association prediction errors ( ${PE}_{t}^{R}$ ). Significance symbol convention is n.s.: non-significant

A linear mixed model (LMM) was built in JASP with Trial as a random effect factor, Group (AVGPs/NVGPs) as a fixed effect factor, Log Association Learning Rate ( ${\alpha }_{t}^{R}$ , Eqs.7–9) in each trial as the dependent variable. We found that the effect of Group is significant, indicating the overall higher learning rate of the AVGPs than that of the NVGPs ( t (21119 ) = 2.852, p = 0.004, Estimate = 0.055, SE = 0.019, CI = [0.017, 0.093]). In summary, Fig. 3 A shows that the AVGPs indeed had a generally higher learning rate than the NVGPs, although the learning rate in both groups varied from trial to trial in both groups.

Because the trial-by-trial update of the association probability ( ${\Delta R}_{t}$ , Eq. 6 ) is determined by both association learning rate ( ${\alpha }_{t}^{R}$ ) and association prediction errors ( ${PE}_{t}^{R}$ , Eqs. 6 & 10 ), we also analyzed the association prediction errors ( ${PE}_{t}^{R}$ ) in both groups and plotted them as a function of trials in Fig. 3 B. A LMM was performed with the Association Prediction Errors ( ${PE}_{t}^{R}$ ) as the dependent variable; Group (AVGPs/NVGPs) as a fixed effect factor and Trial as a random effect factor. We found no significant effect of Group ( t (21119) = -0.036, p = 0.971, Estimate = -0.001, SE = 0.002, CI = [-0.003, 0.003]), suggesting the superior learning in AVGPs is mostly due to the association learning rate rather than association prediction errors.

Higher low-level learning rate in AVGPs is due to high-level association volatility

We have confirmed the overall higher association learning rate in AVGPs. A higher association learning rate ( ${\alpha }_{t}^{R}$ ) leads to a larger update ( ${\Delta R}_{t}$ ) of the estimated association probability. But how did AVGPs develop an overall higher association learning rate in the volatile reversal task? The key aspect of the HGF is that association learning rate is determined by association variance in the last trial ( ${\kappa }_{2}*{V}_{t-1}+\omega$ ), which is further controlled by high-level volatility ${V}_{t-1}$ in the last trial (Eqs. 7 – 9 ). Here, we examine whether higher association variance leading to an increased association learning rate in the AVGPs.

A LMM was performed with Association variance ( ${\kappa }_{2}*{V}_{t}+\omega$ ) as the dependent variable; Group (AVGPs/NVGPs) as a fixed effect factor and Trial as a random effect factor. We found that the effect of the Group was significant, indicating overall greater association variance of AVGPs compared to NVGPs ( t (21119) = 2.516, p = 0.012, Estimate = 0.100, SE = 0.040, CI = [0.022, 0.179], Fig. 4 A). For completeness, in addition to the association variance ( ${\kappa }_{2}*{V}_{t}+\omega$ ) and the association learning rate from the previous trial ( ${\alpha }_{t-1}^{R}$ ), we also compared action expectation ( ${\widehat{\alpha }}_{t}^{A}$ , Eqs. 6 & 7 ) that contribute to the update of association learning rate (Eq. 7 ). We found no significant effect of Group ( t (21119) = -0.071, p = 0.944, Estimate = -0.001, SE = 0.005, CI = [-0.010, 0.009]) . This suggests that the higher association learning rate ( ${\alpha }_{t}^{R}$ ) observed in AVGPs is likely due to their overall higher association variance ( ${\kappa }_{2}*{V}_{t}+\omega$ ).

Association variance and estimated association volatility in two groups. A Participants’ association variance ( ${\kappa }_{2}*{V}_{t}+\omega$ ) across trials. The x-axis represents the trial sequence ( t ), and the y-axis illustrates participants’ association variance ( ${\kappa }_{2}*{V}_{t}+\omega$ ). The red line represents AVGPs, and the blue line represents NVGPs. The shaded area represents S.E.M across all participants within each group (33 AVGPs, 34 NVGPs). Significance symbol conventions is *: p < 0.05. B Participants’ estimated log association volatility ( ${V}_{t}$ ) across trials. The x-axis represents the trial sequence ( t ), and the y-axis illustrates participants’ estimated association volatility ( ${V}_{t}$ ). The y-axis is plotted on a logarithmic scale. The red line represents AVGPs, and the blue line represents NVGPs. Significance symbol conventions is ***: p < 0.001

The association variance ( ${\kappa }_{2}*{V}_{t}+\omega$ ) is determined by the linear addition of two components: a top-down component ( ${\kappa }_{2}*{V}_{t}$ ) and a constant component ( $\omega$ ). The top-down component indicates that a higher estimated association volatility ( ${V}_{t}$ ) leads to a larger update of the association learning rate, where ${\kappa }_{2}$ is the top-down coupling factor. The constant step indicates the default magnitude of the update in the subject. Note that the top-down factor ${\kappa }_{2}$ and the association constant step $\omega$ are considered as traits of each subject and are fixed across trials, while the high-level estimated association volatility ${V}_{t}$ varied across trials.

