Ed. Note: Today we present a guest post from Josh Cuevas, a cognitive psychologist and assistant professor in the College of Education at the University of North Georgia. Enjoy!


Breaking the cycle

Since early on in graduate school when I began studying cognition, I’ve followed the learning styles movement because it was such a powerful phenomenon. It took hold rapidly, seemingly overnight, at all levels of education. And, like so many fads in education and science, it created a big-money industry involving conferences, training seminars, paid speakers, how-to manuals, and a variety of other mediums, inevitably linked to a profit in some way. Yet in the peer reviewed studies I was sifting through, evidence for learning styles was nowhere to be found. And more than a decade later I’m still looking for it.

Today when I suggest to students that learning styles are no more than a myth, I can hear their collective jaws drop, regardless of whether they’re undergraduates or graduate students, because learning styles have been preached to them the entire time they’ve been in school. The graduate students concern me the most because they’re supposed to know the research. And I used the term “preached” because these students have been convinced via no more than word of mouth, are asked to accept the information based on faith, and many come to hold a strange religious-like fervor for the concept. That’s not how science works and it shouldn’t be how education works.

It has been no easy task combating this common misconception in college classrooms, particularly when it is reinforced in textbooks, by other professors (who are also supposed to know the research), and in public schools where students do their internships. The research we’re doing at the University of North Georgia on learning styles has two purposes – it allows us to collect data on the effects of learning styles and contrast it to a stronger model, dual coding, but it also lets us demonstrate, in real time, to students who will one day be teachers how what they’ve long believed to be true simply does not work when put to the test.

Emergence of a myth

It’s no secret that popular culture has become enamored with a number of brain-based propositions that have no basis in reality, such as the idea that humans only use 10% of our brains or that personality type is partially dictated by the dominance of one of the two hemispheres of the brain.

And that has bled into the world of so-called research-based education. Over the past two decades it has become common at all levels of education to incorporate these “brain-based instructional practices”, with many educators and public figures (but seldom actual researchers) touting their benefits as a revolutionary trend in human learning. No such method is more ubiquitous than learning styles, derived mainly from Gardner’s multiple intelligence hypothesis.

I use the term “hypothesis” because multiple intelligences have gone largely untested, and psychologists and neuroscientists generally have not come to accept the model, yet it has spawned practices like learning styles that have been widely adopted in many educational settings, from k-12 through college classrooms, including business and medical schools. Yes, I said medical schools, and not holistic ones.

I know of public school principals who ask their teachers to base the whole curriculum on learning styles. And there are schools that have charters entirely devoted to teaching through learning styles or multiple intelligences. Considering that a doctorate is a common prerequisite to becoming a principal these days, one would have to question the quality of those educational “leadership” degrees when the senior administrator in the school is apparently unaware of the (lack of) research behind their favorite instructional method.

So what does that research say?

Where’s the evidence?

Five years ago a team of highly respected cognitive psychologists (Pashler, McDaniel, Rohrer, & Bjork, 2009) published what should have been a bombshell in a rational world. They identified the type of evidence necessary to confirm the learning styles hypothesis and then went about searching for studies that could provide that sort of evidence, ultimately finding none. None.

They pointed out that proponents of learning styles instruction contend that if the mode of instruction is matched to a student’s preferred mode of learning then learning will increase or accelerate, what’s known as the matching hypothesis or meshing hypothesis.

In order to show that the matching hypothesis actually works, an experimental design must be used and the results must show a significant interaction effect in each condition. This means that someone identified as a visual learner will learn more when presented with visual information, while an auditory learner will thrive in response to auditory information. While there are a variety of learning styles frameworks out there, none of which has any real support, the practice is most likely to take the form of delivering instruction in visual, auditory, and kinesthetic modes to match students’ visual, auditory, or kinesthetic learning preferences.

