Most rats prefer gambling opportunities featuring win-paired cues that drive risky choice: Synergistic interactions between choice of and choice during the cued rat gambling task
Bibliographic record
Abstract
Win-paired cues drive maladaptive decision-making in laboratory-based gambling tasks. However, humans prefer these cued games that facilitate gambling addiction. Whether rats prefer tasks that employ win-paired cues is unknown, yet this has consequences for the validity of using rodent models to investigate problem gambling. Here, we allowed rats to choose on a trial-by-trial basis whether to work for cued or uncued rewards on the rat gambling task. We also performed computational modelling to investigate individual differences in decision-making strategies. Rats could be grouped based on preference for task type, which became more pronounced across training, or preference for risky options, as is standard for the rat gambling task. Risk-preferring rats increasingly preferred the cued task over time. Decision-making was marginally riskier on cued trials, particularly in risk-preferring rats, but this was independent of task preference. Pairing of rewards with audiovisual cues accelerated response times and enhanced impulsivity in both cue- and risk-preferring rats. Cued wins also resulted in greater post-reinforcement pauses in risk-preferring animals only. Diffusion model analyses revealed optimal decision-makers have longer non-decision times before risky or impulsive choices, yet this is absent in risk-preferring animals. As such, lapses in cognitive control could be responsible for maladaptive trial outcomes in optimal, but not risk-preferring, rats. Collectively, these data support the use of high-risk preference at baseline as a proxy for vulnerability to problem gambling. Furthermore, diverse computational mechanisms could be responsible for the negative impact of win-paired cues on gambling-like behaviour in at-risk versus resilient individuals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".