Next, we sought to understand which factor of association variance contributed most to the increased association learning rate. There were no significant differences in both ${\kappa }_{2}$ ( t (58.569) = -0.236, p = 0.814, Cohen’s d = 0.058, CI = [-0.320, 0.250]) and $\omega$ ( t (64.677) = -0.597, p = 0.552, Cohen’s d = 0.146, CI = [-1.740, 0.940]). A LMM was performed with Estimated Association Volatility ( ${V}_{t}$ ) in each trial as the dependent variable, Group (AVGPs/NVGPs), Block Type (stable/volatile), and their interaction as the fixed effect factors, and Trial as a random effect factor. The “learning to learn” theory predicts that AVGPs should be more sensitive to task statistics (i.e., volatility). Indeed, we found that AVGPs estimated higher association volatility than NVGPs ( t (21116) = 8.453, p < 0.001, Estimate = 0.073, SE = 0.009, CI = [0.056, 0.090]). Post-hoc pairwise comparisons revealed that AVGPs had significantly higher estimated association volatility ( ${V}_{t}$ ) than NVGPs in the second stable block(stable block 2, t (211116) = 3.737, p < 0.001, Estimate = 0.016, SE = 0.004, CI = [0.008, 0.025]) and two volatile blocks (volatile block 1, t (21116) = 2.378, p = 0.017, Estimate = 0.010, SE = 0.004, CI = [0.002, 0.019]; volatile block 2, ( t (21116) = 11.1778, p < 0.001, Estimate = 0.048, SE = 0.004, CI = [0.040, 0.057]) but not in the first stable block (stable block 1, t (21116) = -0.387, p = 0.698, Estimate = -0.002, SE = 0.004, CI = [-0.011, 0.007], Fig. 4 B). This may be because the first block was a stable block. These results show that the AVGPs can detect relatively higher association volatility ( ${V}_{t}$ ) as the task proceeds and then produce a greater trial-by-trial update of the association learning rate, resulting in faster learning of low-level associations. This process is consistent with the “learning to learn” theory that AVGPs can quickly adapt to ever-changing task environments.

Furthermore, we found that the estimated association volatility ${V}_{t}$ during the volatile blocks was significantly higher than that during the stable blocks in both groups ( t (316.125) = 19.862, p < 0.001, Estimate = 0.183, SE = 0.009, CI = [0.164, 0.201]). This result indicates that both groups can indeed recognize the different levels of volatility of the task. This is also consistent with the well-established theory in reinforcement learning that an agent should relatively increase learning rate in a volatile reward environment [ 32 ].

Superior high-level learning rate of tasks statistics in AVGPs

The above results suggest that AVGPs subjectively experience a higher high-level association volatility ( ${V}_{t}$ ) and use this information to increase the low-level association learning rate ( ${\alpha }_{t}^{R}$ ). Here, we further asked how AVGPs learn the task statistics and obtain the higher association volatility. Again, we examined the volatility learning rate ( ${\alpha }_{t}^{V}$ , Eq. 12 ), which indicates how quickly the association volatility ( ${V}_{t}$ ) evolves across trials. The volatility learning rate is plotted as a function of trials in Fig. 5 A. A LMM was performed with Log Volatility Learning Rate as the dependent variable; Group (AVGPs/NVGPs) as a fixed effect factor and Trial as a random effect factor. We found that the volatility learning rate of AVGPs consistently exceeded that of NVGPs’ ( t (211119) = 3.995, p < 0.001, Estimate = 0.081, SE = 0.020, CI = [0.041, 0.120]).

Volatility learning in two groups. A The log volatility learning rate ( ${\alpha }_{t}^{V}$ ) over all trials of the two groups. The x-axis represents the trial sequence, and the y-axis reflects the volatility learning rate. The red line represents the AVGPs, and the blue line represents the NVGPs. The shaded area represents S.E.M across all participants within each group (33 AVGPs, 34 NVGPs). Significance symbol conventions is ***: p < 0.001. B The unweighted volatility learning rate ( ${\overline{\alpha }}_{t}^{V}$ ) of the two groups across trials. The y-axis is plotted on a logarithmic scale. C The precision weighting factor ( ${w}_{t}^{V}$ ) of the association prediction errors of the two groups across trials. Significance symbol conventions is *: p < 0.005. D the volatility prediction errors ( ${PE}_{t}^{V}$ ) of the two groups across trials. Significance symbol convention is n.s.: non-significant

It was mentioned earlier that an advantage of the HGF model over traditional reinforcement learning models is that the precision-weighted learning rates (including the association learning rate and the volatility learning rate) in HGF can vary from trial to trial, allowing more flexible adaptation of individual beliefs to volatilities. According to the HGF model (Eq. 12 , ${\alpha }_{t}^{V}={\overline{\alpha }}_{t}^{V}*\frac{{\kappa }_{2}}{2}*{w}_{t}^{V}$ ), the volatility learning rate ( ${\alpha }_{t}^{V}$ ) is determined by three factors: the unweighted volatility learning rate ${\overline{\alpha }}_{t}^{V}$ (see Eq. 13 ), the top-down factor ${\kappa }_{2}$ introduced above, and the precision weighting factor ( ${w}_{t}^{V}$ , Eq. 14 ) of the volatility prediction errors ( ${PE}_{t}^{V}$ Eq. 16 ). Note that ${\overline{\alpha }}_{t}^{V}$ and ${w}_{t}^{V}$ varied from trial to trial but ${\kappa }_{2}$ is a fixed value in each subject.

The trial-by-trial unweighted volatility learning rate, precision weighting factor, and volatility prediction errors are plotted as function of trials in Fig. 5 B-D. Three LMMs were performed with Unweighted Volatility Learning Rate ( ${\overline{\alpha }}_{t}^{V}$ ), Precision Weighting Factor ( ${w}_{t}^{V}$ ), and Volatility Prediction Errors ( ${PE}_{t}^{V}$ ) as the dependent variables; Group (AVGPs/NVGPs) as the fixed effect factor and Trial as a random effect factor. We found that AVGPs had an overall higher unweighted learning rate ( t (21119) = 5.142, p < 0.001, Estimate = 0.219, SE = 0.043, CI = [0.136, 0.303]) and an overall higher precision weighting ( t (21119) = 2.459, p = 0.014, Estimate = 0.048, SE = 0.020, CI = [0.010, 0.087]) than NVGPs. However, there was no group difference on the volatility prediction errors ( t (21119) = -0.767, p = 0.443, Estimate = -0.003, SE = 0.004, CI = [-0.010, 0.005]).

Taken together, we found that AVGPs can perceive higher association volatility because they can learn volatility per se faster (i.e., higher volatility learning rate) rather than because of higher volatility prediction errors. This higher volatility learning rate is augmented by more optimal uncertainty processing (i.e., higher precision weighting factor).

