Impact of Contextual Factors and Substance Characteristics on Perspectives toward Cognitive Enhancement
Bibliographic record
Abstract
Enhancing cognitive performance with substances--especially prescription drugs--is a fiercely debated topic among scholars and in the media. The empirical basis for these discussions is limited, given that the actual nature of factors that influence the acceptability of and willingness to use cognitive enhancement substances remains unclear. In an online factorial survey, contextual and substance-specific characteristics of substances that improve academic performance were varied experimentally and presented to respondents. Students in four German universities rated their willingness to use and moral acceptance of different substances for cognitive enhancement. We found that the overall willingness to use performance enhancing substances is low. Most respondents considered the use of these substances as morally unacceptable. Situational influences such as peer pressure, policies concerning substance use, relative performance level of peers, but also characteristics of the substance, such as perceptions of substance safety, shape the willingness and acceptability of using a substance to enhance academic performance. Among the findings is evidence of a contagion effect meaning that the willingness was higher when the respondents have more CE drug users in their social network. We also found deterrence effects from strong side effects of using the substance, as well as from policy regulations and sanctions. Regulations might activate social norms against usage and sanctions can be seen as costly to users. Moreover, enhancement substances seem to be most tempting to low performers to catch up with others compared to high performers. By identifying contextual factors and substance characteristics influencing the willingness and acceptability of cognitive enhancers, policy approaches could consider these insights to better manage the use of such substances.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".