What Pashler et al. (2009) found was that there was virtually no research at all to support the existence of learning styles or their impact on student learning. A handful of other researchers have recently examined the literature and have come to similar conclusions (Bishka, 2010; Fridley & Fridley, 2010; Kirshner & van Merrienboer, 2013; Mayer, 2011; Norman, 2009; Riener & Willingham, 2010; Rohrer & Pashler, 2012; Scott, 2010).

What do stronger studies say?

Yet this wave of peer-reviewed articles questioning the validity of the practice has had little impact on popular perception. There are studies that appear, on the surface, to support the use of learning styles, but the vast majority of the research published in favor of learning styles has been comprised of correlational studies that could not test the matching hypothesis, identify interaction effects, or show causation. Many other favorable studies were published in predatory, pay-to-publish journals with highly questionable publishing standards, essentially a veritable who’s who of Beall’s List infamy.

The strongest empirical studies published on the topic since 2009 have shown a consistent trend – they have found no effect of learning styles or the matching hypothesis (Allcock & Hulme, 2010; Choi, Lee, & Kang, 2009; Kappe, Boekholt, den Rooyen, & Van der Flier, 2009; Kozub, 2010; Martin, 2010; Sankey, Birch, & Gardiner, 2011; Zacharis, 2011). Two experimental studies did claim to find that elusive interaction effect, but one of those tested different behavioral outcomes rather than academic learning (Mahdjoubi & Akplotsyi, 2012) and the other used such a small sample size (N=39) and only a single 15-minute treatment (Hsieh, Jang, Hwang, & Chen, 2011) so the results would need to be replicated if we are to trust their credibility, particularly in light of so many other contradictory findings.

Ultimately, while no one denies that there are cognitive and personality differences in individuals, the current evidence casts serious doubt on both the existence of learning styles and, if they do exist, their ability to influence learning.

How about dual coding?

An alternative theory, dual coding, is more strongly supported by experimental research but somewhat paradoxically less well known and less likely to be intentionally used in a learning environment. As you may have noticed, I used the term “theory” here because there is actually a good deal of support behind this one.

While we are still unsure of the exact mechanisms that govern dual coding, the basic idea is that there are two separate pathways for encoding information into memory, one verbal and one visual. Unlike learning styles, dual coding has real potential to impact learning because it goes to the core of how humans remember things.

It has been fairly well established that mental images and visual representations are associated with higher levels of recall than verbal information alone, and this contradicts the learning styles hypothesis that suggests that auditory and kinesthetic learners should remember more information if it’s perceived through sounds and touch, respectively. Essentially, the learning styles hypothesis and dual coding theory cannot both be right; they are mutually exclusive.

Consider this scenario: Most of us have sat through a presentation or training session when the instructor has displayed a Prezi or PowerPoint with slides filled with long sentences and paragraphs we were expected to read as he or she lectured. Perhaps we were expected to take notes. Most of us recognized this wasn’t effective (even for the “auditory/linguistic learners”). It’s not effective because the left hemisphere of your brain that deals with language is sent into cognitive overload with too much verbal information. You simply can’t process all the written and spoken language at the same time. But if the slides are comprised of mostly pictures with just a few important terms, all of which closely mirror the discussion, then it’s a much different experience. We are actually prone to retain much more information in the latter scenario. Why is that?

We’ve found that conceptual knowledge is widely distributed among neural networks throughout the brain, but the pathways connecting those networks appear to be separate, particularly for auditory and visual stimuli. Some researchers explain this in terms of the different tasks that the two cerebral hemispheres are responsible for (Jessen et al., 2000). From this perspective, dual coding can be indirectly traced to split-brain surgeries performed in the 1940s to sever the corpus callosum, the part of the brain that connects the two cerebral hemispheres, in patients who experienced life threatening seizures. Once the hemispheres were split it allowed scientists to examine the function of each one in isolation, which produced a great deal of information on lateralization (what each side of the brain is responsible for).

