Linking neuroscientific research on decision making to the educational context of novice students assigned to a multiple-choice scientific task involving common misconceptions about electrical circuits
Bibliographic record
Abstract
Functional magnetic resonance imaging was used to identify the brain-based mechanisms of uncertainty and certainty associated with answers to multiple-choice questions involving common misconceptions about electric circuits. Twenty-two scientifically novice participants (humanities and arts college students) were asked, in an fMRI study, whether or not they thought the light bulbs in images presenting electric circuits were lighted up correctly, and if they were certain or uncertain of their answers. When participants reported that they were unsure of their responses, analyses revealed significant activations in brain areas typically involved in uncertainty (anterior cingulate cortex, anterior insula cortex, and superior/dorsomedial frontal cortex) and in the left middle/superior temporal lobe. Certainty was associated with large bilateral activations in the occipital and parietal regions usually involved in visuospatial processing. Correct-and-certain answers were associated with activations that suggest a stronger mobilization of visual attention resources when compared to incorrect-and-certain answers. These findings provide insights into brain-based mechanisms of uncertainty that are activated when common misconceptions, identified as such by science education research literature, interfere in decision making in a school-like task. We also discuss the implications of these results from an educational perspective.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".