The theory of “learning to learn” has recently been proposed as a novel mechanism of learning generalization [ 10 ], in particular the broad cross-task generalizations found in avid AVGPs. In this study, we proposed that enhanced “learning to learn” in AVGPs is achieved by an improved hierarchical dual learning system that takes into account both low-level cue-response associations and high-level task statistics (i.e., volatility). 34 AVGPs and 36 NVGPs completed a volatile reversal learning task in which participants should learn both cue-response associations and the temporal volatility of these associations (i.e., task statistics). We used Hierarchical Gaussian Filter (HGF) to quantify both low-level association learning and high-level volatility learning in the two groups and made three main observations. First, consistent with “learning to learn” and previous results, we found that AVGPs indeed exhibit a higher low-level learning rate of cue-response associations. Second, the higher low-level learning rate of associations is primarily driven by a higher high-level volatility on a trial-by-trial basis. Third, we further investigated the evolution of estimated volatility and found that the high-level learning rate of volatility per se is also higher in the AVGP group. These results strongly support the “learning to learn” theory of action video game play and show that AVGPs can quickly learn the intrinsic statistics of novel tasks and use the learned task knowledge to guide low-level learning of correct responses. Our work sheds new light on generalization in action video games and, more broadly, on cognitive training in general.

Two aspects of “learning to learn”

“Learning to learn” has two key components—enhanced learning rate and multi-level hierarchical learning.

Within the framework of “learning to learn”, enhanced learning rate in novel tasks is a new form of learning generalization. The classical theory of learning generalization posits that observers immediately and directly generalize what they have learned by inferring the shared constructs of the trained and generalization task contexts. This classical view is often referred to as immediate generalization [ 14 , 15 ]. However, immediate generalization highly depends on the recognition of shared constructs between training and generalization. This means that learned experience may be limited to some specific task components. In contrast, the “learning to learn” theory emphasizes the general ability to quickly acquire task statistics and facilitate learning in real time [ 7 , 10 , 16 ]. Most importantly, this “learning to learn” ability should not be specific to a particular task component and thus has the potential to produce broad generalizations across different types of tasks. This new form of generalization has recently been discovered in sequential perceptual learning [ 33 ] and has also been proposed to underlie broad generalization associated with action video game play [ 10 , 34 ]. Both cross-sectional and intervention studies have identified the increased learning rate, as a hallmark of “learning to learn”, associated with action video game play in perceptual [ 7 , 16 ], cognitive [ 16 ], and motor learning tasks [ 35 ].

“Learning to learn” also proposes that high-level statistical learning of task structure is the underlying mechanism for increasing learning rate. Hierarchical learning allows individuals to flexibly adjust their learning rates in response to changing environments. The environments we face are often filled with different types of uncertainty [ 17 , 36 ], such as uncertainty about how an reward is obtained and uncertainty about how tasks may evolve. A lack of flexibility in responding to environmental changes is likely to be associated with psychiatric disorders, such as social anxiety disorder and major depressive disorder [ 37 , 38 ]. Traditional reinforcement learning often assumes that the learning rate is a fixed property of an agent [ 39 ]. This means that an agent has the same learning rate across throughout the task, which is obviously suboptimal and inflexible [ 40 , 41 ]. A better approach is to adjust the learning rate according to task statistics. For example, if the task statistics (e.g., the probabilistic mapping between action and reward) change rapidly, an agent needs to increase the learning rate to adapt quickly to the changes. However, if this task statistics are stable, individuals should decrease the learning rate to avoid overfitting to noise [ 36 , 42 , 43 ]. In other words, the hierarchical form of “learning to learn” allows an agent to flexibly adjust learning speed accordingly in different tasks.

The underlying mechanisms associated with enhanced “learning to learn” in AVGPs

We speculate that several unique characteristics of action video games may be the reasons.

First, the fast pace of action video games may lead to superior cognitive functions. Fast-paced games require players to switch quickly between different scenarios or tasks [ 10 , 26 ]. Several studies have shown that AVGPs have greater task switching abilities [ 34 , 44 , 45 , 46 ]. Given limited cognitive resources [ 47 , 48 ], the reduced cognitive cost of task switching allows AVGPs to allocate more cognitive resources to hierarchical learning, leading to better “learning to learn”. The fast pace of action video games also requires players to simultaneously track and store multiple rapid processes and predict future game events in real time. For example, in a first-person shooting game (i.e., Overwatch), a player must quickly determine where other players have previously attacked and predict their possible current and next locations. Training to track and store information is associated with improved working memory in AVGPs [ 49 , 50 ]. Improved working memory allows players to retain task statistics during sequential tasks and respond more quickly and accurately.

Second, the complex spatial environments of action video games promote perceptual sensitivity. Action video games tend to contain highly complex and realistic spatial environments, and this is associated with increased perceptual sensitivity to external sensory events [ 51 ]. Enhanced perceptual sensitivity allows AVGPs to quickly and accurately detect real-time fluctuations or changes in new tasks, thereby improving “learning to learn”.

However, this is a cross-sectional study, and we cannot exclude the possibility that the people with enhanced “learning to learn’ are more attracted by action video games such that they are related. Researchers [ 52 , 53 ] postulated that the capacity to make multilevel predictions and to learn from uncertainties that emerge during gameplay will facilitate the expeditious and efficacious reduction of prediction errors in game scenarios. This will enable players to “feel good” and, as a result, select and persist with such games.