What we know now, with a high degree of certainty and from many different lines of research, is that the left hemisphere is responsible for most language function while the right is responsible for most spatial reasoning and visuospatial processing (Gazzaniga, 2005). So the right side would be more helpful to a caveman during the hunt as he used his vision and tracked the location of his prey, and the left side would be more useful when he was talking about it around the campfire afterwards.

But for modern day humans, language holds an even more prominent role in learning. Nearly everything we learn is processed via language and our left hemisphere, including almost all academic information. Until we can name a concept or explain how it relates to other concepts, it’s almost impossible to retain or have any lasting memory of it and therefore put it to any use.

But our brains are limited and prone to cognitive overload which causes us to dump information if too much of it is fed into working memory, our “here-and-now” focus of attention. Think about what it would be like if your spouse were to talk to you about dinner while two kids explained their encounter with the neighbor’s puppy as you’re trying to read this. Not so good for processing.

So this view of dual coding predicts that there are parallel pathways for retaining information – one for language on the left side and another predominantly for visuo-spatial information on the right side. And if the visuo-spatial centers on the right are activated during the process then the combined power of bringing both hemispheres into use will increase our ability to retain information without pushing us into cognitive overload. When imagery and language are used together, less activation of language centers occurs than when language is the only source of information (Mazoyer, Tzourio-Mazoyer, Mazard, Denis, & Mellet, 2002). This suggests that the two hemispheres are sharing the load, and that’s a good thing for memory.

Does concreteness help?

The majority of studies on dual coding have contrasted retention of abstract words to retention of concrete words. The rationale for this is that abstract terms like “justice” or “love” are not easily linked to visual imagery and should be processed almost solely in the language centers in the left hemisphere. Conversely, concrete words such as “hammer” or “building” are associated with visual concepts, and imagery can easily be used when encoding or retrieving them. When we hear them we “see” them as well.

Bauch and Otten (2011) recently found that information that is strictly conceptual (i.e. a word with no visual connection) is less likely to be retained than words that have perceptual traits and can be imaged.

While it is true that visual information is handled by both hemispheres, our spatial perceptions are normally entirely dependent upon vision. There are very rare instances of blind individuals who use echolocation to create spatial awareness, and you might use your hands to feel around in the dark when the power goes out at night, but these tend to be extreme exceptions. For most of us spatial reasoning is dependent upon vision; we’ve got to see to know where we are, and therefore concrete words that elicit images should produce more activity in the right hemisphere.


Of the studies that have tested dual coding using neuroimaging, a number have found that indeed more activity appeared in the visuo-spatial regions in the right hemisphere when participants processed concrete words. Jessen et al., (2000) used fMRI and determined that the lower right parietal lobe showed stronger activation when participants thought about concrete words. However, the areas that were activated were associated not with visual imagery but with spatial imagery, causing the researchers to conclude that the participants were perceiving the images in 3-dimensional space rather visualizing them in a 2-dimensional photo-like image.

Mazoyer et al. (2002) found that participants’ recall of concrete words was better than that of abstract words, and PET analysis showed activation in visual regions when participants processed those concrete words. This supports dual coding theory in that the extra visual information of concrete words improved recall, as opposed to limiting it due to cognitive overload.

Similarly, Welcome et al. (2011) used EEG and found an effect of concreteness. When participants engaged in either imagery or linguistic tasks, different patterns were revealed in the spatial and language centers, suggesting the participants processed those words in different areas of their brain. These studies generally showed that participants had greater retention of the words that incorporated both hemispheres.

However, other studies have raised doubts about whether dual coding is related to the different responsibilities of the two hemispheres or suggest that both are involved in all processing, just in different ways (Shen, 2010). And others (Cowan & Morey, 2007; Morey & Cowan, 2004) have also argued that there may actually be only a single storage component to memory rather than dual pathways, therefore calling the basic premise of dual coding into doubt. So the debate is still open.

