Mechanisms of Individual Differences in Impulsive and Risky Choice in Rats
Bibliographic record
Abstract
Individual differences in impulsive and risky choice are key risk factors for a variety of maladaptive behaviors such as drug abuse, gambling, and obesity. In our rat model, ordered individual differences are stable across choice parameters, months of testing, and span a broad spectrum, suggesting that rats, like humans, exhibit trait-level impulsive and risky choice behaviors. In addition, impulsive and risky choices are highly correlated, suggesting a degree of correlation between these two traits. An examination of the underlying cognitive mechanisms has suggested an important role for timing processes in impulsive choice. In addition, in an examination of genetic factors in impulsive choice, the Lewis rat strain emerged as a possible animal model for studying disordered impulsive choice, with this strain demonstrating deficient delay processing. Early rearing environment also affected impulsive behaviors, with rearing in an enriched environment promoting adaptable and more self-controlled choices. The combined results with impulsive choice suggest an important role for timing and reward sensitivity in moderating impulsive behaviors. Relative reward valuation also affects risky choice, with manipulation of objective reward value (relative to an alternative reference point) resulting in loss chasing behaviors that predicted overall risky choice behaviors. The combined results are discussed in relation to domain-specific versus domain-general subjective reward valuation processes and the potential neural substrates of impulsive and risky choice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".