People Differ in Learning Speed, Not Learning Style

Different people generally have different working memory capacities and learn at different rates, but people do not actually learn better in their preferred “learning style.” Instead, different people need the same form of practice but in different amounts.

This post is part of the book The Math Academy Way (Working Draft, Jan 2024). Suggested citation: Skycak, J., advised by Roberts, J. (2024). People Differ in Learning Speed, Not Learning Style. In The Math Academy Way (Working Draft, Jan 2024). https://justinmath.com/people-differ-in-learning-speed-not-learning-style/

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There’s a common myth that goes like this: “everybody has the same working memory capacity and learns at the same rate, but different people learn differently depending on their preferred learning style.”

In reality, the exact opposite is true. Different people generally have different working memory capacities and learn at different rates. While people may have preferred learning “styles” (e.g. visual vs verbal), they do not actually learn better when given information in their preferred style. The myth is that different people need the same amount of practice but in different forms – whereas the reality is that different people need the same form of practice but in different amounts.

Learning Style Preferences are Irrelevant

One of the most widespread – and most widely debunked – neuromyths is that people learn better when they receive information in their preferred “learning style.” To quote the authors of one of the largest and most comprehensive studies on the persistence of neuromyths (Betts et al., 2019):

“Learning styles is one of the most widespread myths in education (Pashler, McDaniel, Rohrer & Bjork, 2008; Reiner & Willingham, 2010; Roher & Pashler, 2012). Despite repeated testing of hypotheses relating to learning styles, there is no evidence to date showing that individuals learn better when they receive information in their preferred learning styles (Newtown & Miah, 2017; Newtown & Miah, 2017).
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In 2006, a learning styles challenge was put forth by a team of underwriters offering $1,000 and then moving it up to $5,000 to provide scientific evidence supporting this myth (Wallace, 2014). To date, there has not been a payout.”

As Grospietsch & Lins (2021) elaborate:

*“According to Grospietsch and Mayer (2021b), the kernel of truth behind this neuromyth is that people differ in the mode in which they prefer to receive information (visually or verbally; e.g., Höffler et al., 2017).

The first erroneous conclusion that can be drawn from this kernel of truth is that there are auditory, visual, haptic and intellectual learning styles, as Vester (1975) suggested in the German context.

The next erroneous conclusion drawn is that people learn better when they obtain information in accordance with their preferred learning style.

Finally, the third erroneous yet widely disseminated conclusion is that teachers must diagnose their students’ learning styles and take them into account in instruction… [T]here is no empirical evidence confirming the effectiveness of considering students’ learning styles in instruction (Willingham et al., 2015).”*

As Kirschner & Hendrick sum it up (2024, pp.108):

“These so-called learning styles have been exposed as nonsense in research time after time. There are no ‘image thinkers’ or ‘language thinkers’. Everyone thinks with both systems and everyone benefits from using both. The more often you use the two systems together, the stronger the trace in your memory and the better you will remember and thus learn.”

Working Memory Capacity (WMC) Differences are Relevant

However, one aspect of the brain that has been widely documented not only to vary between people, but also to affect people’s general cognitive performance, is working memory capacity (WMC). As Conway et al. (2007) describe:

*“A fundamental characteristic of WM [working memory] is that it has a limited capacity, which constrains cognitive performance, such that individuals with greater capacity typically perform better than individuals with lesser capacity on a range of cognitive tasks.

For example, older children have greater capacity than younger children, healthy adults have greater capacity than patients with frontal-lobe damage or disease, younger adults have greater capacity than elderly adults, and in all such cases, those individuals with greater WM capacity out-perform individuals with lesser capacity in several important cognitive domains, including complex learning, reading and listening comprehension, and reasoning.