Neural mechanisms underlying enhanced “learning to learn”

What are the neural mechanisms underlying enhanced “learning to learn”? Previous studies have shown that hierarchical learning exist in the human brain. Existing studies have focused on the neural mechanisms associated with different levels of learning rates and prediction errors (PEs). A study combining HGF modeling with electroencephalogram (EEG) found that beta power in the sensorimotor cortex is negatively correlated with volatility learning rate before action execution and positively correlated with association learning rate after action execution [ 54 ]. Another EEG study found that the P300 response in the frontal and central scalp regions is positively correlated with the absolute values of low-level PEs and negatively correlated with high-level PEs [ 43 ]. In other words, beta power in sensorimotor cortex and P300 responses in the frontal and central scalp may serve as neural markers of hierarchical learning. In this study, we found both increased volatile and association learning rate. Our results predict that enhanced “learning to learn” may produce a weaker and stronger beta wave in sensorimotor cortex before and after action execution. Interestingly, these predictions are consistent with two recent EEG studies of AVGPs. In the two EEG studies, the researchers did not find the changes in beta wave power in the frontal lobes before and after movement but found that the variation of beta-wave power is greater before and after action execution in AVGPs [ 55 ]. In addition, beta wave power has been shown to increase significantly during high-intensity action video game activities [ 56 ]. Our findings also predict a stronger P300 response in the frontal and central scalp regions associated with enhanced “learning to learn”. This prediction is consistent with a recent EEG study that identified a greater amplitude of the task-evoked P300 component in AVGPs [ 57 ].

Corresponding brain regions for learning rate-weighted prediction errors at different levels demonstrated in the previous studies [ 58 , 59 ]

The studies combining HGF modeling with functional magnetic resonance imaging (fMRI) have shown that low-level PEs are encoded in dopamine-related regions of the midbrain, including the ventral tegmental area (VTA) and substantia nigra (SN). These regions have been shown to regulate dopamine release [ 60 , 61 , 62 ]. In contrast, high-level PEs are encoded in the basal forebrain, which regulates acetylcholine release [ 58 , 59 ]. These results predict stronger activities in the midbrain VTA and SN (Fig. 6 ). These predictions are consistent with several recent fMRI studies of AVGPs. One fMRI study found stronger activation of reward-related midbrain structures in AVGPs [ 63 ]. Another longitudinal fMRI study showed that action video games can increase functional connectivity within the basal ganglia [ 64 ]. Similarly, some fMRI studies have found elevated activity in the striatum, as part of the basal forebrain, of AVGPs [ 65 , 66 ]. All of these studies suggest that enhanced “learning to learn” is likely to be associated with stronger activation or inhibition in the midbrain and basal forebrain.

In conclusion, this study employed a Hierarchical Gaussian Filter (HGF) model to test 34 AVGPs and 36 NVGPs in a volatile reversal learning task. The results of the study demonstrate that AVGPs indeed rapidly extract volatility information and utilize the estimated higher volatility to accelerate learning of cue-response associations. These findings provide strong evidence for the “learning to learn” theory of generalization in AVGPs.

Availability of data and materials

The source code of Hierarchical Gaussian Filter (HGF) can be downloaded from https://translationalneuromodeling.github.io/tapas . The HGF task and data for each group of subjects, as well as the code used for analysis and plotting, can be downloaded from https://osf.io/sk82r/ .

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Acknowledgements

The authors thank the participants for their support to this study.

This works was supported by the National Natural Science Foundation of China (32100901) and Natural Science Foundation of Shanghai (21ZR1434700) to R-Y.Z.

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Qiang Zhou and Ru-Yuan Zhang co-senior authors.

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School of Psychology, Shanghai Jiao Tong University, Shanghai, 200030, China

Yu-Yan Gao & Ru-Yuan Zhang

Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China

Yu-Yan Gao, Zeming Fang & Ru-Yuan Zhang

Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, 315302, China

Department of Psychology, Wenzhou Medical University, Wenzhou, 325035, China

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R-Y.Z. and Y.G. conceived and designed the study. Y.G. prepared the computer program for the Behavioral task and collected the data. Y.G. and Z.F. analyzed the data. Y.G. wrote the first draft of the manuscript. R-Y.Z., Y.G., Q.Z., and Z.F. revised the manuscript.

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Gao, YY., Fang, Z., Zhou, Q. et al. Enhanced “learning to learn” through a hierarchical dual-learning system: the case of action video game players. BMC Psychol 12 , 460 (2024). https://doi.org/10.1186/s40359-024-01952-x

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Associate Professor Zoë Hazelwood Clinical psychologist and couples therapist Director, Communication, Attachment and Relationship Experiences Research Laboratory Queensland University of Technology

Presenter/Producer: Sana Qadar
Senior Producer: James Bullen
Producer: Rose Kerr
Sound Engineer: Isabella Tropiano

Thanks to freesound.org users SergeQuadrado , Alexadiaz12, frederic.font , andreaskg , soundscalpel.com , mrjotz , craigsmith , theshuggie and tlcolbe .

Sana Qadar, Presenter

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How does attachment shape your relationships? ( Getty Images: Andriy Onufriyenko )

Gery Karantzas: This kind of one liner statement that if you're with an insecure person, your relationship is doomed. That is also a nonsense. Like, I flatly refute that. That is idiocy at its finest. Yeah, the road is going to be a little more rocky, but the road can also be made really rocky by random life events that occur that take two secure people down a path of relationship difficulty that had nothing to do about what each of them brought to the relationship.

Sana Qadar: You've heard of attachment styles, right? If not, there are one of those theories from psychology that have busted through into popular awareness. And just like love languages, which we covered in last week's episode, certain corners of Tik Tok and Instagram love talking about them and analysing them and dissecting them.

Assortment of audio grabs from social media: Today we’re going to talk about the four major types of attachment // Yes, I'm going to blame your parents. I'm a therapist. That's what we do // I say this as an anxious attachment girly myself that is putting in the work to fix it // It literally aches you physically. You've probably got an anxious attachment style.

Sana Qadar: And the thing is, attachment styles really are one of the key foundational concepts of modern psychology. Like they tell us a whole lot about how humans relate to each other in the world, but a lot of the stuff you see on social media or read about is centred on how these styles can impact your romantic relationships.

And this is where a theory that's backed by quite a lot of research can get twisted or misunderstood, or to borrow one psychologist's words, turned into nonsense.

Gery Karantzas: That is also a nonsense. That is idiocy at its finest.

Sana Qadar: I love a psychologist that doesn't mince words. So where exactly does the evidence end and the twisting begin? Today, the origins of attachment theory. Here's a hint. Crying kids have a lot to do with it. And what we sometimes get wrong when we talk about attachment in adult relationships. I’m Sana Qadar, this is All in the Mind.

