The Effects of Psychosocial Stress and Sex Differences on Cognitive Effort Avoidance
Bibliographic record
Abstract
Background: Recent research suggests stress may affect cognitive performance including memory, executive functioning, decision-making, and task-switching. However, it is unknown whether these effects are aversive or advantageous for effort exertion. This experiment aimed to evaluate the effects of acute psychosocial stress on willingness to exert cognitive control processes in a cognitive-effort-based decision-making task.Methods: To test this, 40 participants (20 female) in a within-subject, fully crossed, randomized design, were exposed to both a psychosocial stress induction condition (the Trier Social Stress Test; TSST) and a control condition. Subsequently, they underwent the Demand Selection Task (DST) that tests for participants’ effort aversion by manipulating switch probabilities in a task-switching paradigm. Results and Conclusion: The induction of stress did not lead to significant error or accuracy rates, or significant differences in cognitive effort avoidance. Previous research indicated sex differences in response to stress. However, there is a lack of data on sex differences in the avoidance of demanding cognitive processes. Therefore, we assessed sex differences in the DST and found that women were more likely to avoid cognitive effort, choosing the less cognitively demanding cue more often than men. Limitations: A limitation of this study is the small sample size. Future research should increase the sample size and take individual differences in stress responders, type of stressor, and biases on effort exertion into account.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".