Prevalence of neuromyths among students and pre-service teachers

The study addresses how widespread neuromyths are among university students in Russia, with a particular focus on pre-service teachers compared to students of other specializations (psychology; chemistry and biology; physics, mathematics, and computer science). The context is the growing intersection of neuroscience and education and concerns about teachers’ susceptibility to misconceptions about brain function. The purpose is to compare neuroscientific literacy and belief in neuromyths across specializations, examine the impact of education level, and assess whether reading reliable scientific sources reduces belief in neuromyths. This is important because persistent neuromyths can misdirect educational practice and teacher preparation.

The paper reviews international research documenting high prevalence of neuromyths among in-service and pre-service teachers across diverse countries (e.g., South Korea, Australia, Portugal, USA, Morocco, Hong Kong, Turkey, India). Prior findings on predictors are mixed: some studies report that higher brain knowledge correlates with greater belief in myths, while others find the opposite. Contributing factors include reliance on unreliable information sources (popular media, commercial programs, brain imagery used in marketing), and materials produced by non-specialists. Frequently endorsed myths include learning styles, hemispheric dominance, and brain-exercise programs. Proposed solutions include interdisciplinary collaboration between neuroscience and education, inclusion of cognitive psychology as a mediator, explicit teaching about neuromyths, and research-based refutation strategies. However, evidence on the effectiveness of courses in reducing neuromyth beliefs is mixed: training often improves neuroscience literacy but may not eliminate myth endorsement.

Design: Cross-sectional anonymous online survey administered in Russian, adapted primarily from Dekker et al. (2012) with additional items from other sources; several items were excluded due to mixed evidence in the literature. The term “neuromyth” was not used in the survey to avoid bias. Participants: 1,074 students were recruited from six regional Russian universities; exclusions included respondents over 35 (n=25), departments with too few respondents (n=85), and invalid/empty responses (n=6), yielding a final sample of 958 students (725 women, 233 men; mean age 19.8, SD 2.41). Students were grouped by specialization: Pedagogy (n=382, pre-service teachers), Chemistry & Biology (C&B; n=172), Psychology (n=218), and Physics, Mathematics & IT (P&M⁢ n=186). Education level was categorized as Younger group (Bachelor years 1–2) and Older group (Bachelor years 3–5, Master’s, PhD). Approximately 67% of all respondents and 59% of pre-service teachers reported interest in neuroscience. Instruments: Part 1 contained 45 items: 20 factual statements about the brain (true) and 25 neuromyth statements (false). Response options: “I agree,” “I disagree,” “I don’t know.” Correct responses were agreement with facts and disagreement with myths. Items F11, F12, M9, M10 (technical principles of brain research) were authored by the researchers. Part 2 collected demographics (age, gender, faculty, education level), sources of neuroscience information (used to index reading reliable literature), and interest. Data availability: Responses available in Harvard Dataverse (https://doi.org/10.7910/DVN/VZ5JFG). Statistical analysis: Normality assessed with Shapiro–Wilk; non-normal distributions led to nonparametric comparisons using Wilcoxon tests with Holm–Bonferroni correction. Factors analyzed: gender, specialization, education level, and reading reliable literature. Analyses conducted in Python (statsmodels). Reported effect sizes for significant comparisons were medium.

• Brain facts: Across all students, 14/20 facts had >60% correct; 6/20 had >80% correct. Best-known facts: F9 (preferred ways of obtaining information; 92% correct), F1 (hormones affect personality; 88%), F13 (brain influences emotions; 86%), F14 (mental practice affects productivity; 86%). Misunderstood facts: Many disagreed with F8 (learning can remediate problems linked to developmental brain differences; 42% disagreed), and 26% disagreed with F10 (brain plasticity after damage). Highest “I don’t know”: F18 (learning via neural connection modification; 37%), F12 (brain-computer interfaces; 34%).
• Neuromyths: >50% agreed with 11/25 myths. Most popular: M20 learning styles (90% agreement), M21 hemispheric dominance (~73%), M22 exercises for developing motor skills/connectivity (~73%). Highest “I don’t know”: M9 (EEG/MEG/MRI visualization of electrical activity; 39%), M18 (brain blood volume increases with physical exertion; 30%), M12 (sugary drinks reduce attention in children; 29%).
• Gender: No significant differences in correct or unsure responses by gender within any specialization.
• Specialization effects: C&B and P&M&IT groups identified both facts and myths more accurately than Pedagogy and Psychology. Biology-related students showed the highest accuracy and fewer unsure responses; P&M&IT students were also more confident. No significant difference between C&B and P&M&IT in overall correctness.
• Education level: Overall, no significant differences between Younger and Older groups. However, in Younger group, C&B and P&M&IT were more accurate (especially on facts); this advantage diminished in Older group as Pedagogy and Psychology improved in recognizing facts. Myth recognition improved with education in P&M&IT but decreased in C&B; Pedagogy and Psychology showed improved fact recognition with education while myth recognition remained unchanged. Unsure responses generally decreased with education for all except C&B, leading to aligned uncertainty levels across specializations in the Older group.
• Reading reliable literature: Reading scientific sources was associated with higher accuracy for facts (significant; Wilcoxon effect size ~0.14) but did not improve myth recognition.
• Effect sizes: For significant comparisons across figures, effect sizes were medium.

The study set out to compare neuroscientific literacy and belief in neuromyths among pre-service teachers and students from other faculties, and to assess influences of specialization, education level, and reading reliable sources. Findings show that specialization is a key factor: students in chemistry/biology and physics/mathematics/IT showed higher baseline competence in recognizing both facts and myths than pre-service teachers and psychology students, likely reflecting stronger prior science preparation. Education level modestly aligned performance across specializations: Pedagogy and Psychology students improved in identifying facts with progression, narrowing gaps with science-oriented peers, but their belief in neuromyths did not decrease, indicating that conventional coursework may not address or dispel specific myths. Reading reliable scientific literature improved recognition of facts but not myths, underscoring that generic exposure to scientific content may raise general literacy without directly challenging entrenched misconceptions. Consistent with prior international research, certain myths (learning styles, hemispheric dominance, brain exercises) remained highly prevalent. Contextual factors (e.g., dissemination through educational media and training) and possible misinterpretation of items (e.g., F8 regarding remediation of learning problems) may contribute. Overall, the results highlight the need to explicitly integrate myth-focused instruction and refutation strategies into teacher education and related curricula to reduce myth persistence while fostering neuroscience literacy.

This study quantified the prevalence of neuromyths and assessed neuroscientific literacy among pre-service teachers and students from other specializations at six Russian universities. Specialization significantly influenced performance, with chemistry/biology and physics/mathematics/IT students outperforming pedagogy and psychology peers. Advancing in education tended to align results, primarily through improved recognition of facts among pedagogy and psychology students, yet belief in neuromyths persisted. Reading reliable scientific literature improved fact recognition but did not reduce myth endorsement. These findings suggest that enhancing coursework with explicit coverage of neuromyths and their refutations, updating curricula in age-related anatomy/psychology with neuroscience content, and designing targeted interventions may help reduce myth prevalence. Future research should evaluate the effectiveness of specific refutation-based and interdisciplinary training approaches longitudinally and across diverse institutions, and clarify why certain science-specialization groups (e.g., C&B) may show declines in myth identification over time.

A primary limitation is sample imbalance across specializations, necessitating the combination of related fields into aggregated groups (Chemistry & Biology; Physics, Mathematics & IT) to balance participant numbers. This grouping may mask within-field variation and affects generalizability across specific disciplines.