Archival audio: Yes, we know you're hungry, darling. But first things first. Bath before breakfast.

Sana Qadar: Our parents, or caregivers, are our very first relationships in life. They're the first people we attach to.

Archival audio: You'd think Wendy Allison's heart was breaking, but mother's hardened herself to that.

Sana Qadar: And since we don't get to pick our parents, we don't get to pick our early attachment styles.

Archival audio: She knows that crying provides some physical exercise.

Sana Qadar: And I say early on purpose because, as we'll get into, adult attachment styles are a bit more complicated. So let's just start with the basics.

Gery Karantzas: Attachment theory is seen as a theory of human bonding, how it is that we form strong emotional bonds with others to provide us with comfort, love and protection. And the importance of that is, especially when we are distressed, when we are feeling vulnerable or threatened.

Sana Qadar: This is Gery Karantzas. He's a professor of psychology and relationship science and the director of the science of adult relationships laboratory at Deakin University.

Gery Karantzas: That is how I would by and large describe attachment theory.

Zoe Hazelwood: So anybody who's ever been around. a small child might notice that if that child starts to wander, say, for example, if you're sitting in a coffee shop or a shopping centre or something.

Sana Qadar: This is Dr. Zoe Hazelwood, Associate Professor of Clinical Psychology and Director of Clinical Psychology Services at Queensland University of Technology.

Zoe Hazelwood: The child perhaps just learning how to walk and is a bit excited and starts to kind of stumble off and walk off in the opposite direction, but they get to a point where they stop. And they turn around and they look and they're seeking visual confirmation that their parents are still close by, or whoever it is they're with, their primary caregiver is still close by. And that is the attachment system being activated.

Sana Qadar: And it might seem kind of obvious now to say that children attach to their parents. Like, duh, of course they do. But it wasn't always an obvious or properly understood dynamic from a research point of view anyways. And so if you want to know the origin story of attachment theory, there are two researchers and one very strange experiment that you need to know about. Let's start with researcher number one, a guy called John Bowlby.

Gery Karantzas: So attachment theory has a really long history, right? We're now getting into, you know, well over 70 years since attachment theory was first really being formulated by the individual who formulated it, it was John Bowlby, who was a clinical, you know, psychotherapist and he was especially interested in understanding how separation between a child and their primary caregiver, namely back in that day and age, which was a mother or a mother like figure, what that separation would do to the development of the child. How would it affect personality development?

Sana Qadar: Bowlby was working in the 1950s and 60s, and part of the reason he was interested in this was because not a lot of other researchers were considering how life circumstances could impact a person.

Gery Karantzas: Because a lot of the kind of training that he had and a lot of the discourse that was going on at the time was really around locking in on what was going on in, you know, people's kind of unconscious. And if you really wanted to discover and understand what was going on, you needed to go into the deep recesses of the mind, into the unconscious and into people's dreams.

And something that he was kind of rallying against is he was saying, but what about people's conscious awareness, let alone their unconscious, but even their conscious awareness, and their lived experiences around what was going on in their current lives?

Sana Qadar: So Bowlby was kind of going against the grain and he started to dig deep into how parents care for children and what happens when a primary caregiver is absent, especially in the early years.

He even went so far as to look at animal relationships.

Gery Karantzas: And he started to study birds. And he was noticing that the way that kind of parents were tending to the needs of offspring to survive was in some ways paralleled a little bit what was going on in humans. So the point is that he was starting to draw all of these different things together to start to develop this theory he called attachment theory.

And he spent about a decade doing that before he released his first book. That really is how attachment theory was born.

Sana Qadar: Bowlby's book was called Attachment and it was published in 1969. And one of the things, among many, that make his attachment theory different to the concept of love languages, which we covered in last week's episode, is that another researcher soon came along and tested his theory.

And this is where stuff gets really interesting. Mary Ainsworth is researcher number two in the story. She was an American/Canadian psychologist who was also super interested in the bonds between mothers and babies. She and Bowlby started talking to each other and eventually she developed a test that would demonstrate for the first time how exactly attachment worked and it would identify the various styles of attachment.

So we can thank her for the terms anxious attachment, avoidant attachment, and secure attachment. We'll get into what exactly those mean in a moment. But first, her experiment was called the Ainsworth strange situation.

Gery Karantzas: The strange situation is called the strange situation because in some ways the child is exposed to a strange situation.

Sana Qadar: Here's how it worked. Basically, a mother and a baby would be brought into a research lab and the kid would be about a year old.

And they'd be put in this room that had toys and other things to entertain the kid. But then eventually a stranger would enter the room. And after a few minutes, the mother would leave the room, leaving the baby alone with the stranger.

After another few minutes, the mom would come back and comfort the baby. But then again, for a second time, the mother, and this time also the stranger, would leave the room, leaving the baby entirely alone. Throughout this, researchers were observing how the baby responded in each of these various situations.

But they were primarily interested in how the baby responded when their mother returned. Because that reunion, that reunion, That told them something about the bond the baby had with their caregiver. It told them their attachment style. And so what did she observe from the babies when their mothers came back?

Gery Karantzas: She by and large saw three major patterns. Let's start with the secure pattern. It means that the child feels comfortable to approach the parents. The parent would be able to comfort the child, and the child would feel soothed. So it's like, oh, you're here now. Oh, I feel so much better. You experience relief, distress goes down, and then the child might even go off and start playing with the toys that were in the room, or start exploring the room again.

Sana Qadar: Then there were the kids she observed with an anxious attachment style. And these babies would take way longer to calm down. They would keep crying even when their mothers came back in the room. Gery says it was like the kids seemed frustrated or angry with their mothers.

Gery Karantzas: Now the question is why? The idea is that if the mother reunites with the child, that should be the solution to the child's distress.

Now to be able to understand that, you need to know the caregiving history that that child experienced, which is also what Ainsworth had done. She didn't just do the Strange Situation. She spent six months doing observational assessments of mother baby interactions in the child's home.