In short, we know that variation in WM capacity exists and that this variation is important to everyday cognitive performance.”*

These differences in working memory capacity have been characterized not only at a psychological level, but also at the physical level of brain activity measures. Vogel & Machizawa (2004) have found that brain activity reaches a plateau when people attempt to perform tasks that meet or exceed their WMC, and people with high WMC reach this plateau much later than people with low WMC:

*“Here, we provide electrophysiological evidence for lateralized activity in humans that reflects the encoding and maintenance of items in visual memory. The amplitude of this activity is strongly modulated by the number of objects being held in the memory at the time, but approaches a limit asymptotically for arrays that meet or exceed storage capacity.

Indeed, the precise limit is determined by each individual’s memory capacity, such that the activity from low-capacity individuals reaches this plateau much sooner than that from high-capacity individuals. Consequently, this measure provides a strong neurophysiological predictor of an individual’s capacity, allowing the demonstration of a direct relationship between neural activity and memory capacity.
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That is, simply by measuring the amplitude increase across memory array sizes, we could accurately predict an individual’s memory capacity.”*

Engström, Landtblom, & Karlsson (2013) have explained why this happens: the higher one’s WMC, the less neural activity their brain requires to perform the task – in other words, the task is less taxing on their brain.

“Low- and high-capacity participants showed an increase in activity as a function of increasing demands but differed in that high-capacity participants started from a lower level.”

WMC Impacts Perceived Effort

It comes as no surprise, then, that people with higher WMC will generally perceive a given task to be easier than people with lower WMC. Indeed, this has been demonstrated experimentally in a study that measured how difficult people found it to identify spoken words in the presence of background noise (Rudner et al., 2012):

”…[P]articipants were asked to rate effort at SNRs [signal-to-noise ratios, i.e. difficulty levels] individually adapted to their speech recognition performance. Thus, individual differences in speech recognition ability were taken into account in the ratings of perceived effort; even so WM capacity explained variance in perceived effort between conditions.
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[T]he difference in perceived listening effort in modulated and steady state noise at relatively favorable SNRs is a function of WM capacity… [P]ersons with greater WM capacity find listening in noise less effortful than persons with lower WM capacity across all three levels and noise types.
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[R]atings of listening effort may be an indicator of the relative degree of engagement of explicit processing resources in WM. Thus, a relation between WM and rated effort may indicate that persons with greater WM capacity are using fewer explicit processing resources”

WMC Impacts Abstraction Ability

Similarly, it has also been shown that high WMC facilitates abstraction, that is, seeing “the forest for the trees” by learning underlying rules as opposed to memorizing example-specific details (McDaniel et al., 2014). This is unsurprising, given that understanding large-scale patterns requires balancing many concepts simultaneously in WM.

*”…[A]fter training (on a function-learning task), participants either displayed an extrapolation profile reflecting acquisition of the trained cue-criterion associations (exemplar learners) or abstraction of the function rule (rule learners; Studies 1a and 1b).
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Studies 1c and 2 examined the persistence of these learning tendencies on several categorization tasks. Study 1c showed that rule learners were more likely than exemplar learners (indexed a priori by extrapolation profiles) to resist using idiosyncratic features (exemplar similarity) in generalization (transfer) of the trained category. Study 2 showed that the rule learners but not the exemplar learners performed well on a novel categorization task (transfer) after training on an abstract coherent category.
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[W]orking memory capacity (as measured by Ospan following Wiley et al., 2011) was a significant and unique predictor of the tendency to rely on rule versus exemplar processes in the function learning task, such that higher working memory capacity was related to reliance on rule learning.

For a number of reasons, greater working memory capacity could facilitate abstracting the function rule during learning, including the ability to maintain and compare several stimuli concurrently (Craig & Lewandowsky, 2012), to partition the training stimuli into two linear segments and switch back and forth between them during learning (Erickson, 2008; Sewell & Lewandowsky, 2012), and to reject or ignore initial biases (e.g., a positive linear) in order to discern the given function (cf., Wiley et al., 2011).

