Effects of teaching neuroplasticity on motivation, inhibitory control and task performance, and the role of mindset theory
Bibliographic record
Abstract
BACKGROUND: Frequent and persistent errors resist teaching, partly due to difficulties in mobilizing inhibitory control. A promising strategy to address this challenge involves teaching students about neuroplasticity. Such instruction may indeed foster motivational beliefs (often referred to as growth mindset), which in turn could positively influence the mobilization of inhibitory control. This study investigated the effects of a neuroplasticity-based intervention on motivation (including constructs from mindset theory), inhibitory control and task performance. METHOD: The final sample included 44 10-12 y/o students recruited from French-speaking elementary schools in the Montreal area (Québec, Canada), primarily through an online advertisement posted on social media. They were assigned to either the experimental group (neuroplasticity intervention) or the control group. They completed a motivational questionnaire at both pretest and posttest and performed a fraction comparison task while undergoing fMRI scanning. RESULTS: Results indicated that students who learned about neuroplasticity demonstrated significant improvements in motivation and greater activation of the ventrolateral prefrontal cortex (VLPFC), a brain region associated with inhibitory control. However, task performance did not significantly differ between groups. Notably, the change in perceived competence was the only motivational variable significantly associated with brain activity related to inhibitory control. CONCLUSIONS: These findings suggest that teaching neuroplasticity can both foster motivation and neural engagement, with perceived competence emerging as a central variable in this relationship. While the intervention did not produce direct effects on academic performance, it remains a promising cost-effective strategy to support students with inhibitory control difficulties and offers valuable insights for future educational interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".