Because what she was looking for is to understand what is mom doing and what is child doing that brings about these potential patterns that they ended up observing.

Sana Qadar: In the strange situation, what Mary Ainsworth found was that children who had this anxious presentation had mothers who offered either inconsistent caregiving, so sometimes they were there, sometimes they weren't, or inept caregiving. So for example, maybe if a child has hurt themselves, they would offer a song rather than a band aid, which is very sweet. But what the child really needed at the time was a band aid.

Gery Karantzas: The child goes, wait, I'm injured. Medical attention would likely be sufficient as opposed to us engaging in a joyous song.

The parent is still trying to do something. But the parent gets it wrong. So in both those instances that I've given you, the inconsistent care and the inept care, the child ends up escalating their distress. And they start to become really worried and anxious, like, will you actually give me what I need?

Do you even know what I want? Do you even kind of really love me and check in with me in terms of what I need? So that's where the anxious presentation comes from.

Sana Qadar: Then after anxious and secure attachment, the third style that Mary Ainsworth noted was avoidant attachment.

Gery Karantzas: So attachment avoidance is when on reunion behaviour, the child really doesn't engage with the mother.

You know, they might ignore mom. They might not get close to mom. They wouldn't have that upward distressed presentation that we were just talking about for anxious babies. So the reason that these children come across like that is because their caregiving history, what Ainsworth had noticed was consistently rejecting.

Sana Qadar: Gery says that means anytime the child was distressed or needed their parent's help, the parent would dismiss them. Like, you don't need my help. You'll be fine. Or you're wasting my time. Sort it out yourself.

Gery Karantzas: Or you're just being a big baby. You know, they might shame the child, but ultimately it's all communicating a message of rejection.

So what happens is the child learns that anytime that I experience distress, it's unlikely to be met because my parent doesn't care. So what the child then learns is rather than saying something, voicing your needs, voicing your worry, keep it in. The only person you can really trust is yourself.

Sana Qadar: So it was Mary Ainsworth's strange situation that showed how attachment operated and that there were distinct types of attachment. And for a long time, it was thought that the attachment style you develop as a kid stayed with you for life. But from the eighties onwards, researchers started looking at how attachment operated in adults. And it turns out it's not so straightforward.

Sana Qadar: And so then zooming out from her study in that history, if those are the three types that were observed in children, is that more or less what we see in adults?

Gery Karantzas: Yes - in the early work. But there's a big caveat around that one. And this is one of the big things that we need to talk about in terms of attachment styles that we need to pin to the mast.

And this is where the kind of pop culture stuff comes in and what everyone gets out there and says and the rest of it. What we've been describing, and the way you've kind of framed it to me, is around these styles as if they're categories.

Sana Qadar: Right.

Gery Karantzas: So, now, forget that. Forget the idea that attachment styles are categories.

Sana Qadar: Okay.

Gery Karantzas: Research into attachment that I've been involved with for nearly, you know, 20 years now, back in 1998, made some bold kind of claims and did some really fantastic work to suggest when it comes to adult relationships, these fixed categories don't exist in the same way that we think that they do what it is, is that people vary on two broad continuums. One of those continuums is attachment anxiety. And the other continuum that they vary on is attachment avoidance. And you can sit high, low and anywhere else in between. But what we know is in the general population, when we sit there and do these studies, that most people are sitting on moderate levels of attachment insecurity.

Gery Karantzas: Now, the reason that that's relevant is because people aren't necessarily, when we say people are anxious in their attachment, not all people look the same in terms of attachment anxiety, because it ain't a category. You vary as to how much anxiety you might experience. You vary as to how much avoidance you might experience.

And that's more than just academic. It absolutely matters on the ground. And to be fair, that is something that is completely lost when people talk about attachment styles, but yet it absolutely matters on the ground. And it absolutely matters to those of us that do, that do couples therapy and other forms of relationship therapy with those differences where you sit on those sliding continuums is going to matter.

Sana Qadar: Now, this would be a good point to describe what exactly these attachment styles look like in an adult relationship, how they manifest. And so let's start with anxious attachments, since we're already talking about that. And here's what someone really high on that dimension might act like.

Zoe Hazelwood: So this is a person who, as the name suggests, and I'm talking here in stereotypes and in very big generalisations, this is somebody who may experience quite a lot of anxiety in their relationship.

Sana Qadar: This is Dr. Zoe Hazelwood again, associate professor of clinical psychology at Queensland University of Technology.

Zoe Hazelwood: Usually because there's some degree of fear of abandonment or they have a negative perception of themself.

Gery Karantzas: Because of that, we also have this preoccupation with relationship partners. You're thinking about, are they paying attention to me? Where are they? Why did they go out instead of staying home with me? Why are they talking to that person instead of, instead of their attention being on me, do they actually like that person? Why are they not going out with me tomorrow? Why are they staying and working late?

Sana Qadar: Right. A lot.

Sana Qadar: That's anxious. Avoidant attachment is kind of like the opposite of anxious, but I should note it's often been the most maligned of attachment styles, both on social media and in a book called Attached that was released about a decade ago that brought the entire concept of attachment styles to the wider public. So here's what someone high on avoidance can look like.

Gery Karantzas: The kind of characteristic features are excessive self reliance. The other is a discomfort with emotional closeness. Put another way, it's me having a hard time being vulnerable and open in relationships. Some that are high on the dimension can also have what we call relationships as secondary.

That is that they dismiss the importance of relationships in their life. Their primary concerns might be around career, might be around hobbies, other things.

Zoe Hazelwood: Let's say, for example, if it's from a fear perspective, where the avoidantly attached person is, is fearful of the, the harm that the connection to another person might do.

So there's a sense of not wanting to kind of risk that vulnerability because that perhaps they've been hurt before. Perhaps relationships inherently are seen as a pretty dangerous playground for them. And the alternative to that is this more dismissing style still coming from a place of longing.