Thus, learners enjoying greater working memory capacity might be more inclined to engage processes that would support rule learning (relating several training trials, partitioning training trials, ignoring initial biases) than would learners with more limited working memory capacity.”*

Abstracting underlying rules improves one’s ability to extrapolate knowledge to new contexts, a skill that is widely assessed in academic settings. Indeed, individual differences in abstraction ability have been shown to impact educational outcomes (McDaniel et al., 2018):

*“Students may do well answering exam questions that are similar to examples presented in class. Yet, some of these students perform poorly on exam questions that require applying instructed concepts to a new problem whereas others fare better on such questions.

Our hypothesis is that these performance differences reflect, in part, individual differences in learners’ tendencies to focus on acquiring the particular exemplars and responses associated with the training exemplars (exemplar learners) versus attempting to abstract underlying regularities reflected in particular exemplars (abstraction learners)… [W]e differentiated students on this dimension, and then tracked their final exam performances in introductory chemistry courses.

Abstraction learners demonstrated advantages over exemplar learners for transfer questions but not for retention questions. The results converge on the idea that individual differences displayed in how learners acquire and represent concepts persist from laboratory concept learning to learning complex concepts in science courses.”*

WMC Impacts Learning Speed

As one might infer from the impact of WMC on perceived effort and abstraction ability, WMC has also been shown to impact speed of learning, that is, the rate at which one’s ability to perform a task improves over the course of exposure, instruction, and practice on the task (though the impact of WMC on task performance is diminished after the task is learned to a sufficient level of performance).

Multiple studies have linked individual differences in speed of learning and WMC in the context of categorization tasks (see McDaniel et al., 2014 for a summary):

”…[A]cross several types of categorization tasks, Craig and Lewandowsky (2012) and Lewandowsky (2011) reported significant correlations between speed of learning and working memory capacity. In the present study, we found a similar general association between speed of learning in the function task and working memory capacity as indexed by Ospan alone.
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Learning the function rule presumably requires maintaining and comparing stimuli across trials (“comparative hypothesizing”, Klayman, 1988) and possibly partitioning the stimuli into subsets for the different slopes and switching back and forth across these partitioned segments during training (Lewandowsky et al., 2002; Sewall & Lewandowsky, 2012), and these processes require working memory capacity (both from a theoretical perspective, Craig & Lewandowsky, 2012; and based on empirical findings, Sewall & Lewandowsky, 2012). Consequently, for participants attempting to abstract the function rule, higher working memory capacity (as indexed by Ospan scores), would facilitate learning.”
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”The implication is that for the rule learners, those with higher working memory capacity were able to more effectively support the processing needed to determine the functional relation among the training points, thereby supporting faster learning.”

Another study reported that reducing WMC slowed learning during a puzzle (Reber & Kotovsky, 1997):

“Participants solving the Balls and Boxes puzzle for the first time were slowed in proportion to the level of working memory (WM) reduction resulting from a concurrent secondary task. On a second and still challenging solution of the same puzzle, performance was greatly improved, and the same WM load did not impair problem-solving efficiency. Thus, the effect of WM capacity reduction was selective for the first solution of the puzzle, indicating that learning to solve the puzzle, a vital part of the first solution, is slowed by the secondary WM-loading task.”

The impact of WMC on learning speed is not limited to puzzles in academic laboratories – it extends to real-life contexts of academics and professional expertise. For instance, in a study of piano players, WMC was a significant predictor of performance even for experts who had logged thousands of hours of practice – that is, high-WMC pianists attained the same level of performance with fewer hours of practice, or a greater level of performance with the same hours of practice, compared to low-WMC pianists (Meinz & Hambrick, 2010).

“In evaluating participants having a wide range of piano-playing skill (novice to expert), we found that deliberate practice accounted for nearly half of the total variance in piano sight-reading performance. However, there was an incremental positive effect of WMC, and there was no evidence that deliberate practice reduced this effect. Evidence indicates that WMC is highly general, stable, and heritable, and thus our results call into question the view that expert performance is solely a reflection of deliberate practice.”

