Stimulant medication is not associated with increased cheating during online task performance: A field study (Preprint)
Bibliographic record
Abstract
BACKGROUND Performance-related cheating is a common psychological side effect in settings where task attention is enhanced, for instance, through incentives. However, only a single previous study found a positive effect of stimulants commonly used to elevate attention in attention-deficit/hyperactivity disorder on cheating. OBJECTIVE In a field study, we examined whether methylphenidate (MPH) and mixed amphetamine salts (MAS) increased prohibited usage of Internet resources during the performance of judgment and knowledge tasks. METHODS The participants were 656 US and Canada residents recruited via Prolific Academic who indicated using either MPH or MAS on a weekly basis. Among them 352 were medicated and 304 were unmedicated when performing the experimental tasks. We tested whether participants showed better performance in a version of the Cognitive Reflection Test where solutions are accessible online; and whether they answered a difficult knowledge question based on online resources. RESULTS There was a significant crossover interaction in both cheating paradigms, such that those medicated with MPH exhibited lower cheating than those unmedicated, while those medicated with MAS indicated a slight reverse effect, yet both medications did not evidence a simple effect of medication. There was also no effect or interaction of ADHD diagnosis. CONCLUSIONS We find no evidence for the positive effect of MPH on cheating observed previously, and instead find a trend in the reverse direction (Cohen’s d = -0.31), which also sheds light on the heterogeneity in the effect of MPH on performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".