I mean, the reality is we all want to connect with people. We all have this inbuilt social system within us that genetically kind of dictates that we need to be social creatures. We don't function as a species without connecting to other people. So even an avoidant person is still longing for connection, but it seems really dangerous.

Sana Qadar: And then finally ..

Zoe Hazelwood: Here's what secure attachment can look like in adult relationships. The secure attachment is the person who's able to recognise that relationships are going to have ups and downs, that there are going to be times when you might need to be a bit more vulnerable, and they can tolerate that.

Gery Karantzas: These people are very trusting of others. They have a pretty healthy view of themselves, that they are worthy of love, and are comfortable in giving love and being intimate with other people.

Sana Qadar: Yeah, and so these secure adults, are they like some golden minority?

Gery Karantzas: Most of us are. They're not as much of a minority as you would think. You know, around 60 percent of the population, give or take, is in a secure relationship.

Sana Qadar: Oh, wow. So the majority.

Gery Karantzas: Yeah, but also if we take it from an evolutionary perspective, which Bowlby also drew on, we would expect that the kind of from evolution, developing secure relationships, you know, has a major advantage. Because also if we had this underlying evolutionary kind of need to bond, we want to increase the success of that bonding. And so we need it to go right most of the time than not.

Sana Qadar: So that's really good news. And that brings us back to the main myth that the style you develop as a child is the style you have for life. Here's why that's not totally correct.

Zoe Hazelwood: So there's actually literature that shows that. That parents, like say if you've been raised in a, you know, heteronormative relationship of like mum and dad, that you might have a different attachment style with your mum than what you do with your dad. You might have a different attachment style with partner A than what you do with partner B because history.

It has a huge role in or plays a huge role in how you connect with future partners. So if you've had a bad experience, then you carry that with you and that can impact on how you trust somebody in the future. But if you've had a bad experience and you've worked on it and you've come out the other side really powerful and strong and feeling good about yourself and everything, then it probably won't have as big an impact.

Gery Karantzas: So the idea that there is this perfect one to one correspondence, if I was anxious as a baby, I'm going to be anxious in my relationship when I'm in my 30s.

Gery Karantzas: You can't turn around and draw a straight line and say, oh yes, that's it, you're destined for that.

Sana Qadar: Right, or my mum didn't pay attention to me as a kid and that's why I'm avoidant as an adult.

Gery Karantzas: For some people that is likely to play a role and it would play a role to some extent. What the research usually finds is that attachment styles are moderately stable. That means. There is a core part of our attachment style that can kind of stay the same, but there is also quite a bit of movement.

Sana Qadar: Right. Okay. There's also this idea I've seen out there on like social media that attachment styles can sort of tell you how doomed a relationship is likely to be. So for example, like avoid avoidantly attach people altogether. Is that unfair? Is there anything to that?

Gery Karantzas: Like so many things that we study, it's a complicated story.

Typically attachment styles are consistently associated with negative outcomes in relationships. So you would go, ah, well that, that then means that what people are saying is true. Avoid at all costs. Yeah. The question is how strong are those associations? And they're not that big that you would say it's the be all and end all.

Right. It can mean that relationships can be more complicated. It can mean that you need to put more work into them. But again, this kind of one liner statement that if you're with an insecure person, your relationship is doomed. That is also a nonsense. Like I flatly refute that. That is idiocy at its finest.

Yeah. The road is going to be a little more rocky, but the road can also be made really rocky by random life events that occur, that take two secure people down a path of relationship difficulty that had nothing to do about what each of them brought to the relationship.

Sana Qadar: So the TL;DR version of all this – too long didn't read, to use internet speak – is that sometimes Things are just a bit more complicated than social media might suggest, which I'll admit isn't exactly breaking news. Like social media simplifies who knew. But the thing I'm left wondering is given the enduring popularity of concepts like attachment styles and love languages, whether we risk pathologising our relationship behaviours more.

Zoe Hazelwood: I think there's probably some truth to that. I think that inherent in that sort of belief is that there is a naturally right and a naturally wrong way of doing things. And I think that what would be a more appropriate way of looking at it is to say, these are All things that everybody's likely to experience from time to time. And maybe it's about recognizing that it's like a coping mechanism.

And I think we have a tendency to want to do that, to label things as good or bad, because that helps us to feel better about what we're doing and what we're seeing and what we're tolerating.

Sana Qadar: Yeah. And then do you have any thoughts on why attachment styles in particular has become so important? So popular on social media.

Zoe Hazelwood: I think we are naturally the type of species that don't like surprises. We like to know that we can predict what's around the corner, particularly when it comes to our relationships. And I think there's a big part of helping us feel like we know what we're up against a little bit more. If we feel like we know this information, like things like attachment styles, love languages, that sort of stuff, it contributes to what we might think is the predictability of something that is inherently unpredictable, which is the behaviour of other people. So it's almost a bit like. The belief that you can regain a little bit of control.

Sana Qadar: Yeah. Yeah. And I guess also it helps you kind of understand yourself a bit better, though. That's right. Like if you've never come across this before. You're learning online, oh, this is why I'm the way I am. It's my attachment style. That's kind of a revelation.

Zoe Hazelwood: That's right. It can be. And there are people who, you know, that's, that's actually. a good point, you know, this notion that people can finally feel that they have words to describe something that has been indescribable up to this point in time, or that insight has really helped them to know themselves better. And that's, that's almost always a positive, right?

Sana Qadar: Zoe tells me another example that shows how social media and pop culture sometimes gets things right.

Zoe Hazelwood: The other area that I do a huge amount of research in, in relationships is mental load, right? And one of the first things that ever really got mental load kind of onto people's, into people's awareness was a cartoon that was originally written in French by a blogger that was a sort of a pictorial kind of representation of what life was like when hubby was bringing, you know, Guests from work home for dinner at the last minute, and the wife had to basically get everything ready. Right. And it was very stereotypical. And this wasn't that long ago, I might add.

Sana Qadar: I feel like I might have a vague memory of this cartoon, yes.