To be clear, the variation in ability was explained primarily by the amount of effective practice, but WMC was indeed a significant secondary factor. As Kulasegaram, Grierson, & Norman (2013) summarize:

“Although all studies support extensive DP [deliberate practice] as a factor in explaining expertise, much research suggests individual cognitive differences, such as WM capacity, predict expert performance after controlling for DP. The extent to which this occurs may be influenced by the nature of the task under study and the cognitive processes used by experts. The importance of WM capacity is greater for tasks that are non-routine or functionally complex.”

At the other end of the spectrum, Swanson & Siegel (2011) found that students with learning disabilities generally have lower WMC:

“We argue that in the domain of reading and/or math, individuals with LD have smaller general working-memory capacity than their normal achieving counterparts and this capacity deficit is not entirely specific to their academic disability (i.e., reading or math)… We find that in situations that place high demands on processing, individuals with LD have deficits related to controlled attentional processes (e.g., maintaining task relevant information in the face of distraction or interference) when compared to their chronological aged-matched counterparts.
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One conclusion from the experimental literature is that individual differences in WM (of which executive processing is a component) are directly related to achievement (e.g., reading comprehension) in individuals with average or above average intelligence (e.g., Daneman & Carpenter, 1980). Thus, children or adults with normal IQs have difficulty (or efficiency varies) in executive processing and that such difficulties are not restricted to those with depressed intelligence
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Our conclusions from approximately two decades of research are that WM deficits are fundamental problems of children and adults with LD. Further, these WM problems are related to difficulties in reading and mathematics, and perhaps writing. Although WM is obviously not the only skill that contributes to academic difficulties [e.g., vocabulary and syntactical skills are also important (Siegal and Ryan, 1988)], WM does play a significance role in accounting for individual differences in academic performance.”

Lack of Evidence for WMC Training

While it is possible to train and improve on tasks that are typically used to measure WMC, evidence is currently lacking that these task-specific performance improvements actually represent an increase in WMC that can be transferred to more general contexts. As described by Redick et al. (2015):

“Despite the promising results of initial research studies, the current review of all of the available evidence of working memory training efficacy is less optimistic. Our conclusion is that working memory training produces limited benefits in terms of specific gains on short-term and working memory tasks that are very similar to the training programs, but no advantage for academic and achievement-based reading and arithmetic outcomes.
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Previous work has shown that manipulations can increase a person’s score on a working memory measure (e.g., re-taking a test, motivation, strategy instruction), but this improvement in the individual’s working memory score may not reflect a true change in underlying working memory ability. For example, Ericsson et al. (1980) demonstrated a subject who, through mnemonic strategies, was able to increase his serial recall of digits to 79 in a row, though when tested on memory span measures that did not include digits, his scores were in the normal range (7 ± 2).
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The bulk of the evidence from studies with rigorous methodology provide little evidence for the efficacy of working memory training in improving academic and achievement outcomes such as reading, spelling, and math. The observation of positive near transfer to working memory and lack of academic or achievement test far transfer corresponds with previous meta-analyses (Melby-Lervåg & Hulme, 2013; Rapport et al., 2013), and indicates that contrary to popular belief, the evidence for the educational benefit of working memory training is lacking.”

However, as Anderson (1987) points out, training domain-specific skills can effectively turn long-term memory into an extension of working memory:

” Chase and Ericsson (1982) showed that experience in a domain can increase capacity for that domain. Their analysis implied that what was happening is that storage of new information in long-term memory, became so reliable that long-term became an effective extension of short-term memory.”

For emphasis, we quote Chase and Ericsson (1982) directly:

“The major theoretical point we wanted to make here is that one important component of skilled performance is the rapid access to a sizable set of knowledge structures that have been stored in directly retrievable locations in long-term memory. We have argued that these ingredients produce an effective increase in the working memory capacity for that knowledge base.”

It comes as no surprise, then, that Redick et al. (2015) recommend that students focus on training subject-specific skills directly:

“We recommend that in contrast to unstructured, unguided, general interventions such as cognitive training and videogame training, more research should be focused on training specific skills and abilities that are likely to exhibit near transfer to very similar academically relevant outcomes — for example, training specific language skills in children with text comprehension difficulties (Clarke, Snowling, Truelove, & Hulme, 2010), or computer-assisted instruction of reading and math skills (Rabiner, Murray, Skinner, & Malone, 2010).”

