Divergent effects of win-paired cues on learning from timeout penalties in female and male rats
Bibliographic record
Abstract
In both males and females, linking rewards with salient audiovisual cues in simulated gambling games increases risky choice in humans and rats. However, the prevalence and severity of gambling problems differs in men and women. In previous work, reinforcement learning (RL) models were applied to data from male rats performing the rat gambling task (rGT) to investigate the computational processes promoting risky choice. In the rGT, the optimal strategy is to favor options paired with smaller per-trial gains but shorter and less frequent time-out penalties. Rewards are either delivered with (cued) or without (uncued) concurrent audiovisual cues. Previous work showed these cues drive risky decision making by causing male rats to under weigh the relative cost of timeout punishments, specifically for one of the highly risky options. Here, we applied the same methodology to a large dataset from female rats performing the cued and uncued rGT to investigate whether the same cognitive mechanism drives risky decision making across sexes. Cues decreased the learning rate from all time-out penalties in female rats, rather than specifically from those paired with a risky option. Although females were less sensitive to the shortest time-outs associated with the one pellet option (P1), this computation failed to promote choice of this comparatively safe option due to the overall lower learning rate from penalties. Differences revealed by computational modeling in the way risky choice develops across sexes may help us understand the divergent trajectory of gambling disorder in men and women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".