Zoe Hazelwood: And so that essentially, that whole concept of mental load started with. A blog post, you know, started with social media essentially. And then it grew into something that was a recognised, but at the same time also very under researched area of that contributes to people's wellbeing in relationships.

Gery Karantzas: We're pathologising everything.

Sana Qadar: I'm going to give the final word on how social media discusses concepts like love languages and attachment styles to Gery Karantzas though, because he has some good advice on what you can do if all of this has you questioning or pondering the quality of your own relationship.

Gery Karantzas: Not only that, but we're also putting these huge pressures and expectations around what a relationship should look like.

Sana Qadar: Yeah.

Gery Karantzas: But we're putting more demands about how everyone can strive for the most amazing relationship, about how everyone can strive to be the best version of themselves. And you need to be a better parent and you need to be better at work and you need to be stronger, faster and higher.

But then at the same time, we turn around and say, oh, but please be kind to yourself and be self-compassionate and be okay with your failings. I don't know. But in there, I hear a heavily confused message.

So my answer to it would be this. If you really want at the essence of what is going to make a relationship work. Whether you want to focus on attachment styles, love languages, whatever it is that you might look at, the big thing you need to be focusing in on is, is there anything that you do in your relationship that makes your partner respond in a certain way, good, bad, or otherwise? And is there anything that your partner does that triggers something in you that makes you feel either more positive or negative.

That's the stuff you should be focusing on in your relationships. Am I actually with someone who by and large has got my back? Are they kind? Are they nice? Am I confident that they love me? I might have my own hangups about that, but if I just pull back a little bit from that, am I with someone who really is kind of there for me? Like these are really basic questions that go beyond any concept, any fad that's out there.

Ask simple questions that get at the heart of whether I'm in a relationship that is good for me and whether we are good for each other.

Sana Qadar: That is Gery Karantzas, Professor of Psychology and Relationship Science and the Director of the Science of Adult Relationships Laboratory at Deakin University. You also heard from Zoe Hazelwood, Associate Professor of Clinical Psychology and the Director of Clinical Psychology Services at the Queensland University of Technology.

Now, both Zoe and Gery appeared in our previous episode on love languages. So if you're interested in that concept and finding out whether it's grounded in evidence or not, it'll be the previous episode on your podcast feed. It's called ‘the false promise of love languages’. That's it for All in the Mind this week.

Thanks to producer Rose Kerr and senior producer James Bullen. And I'm Sana Qadar. Thank you for listening. I will catch you next time.

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Learning Styles: An overview of theories, models, and measures
Although its origins have been traced back much further, research in the area of learning style has been active for--at a conservative estimate--around four decades. During that period the intensity of activity has varied, with recent years seeing a particularly marked upturn in the number of researchers working in the area. Also of note is the variety of disciplines from which the research is ...
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Visual. Auditory. Text. Kinesthetic. Learning styles are a popular concept in psychology and education and are intended to identify how people learn best. VARK learning styles suggest that there are four main types of learners: visual, auditory, reading/writing, and kinesthetic. The idea that students learn best when teaching methods and school ...
Learning Styles
The term learning styles is widely used to describe how learners gather, sift through, interpret, organize, come to conclusions about, and "store" information for further use. As spelled out in VARK (one of the most popular learning styles inventories), these styles are often categorized by sensory approaches: v isual, a ural, verbal [ r ...
Learning Theories In Psychology & Education
There are five basic types of learning theory: behaviorist, cognitive, constructivist, social, and experiential. This section provides a brief introduction to each type of learning theory. ... Learning Theories. Kolb's Learning Styles and Experiential Learning Cycle. Reviewed by Olivia Guy-Evans, MSc. Learning Theories. Montessori Theory of ...
Learning Styles: What are They, Models and Discussion
Neil Fleming's Model of Learning Styles. Dr. Neil Fleming identified four learning styles in the 1980's. These four styles came to be known as the "VARK" model of learning styles. This model describes the sensory preferences of learning. It is built on earlier notions of sensory processing, such the VAK model.
Is learning styles-based instruction effective? A comprehensive
The learning styles hypothesis is arguably more important to teacher education than any other field because what tens of thousands of pre-service teachers learn in certification programs and subsequently take with them into the classroom can potentially impact the instruction of millions of k-12 students over the decades they teach. But, like ...
Gardner's Theory Of Multiple Intelligences
A common misconception about the theory of multiple intelligences is that it is synonymous with learning styles. Gardner states that learning styles refer to the way an individual is most comfortable approaching a range of tasks and materials. Multiple intelligences theory states that everyone has all eight intelligences at varying degrees of ...
7 Types of Learning Styles and How To Teach Them
The seven types of learning. New Zealand educator Neil Fleming developed the VARK model in 1987. It's one of the most common methods to identify learning styles. Fleming proposed four primary learning preferences—visual, auditory, reading/writing, and kinesthetic. The first letter of each spells out the acronym (VARK).
Towards a Computational Theory of the Brain: The Simplest Neural Models
6. Algorithms for several of the most basic and initial steps in language acquisition in the baby brain. This includes an algorithm for the learning of the simplest, concrete nouns and action verbs (words like "cat" and "jump") from whole sentences in basic-NEMO with a novel representation of word and contextual inputs.
How to Apply Constructivism Learning Theory in Your Studies
Getting the Most Out of Constructivism Learning Theory. To truly benefit from Constructivism Learning Theory, embracing its core principles and incorporating them into your study routine is essential. Start by actively engaging with the material through hands-on activities, discussions, and problem-solving exercises.
Enhanced "learning to learn" through a hierarchical dual-learning
In contrast to conventional cognitive training paradigms, where learning effects are specific to trained parameters, playing action video games has been shown to produce broad enhancements in many cognitive functions. These remarkable generalizations challenge the conventional theory of generalization that learned knowledge can be immediately applied to novel situations (i.e., immediate ...
What we get wrong about attachment styles
Attachment theory is a foundation of modern psychology — a framework for understanding human relationships and how we connect other people. ... what we get wrong about attachment styles. Plus ...