These recommendations are echoed (Anderson et al., 1998) by K. Anders Ericsson, one of the most influential researchers in the field of human expertise and performance:

*”…[M]odern educators have trained many generalizable abilities such as creativity, general problem-solving methods, and critical thinking. However, decades of laboratory studies and theoretical analyses of the structure of human cognition have raised doubts on the possibility of training general skills and processes directly, independent of specific knowledge and tasks.

For example, research on thinking and problem solving show that successful performance depends on special knowledge and acquired skills, and studies of learning and skill acquisition show that improvements in performance are primarily limited to activities in the specific domain.”*

The recommendations are also echoed by researchers Amanda VanDerHeyden and Robin S. Codding (2020), who have extensive experience researching academic intervention in mathematics:

“The evidence summarized and analyzed in meta-analytic studies illustrates that (a) although cognitive measures correlate with mathematics achievement, these measures do not correlate with student responsiveness to intervention; (b) using cognitive assessment tools does not provide the information necessary to improve academic skill weaknesses; and (c) cognitive interventions do very little to improve academic performance outcomes (Burns, 2016).
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[Jacob and Parkinson (2015)] concluded that there are very few rigorous intervention studies examining the causal link between executive function interventions and academic outcomes… [T]hese existing studies showed improvements on measures of executive function but no improvements on academic achievement. Thus, the notion that executive function training can bring about gains in mathematics proficiency is not consistent with existing evidence. The evidence serves as a reminder that the most effective way to address a math skill deficit is to directly remediate math skills rather than trying to improve working memory or executive functioning as a means to address math skill deficits.”

Different Students Need Different Amounts of Practice

The takeaway from all of this is that an adaptive learning system should focus on subject-specific learning tasks and adapt to a student’s observed learning speed, not their preferred learning style. Each student needs to be given enough practice to achieve mastery on each learning task – and that amount of practice may vary depending on the particular student and the particular learning task.

While this may seem like a disappointing truth for students who generally need more practice than others, recall a study quoted earlier in this post, which showed that the impact of WMC on task performance was diminished after the task was learned to a sufficient level of performance (Reber & Kotovsky, 1997).

“Participants solving the Balls and Boxes puzzle for the first time were slowed in proportion to the level of working memory (WM) reduction resulting from a concurrent secondary task. On a second and still challenging solution of the same puzzle, performance was greatly improved, and the same WM load did not impair problem-solving efficiency. Thus, the effect of WM capacity reduction was selective for the first solution of the puzzle, indicating that learning to solve the puzzle, a vital part of the first solution, is slowed by the secondary WM-loading task.”

More generally, as Unsworth & Engle (2005) explain:

”…[I]ndividual differences in WM capacity occur in tasks requiring some form of control, with little difference appearing on tasks that required relatively automatic processing.”

In this view, extra practice should not be viewed as limiting the progress of students who are slower to learn, but rather as empowering them to develop greater automaticity and lessen the impact of the cognitive difference responsible for their slower learning, thereby allowing continued learning on more advanced material.

Note that this is fully compatible with, and in fact a necessary part of maintaining a growth mindset. Nobody’s current level of knowledge is “fixed” or set in stone, and in order to support every student and maximize their learning, it’s necessary to provide some students with more practice than others. The whole goal of adapting the amount of practice to individual differences in student learning speeds is to support maximum student growth. In fact, in the absence of such adaptivity student growth would certainly be stunted:

If a student is catching on slowly, and you don’t give them enough practice and instead move them on to the next thing before they are able to do the current thing, then you’ll soon push them so far out of their depth that they’ll just be struggling all the time and not actually learning anything, thereby stunting their growth.
Likewise, if a student picks up on something really quickly and you make them practice it for way longer than they need to instead of allowing them to move onward to more advanced material, that’s also stunting their growth.

To maximize each individual student’s growth on each individual skill that they’re learning, each student must be given enough practice to achieve mastery and allowed to move on to more advanced skills immediately after mastering the prerequisites.

