Evidence for What doesn’t work: learning myths and weak methods #

Every substantive claim on the What doesn’t work: learning myths and weak methods page is checked against current research. Here is each claim, how well today’s evidence supports it, and the sources. The full, de-duplicated source list lives on the references page.

Supported · strong evidence — The ‘we only use 10% of our brain’ claim is a myth; virtually the whole brain is active and there is no large dormant region waiting to be unlocked.

Functional imaging shows activity throughout the brain over the course of normal activity, lesions to almost any region impair function, and metabolic cost argues against a 90% reserve; the 10% claim is a textbook neuromyth with no scientific basis, a position unchanged in 2026.

Sources: Lilienfeld, S. O., Lynn, S. J., Ruscio, J., & Beyerstein, B. L. (2010), 50 Great Myths of Popular Psychology. Wiley-Blackwell — https://doi.org/10.1002/9781444305296 · full reference ›

Supported · moderate evidence — Individuals are not globally ’left-brained’ or ‘right-brained’; although some functions are lateralised (e.g. language tends left), people use both hemispheres extensively rather than having a dominant side that should dictate learning method.

Nielsen et al.’s large resting-state connectivity analysis found no evidence of global left- or right-hemisphere dominance across individuals, while acknowledging genuine function-specific lateralisation; the popular ‘dominance’ dichotomy is not supported, consistent with the 2026 consensus.

Sources: Nielsen, J. A., Zielinski, B. A., Ferguson, M. A., Lainhart, J. E., & Anderson, J. S. (2013), An evaluation of the left-brain vs. right-brain hypothesis with resting state functional connectivity MRI. PLOS ONE — https://doi.org/10.1371/journal.pone.0071275 · full reference ›

Supported · strong evidence — Learners have genuine, relatively stable preferences for how they take in material, but matching instruction to a learner’s preferred style (the ‘meshing hypothesis’) does not reliably improve learning.

Pashler et al. accept that preferences exist but found almost no studies using the crossover-interaction design needed to test matching, and those that did failed to support it; the debunk of the matching (meshing) claim remains the firm consensus in 2026 while the reality of preferences is uncontested.

Sources: Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008), Learning styles: Concepts and evidence. Psychological Science in the Public Interest — https://doi.org/10.1111/j.1539-6539.2009.01038.x · full reference ›

Supported · strong evidence — Learning-styles matching is one of the most widely believed ’neuromyths’ among educators despite lacking experimental support.

Dekker et al. surveyed teachers and found very high endorsement of the learning-styles matching idea and other neuromyths despite the absence of supporting evidence; the gap between belief and evidence is well established and persists in 2026.

Sources: Dekker, S., Lee, N. C., Howard-Jones, P., & Jolles, J. (2012), Neuromyths in education: Prevalence and predictors of misconceptions among teachers. Frontiers in Psychology — https://doi.org/10.3389/fpsyg.2012.00429 · full reference ›

Supported · strong evidence — The ‘Mozart effect’ — that listening to Mozart produces a meaningful, lasting boost in intelligence — is not supported; any effect is small, transient, and attributable to short-term arousal/mood from enjoyable music rather than to the music enhancing cognition.

Pietschnig, Voracek & Formann’s meta-analysis of ~40 studies found only a very small overall effect that shrank further in higher-quality and non-affiliated samples, consistent with an arousal-and-mood explanation rather than a genuine cognitive enhancement; this is the settled view in 2026.

Sources: Pietschnig, J., Voracek, M., & Formann, A. K. (2010), Mozart effect–Shmozart effect: A meta-analysis. Intelligence — https://doi.org/10.1016/j.intell.2010.03.001 · full reference ›

Supported · strong evidence — Commercial brain-training programs improve performance on the trained tasks and closely similar ones, but there is little convincing evidence they transfer to broad everyday cognition or unrelated abilities (far transfer).

Simons et al.’s comprehensive review concluded the evidence supports near-transfer to trained tasks but not the broad real-world cognitive benefits advertised; subsequent work and a regulatory settlement against a major brain-training vendor reinforce this, and it remains the consensus in 2026.

Sources: Simons, D. J., Boot, W. R., Charness, N., Gathercole, S. E., Chabris, C. F., Hambrick, D. Z., & Stine-Morrow, E. A. L. (2016), Do ‘brain-training’ programs work? Psychological Science in the Public Interest — https://doi.org/10.1177/1529100616661983 · full reference ›

Supported · moderate evidence — Specific NLP techniques (e.g. the swish pattern, anchoring, dissociation) lack credible controlled-trial evidence for the effects they claim.

Sturt et al.’s systematic review of NLP in a health-care context found the existing studies were few and methodologically weak, providing little evidence that NLP techniques achieve their claimed effects; the broader research literature similarly fails to validate NLP, a stance maintained in 2026. Strength rated moderate because the controlled evidence base is thin (absence of good support) rather than a large body of well-powered null results.

Sources: Sturt, J., Ali, S., Robertson, W., Metcalfe, D., Grove, A., Bourne, C., & Bridle, C. (2012), Neurolinguistic programming: a systematic review of the effects on health outcomes. British Journal of General Practice — https://doi.org/10.3399/bjgp12X658287 · full reference ›

Supported · strong evidence — Reading speed and comprehension trade off beyond a point: very high ‘speed reading’ rates are achieved by skimming, which sacrifices detail and depth, so claims of extreme speed with full comprehension are not credible.

Rayner et al.’s review of the reading science concluded there is a robust speed–accuracy trade-off, that the eye/language system imposes real limits, and that dramatic speed-reading gains come at the cost of comprehension (essentially skimming); this is the well-supported position in 2026.

Sources: Rayner, K., Schotter, E. R., Masson, M. E. J., Potter, M. C., & Treiman, R. (2016), So much to read, so little time: How do we read, and can speed reading help? Psychological Science in the Public Interest — https://doi.org/10.1177/1529100615623267 · full reference ›

Supported · strong evidence — Instead of styles-matching, presenting material in the mode the content demands and combining words with a corresponding picture (the multimedia / dual-coding principle) improves learning for learners generally.

The multimedia principle — combining words with matching visuals aids learning across learners — is one of the most replicated findings in instructional research (Mayer 2009), and provides the sound, evidence-based alternative to learning-styles matching recommended on the page.

Sources: Mayer, R. E. (2009), Multimedia Learning (2nd ed.). Cambridge University Press — https://doi.org/10.1017/CBO9780511811678 · full reference ›

Supported · strong evidence — The reliable gains in durable learning come from how material is engaged — retrieval practice, spacing, interleaving, and elaboration — rather than from brain-folklore shortcuts or modality channel.

Dunlosky et al. rate practice testing and distributed practice as high-utility across learners and subjects, with broad subsequent replication; redirecting effort from debunked methods to these techniques is strongly evidence-supported in 2026.

Sources: Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013), Improving students’ learning with effective learning techniques. Psychological Science in the Public Interest — https://doi.org/10.1177/1529100612453266 · full reference ›

Supported · moderate evidence — Skills and training tend not to transfer far to dissimilar tasks; improvement is usually specific to what was practised, which is why general ‘brain-training’ fails to boost unrelated abilities and why practising the actual target subject works better.

The limited-far-transfer principle is well documented across the cognitive-training and transfer literatures and is the mechanism Simons et al. invoke to explain brain-training’s null broad effects; the practical implication that targeted practice beats generic games is sound, though transfer magnitude varies by domain.