Neural Mechanisms Mediating Sex Differences in Motivation for Reward: Cognitive Bias, Food, Gambling, and Drugs of Abuse
Bibliographic record
Abstract
Sex differences in motivation for food rewards, gambling, and drugs of abuse are modulated by multiple factors, including sensory stimuli, gonadal hormones, and cognitive bias. Cues, drugs of abuse, and a high-fat diet can significantly impact neural signaling in the reward system and functioning of neural systems that regulate executive functions differentially in males and females. Additionally, sex differences in risky decision-making, cognitive bias, and motivation for food and drugs of abuse are mediated by gonadal hormones in both sexes. As neuroscientists analyze data from both sexes, it is becoming apparent that these differences are not simply mediated by hormones in females, but involve sex differences in the specific neural responses to stimuli, including both external stimuli and internal hormonal signals. Understanding sex differences in the mechanisms underlying reward-seeking behaviors and the development of substance use disorders will help uncover potential therapies and treatments that will benefit both men and women. Based on these observations, it is essential that females are included in neuroscience research.
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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) | 0.002 | 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".