Preliminary findings of our research

At the University of North Georgia we are testing the assumptions of both learning styles and dual coding. But rather than contrasting participants’ memory of concrete terms with that of abstract terms, we instead presented them with scenarios filled with concrete terms and prompted them to encode the information using either imagery or auditory means.

The initial findings in our experiment are very clear and appear to show no interaction effect when a student’s preferred learning style is matched to the form of instruction they receive. In other words, the auditory learners did not show better retention when information was encoded in an auditory fashion. Visual learners did not show better retention when information was encoded in a visual fashion, relative to auditory learners.

Instead, what we are finding is that almost all students, regardless of their supposed learning style, showed stronger retention in the visual condition, and almost all of them showed poorer retention in the auditory condition. This outcome is exactly the opposite of what the learning styles hypothesis predicts, and if our final analysis is consistent with our preliminary findings, it will provide more evidence that the common notions about learning styles are simply not true.

These results, with all participants performing better in the visual condition and worse in the auditory condition, are exactly what dual coding does predict. When the participants were provided with stimuli that required them to use imagery and activate the visuo-spatial areas of the right cerebral hemisphere, in addition to using the left, they remembered more of what they were exposed to. The pictures they created in their heads to go along with the word helped them to remember more information than those who just focused on the words and the sound of them.

Conversely, if they were prompted in such a way that no imagery was stimulated and only the left hemisphere was activated, they retained far less information. This pattern appears to hold true no matter what the participant’s learning style is reported to be. We expect to complete data collection and analysis this year and publish our results in 2015.

So what should we make of all this information?

Humans learn in a variety of different ways. We are all visual learners. We are all auditory learners. We are all kinesthetic learners. But there is very little evidence that if instruction is tailored to a student’s purported learning style that it will improve learning. Evidence is rapidly mounting that learning styles instruction is ineffectual and a waste of valuable instructional time. But is the public listening? Are professors, teachers, and administrators listening? Maybe a demonstration would help since the words are apparently not getting through.

Yet dual coding offers promise for both additional research and for application in learning environments. We hope that with the spread of technology and information resources, research findings on such topics become more accessible to the general population so that educators and the public can become aware of the methods that are supported by research and abandon those shown to be little more than common myths. Considering that we seem to be backpedaling on so many other science-based issues where ideology now trumps evidence, I do have my concerns. But I also must continue to have hope that we will evolve past so many of these misconceptions we still cling to, along with our guns and our Bibles.

About the author


Josh Cuevas is a cognitive psychologist and assistant professor in the College of Education at the University of North Georgia. He earned his Ph.D. in educational psychology from Georgia State University and has previously worked in educational assessment at both the state and national levels. His fields of interest include applied cognition, assessment, educational measurement, evidence-based reasoning, language and literacy, and quantitative research design. When he’s not involved in academic pursuits he enjoys watching the Georgia Bulldogs (from his other alma mater), drinking cheap beer, smoking BBQ on the grill, and has been known to play electric guitar badly.


