Win-Concurrent Sensory Cues Can Promote Riskier Choice
Bibliographic record
Abstract
Reward-related stimuli can potently influence behavior; for example, exposure to drug-paired cues can trigger drug use and relapse in people with addictions. Psychological mechanisms that generate such outcomes likely include cue-induced cravings and attentional biases. Recent animal data suggest another candidate mechanism: reward-paired cues can enhance risky decision making, yet whether this translates to humans is unknown. Here, we examined whether sensory reward-paired cues alter decision making under uncertainty and risk, as measured respectively by the Iowa Gambling Task and a two-choice lottery task. In the cued versions of both tasks, gain feedback was augmented with reward-concurrent audiovisual stimuli. Healthy human volunteers (53 males, 78 females) performed each task once, one with and the other without cues (cued Iowa Gambling Task/uncued Vancouver Gambling Task: n = 63; uncued Iowa Gambling Task/cued Vancouver Gambling Task: n = 68), with concurrent eye-tracking. Reward-paired cues did not affect choice on the Iowa Gambling Task. On the two-choice lottery task, the cued group displayed riskier choice and reduced sensitivity to probability information. The cued condition was associated with reduced eye fixations on probability information shown on the screen and greater pupil dilation related to decision and reward anticipation. This pupil effect was unrelated to the risk-promoting effects of cues: the degree of pupil dilation for risky versus risk-averse choices did not differ as a function of cues. Together, our data show that sensory reward cues can promote riskier decisions and have additional and distinct effects on arousal. SIGNIFICANCE STATEMENT Animal data suggest that reward-paired cues can promote maladaptive reward-seeking by biasing cost-benefit decision making. Whether this finding translates to humans is unknown. We examined the effects of salient reward-paired audiovisual cues on decision making under risk and uncertainty in human volunteers. Cues had risk-promoting effects on a risky choice task and independently increased task-related arousal as measured by pupil dilation. By demonstrating risk-promoting effects of cues in human participants, our data identify a mechanism whereby cue reactivity could translate into maladaptive behavioral outcomes in people with addictions.
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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".