Sex-biased computations underlying differential set shift performance in mice
Bibliographic record
Abstract
Cognitive flexibility can be defined as the ability to adaptively shift between choices or strategies based on environmental feedback. Flexibility is disrupted in numerous neuropsychiatric conditions. Individual differences in the computations supporting cognitive flexibility may reveal mechanisms of neuropsychiatric risk and resilience. One critical variable well known to influence individual differences in neuropsychiatric risk is sex. While previous research has identified sex differences in value based decision making in mice, whether sex reflects a major source of variation in cognitive flexibility remains unknown. To directly assess sex-biased individual differences in cognitive flexibility, we developed a novel touchscreen Set Shift task that permits robust and continuous testing in mice. Using this task, we discovered that female mice completed significantly more rule shifts with fewer errors than males. We next employed a suite of computational models that revealed sex-biased individual differences in the computations underlying cognitive flexibility. Our results suggest that following rule shifts, female mice learn the new rule faster and commit to exploiting rule choices sooner compared to males-sometimes because they follow multiple rules simultaneously. This suggests that increased choice stability in female rodents enhances commitment to a choice during periods of uncertainty and directly contributes to increased rule shifting. This supports the counterintuitive conclusion that a high degree of stable choice is a strong requirement for enhanced cognitive flexibility in the Set Shift task, one of the well-established cognitive flexibility tasks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".