References

Anderson, J. R. (1987). Skill acquisition: Compilation of weak-method problem situations. Psychological review, 94 (2), 192.

Anderson, J. R., Reder, L. M., Simon, H. A., Ericsson, K. A., & Glaser, R. (1998). Radical constructivism and cognitive psychology. Brookings papers on education policy, (1), 227-278.

Betts, K., Miller, M., Tokuhama-Espinosa, T., Shewokis, P. A., Anderson, A., Borja, C., … & Dekker, S. (2019). International Report: Neuromyths and Evidence-Based Practices in Higher Education. Online Learning Consortium.

Chase, W. G., & Ericsson, K. A. (1982). Skill and working memory. In Psychology of learning and motivation (Vol. 16, pp. 1-58). Academic Press.

Conway, A., Jarrold, C., Kane, M., Miyake, A., & Towse, J. (2007). Variation in Working Memory: An Introduction. In Conway, A., Jarrold, C., Kane, M., Miyake, A., & Towse, J. (Eds.), Variation in working memory (pp.3-17). Oxford University Press.

Engström, M., Landtblom, A. M., & Karlsson, T. (2013). Brain and effort: brain activation and effort-related working memory in healthy participants and patients with working memory deficits. Frontiers in human neuroscience, 7, 140.

Grospietsch, F., & Lins, I. (2021). Review on the prevalence and persistence of neuromyths in education–Where we stand and what Is still needed. In Frontiers in Education (Vol. 6, p. 665752). Frontiers Media SA.

Kirschner, P., & Hendrick, C. (2024). How learning happens: Seminal works in educational psychology and what they mean in practice. Routledge.

Kulasegaram, K. M., Grierson, L. E., & Norman, G. R. (2013). The roles of deliberate practice and innate ability in developing expertise: evidence and implications. Medical education, 47 (10), 979-989.

McDaniel, M. A., Cahill, M. J., Frey, R. F., Rauch, M., Doele, J., Ruvolo, D., & Daschbach, M. M. (2018). Individual differences in learning exemplars versus abstracting rules: Associations with exam performance in college science. Journal of Applied Research in Memory and Cognition, 7 (2), 241-251.

McDaniel, M. A., Cahill, M. J., Robbins, M., & Wiener, C. (2014). Individual differences in learning and transfer: stable tendencies for learning exemplars versus abstracting rules. Journal of Experimental Psychology: General, 143 (2), 668.

Meinz, E. J., & Hambrick, D. Z. (2010). Deliberate practice is necessary but not sufficient to explain individual differences in piano sight-reading skill: The role of working memory capacity. Psychological science, 21 (7), 914-919.

Reber, P. J., & Kotovsky, K. (1997). Implicit learning in problem solving: The role of working memory capacity. Journal of Experimental Psychology: General, 126 (2), 178.

Redick, T. S., Shipstead, Z., Wiemers, E. A., Melby-Lervåg, M., & Hulme, C. (2015). What’s working in working memory training? An educational perspective. Educational Psychology Review, 27 (4), 617-633.

Rudner, M., Lunner, T., Behrens, T., Thorén, E. S., & Rönnberg, J. (2012). Working memory capacity may influence perceived effort during aided speech recognition in noise. Journal of the American Academy of Audiology, 23 (08), 577-589.

Swanson, H. L., & Siegel, L. (2011). Learning disabilities as a working memory deficit. Experimental Psychology, 49 (1), 5-28.

Unsworth, N., & Engle, R. W. (2005). Individual differences in working memory capacity and learning: Evidence from the serial reaction time task. Memory & cognition, 33 (2), 213-220.

VanDerHeyden, A. M., & Codding, R. S. (2020). Belief-Based versus Evidence-Based Math Assessment and Instruction. Communique, 48 (5).

Vogel, E. K., & Machizawa, M. G. (2004). Neural activity predicts individual differences in visual working memory capacity. Nature, 428 (6984), 748-751.

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