  • Allcock, S. J., & Hulme, J. A. (2010). Learning styles in the classroom: Educational benefit or planning exercise? Psychology Teaching Review, 16(2), 67-79.
  • Bauch, E. M., & Otten, L. J. (2011). Study-test congruency affects encoding-related brain activity for some but not all stimulus materials. Journal of Cognitive Neuroscience, 24(1), 183-195.
  • Bishka, A. (2010). Learning styles fray: Brilliant or batty? Performance Improvement, 49(10), 9-13. doi: 10.1002/pfi.20181.
  • Choi, I., Lee, S. J., & Kang, J. (2009). Implementing a case-based e-learning environment in a lecture-oriented anesthesiology class: Do learning styles matter in complex problem solving over time? British Journal of Educational Technology, 40(5), 933-947.
  • Cowan, N. & Morey, C. C. (2007). How can dual-task working memory retention limits be investigated? Psychological Science, 18(8), 686-688.
  • Fridley, W. L., & Fridley, C. A. (2010). Some problems & peculiarities with the learning styles rhetoric and practice. Journal of Philosophy & History of Education, 60, 21-27.
  • Gazzaniga, M. S. (2005). Forty-five years of split brain research and still going strong. Nature Reviews Neuroscience, 6(8), 653-659. doi: 10.1038/nrn1723
  • Hsieh, S. W., Jang, Y. R., Hwang, G. J., & Chen, N. S. (2011). Effects of teaching and learning styles on students’ reflection levels for ubiquitous learning. Computers & Education, 57(1), 1194-1201.
  • Jessen, F., Heun, R., Erb, M., Granath, D. O., Klose, U., Papassotiropoulos, A., & Grodd, W. (2000). The concreteness effect: Evidence for dual coding and context availability. Brain and Language, 74, 103-112. doi: 10.1006/brln.2000.2340
  • Kappe, F.R., Boekholt, L., den Rooyen, C., & Van der Flier, H. (2009). A predictive validity study of the Learning Style Questionnaire (LSQ) using multiple, specific learning criteria. Learning & Individual Differences. 19(4), 464-467. doi: 10.1016/j.lindif.2009.04.001.
  • Kirshner, P. A., van Merrienboer, J. J. G. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169-183.
  • Kozub, R. M. (2010). An ANOVA analysis of the relationships between business students’ learning styles and effectiveness of web based instruction. American Journal of Business Education,3(3), 89-98.
  • Martin, S. (2010). Teachers using learning styles: Torn between research and accountability? Teaching and Teacher Education, 26(8), 1583-1591.
  • Mahdjoubi, L., & Akplotsyi, R. (2012). The impact of sensory learning modalities on children’s sensitivity to sensory cues in the perception of their school environment. Journal of Environmental Psychology, 32(3), 208-215.
  • Mayer, R. E. (2011). Does styles research have useful implications for educational practice? Learning & Individual Differences, 21(3), 319-320. doi: 10.1016/j.lindif.2010.11.016.
  • Mazoyer, B., Tzourio-Mazoyer, N., Mazard, A., Denis, M., & Mellet, E. (2002). Neural bases of image and language interactions. International Journal of Psychology, 37(4), 204-208. doi: 10.1080/00207590244000007
  • Morey, C. C. & Cowan, N. (2004). When visual and verbal memories compete: Evidence of cross-domain limits in working memory. Psychonomic Bulletin & Review, 11(2), 296-301.
  • Norman, G. (2009). When will learning style go out of style? Advances in Health Sciences Education, 14(1), 1-4.
  • Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2009). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9, 105-119.
  • Riener, C., & Willingham, D. (2010). The myth of learning styles. Change, 42(5), 32-35.
  • Rohrer, D. Pashler, H. (2012). Learning styles: Where’s the evidence? Medical Education, 46(7), 634-635.
  • Sankey, M. D., Birch, D., & Gardiner, M. W. (2011). The impact of multiple representations of content using multimedia on learning outcomes across learning styles and modal preferences. International Journal of Education & Development using Information & Communication Technology,7(3), 18-35.
  • Scott, C. (2010). The enduring appeal of “learning styles”. Australian Journal of Education, 54(1), 5-17. (EJ889818)
  • Shen, H. H. (2010). Imagery and verbal coding approaches in Chinese vocabulary instruction. Language Teaching Research 14(4), 485-499. doi: 10.1177/1362168810375370.
  • Welcome, S. E., Paivio, A., McRae, K., Joanisse, M. F. (2011). An electrophysiological study of task demands on concreteness effects: Evidence for dual coding theory. Experimental Brain Research, 212(3), 347-358.
  • Zacharis, N. Z. (2011). The effect of learning style on preference for web-based courses and learning outcomes. British Journal of Educational Technology, 42(5), 790-800.



Posted by Josh Cuevas