Risk-Promoting Effects of Reward-Paired Cues in Human Sign- and Goal-Trackers
Bibliographic record
Abstract
Animal research suggests trait-like individual variation in the degree of incentive salience attribution to reward-predictive cues, defined phenotypically as sign-tracking (high) and goal-tracking (low incentive salience attribution). While these phenotypes have been linked to addiction features in rodents, their translational validity is less clear. Here, we examined whether sign- and goal-tracking in healthy human volunteers modulates the effects of reward-paired cues on decision making. Sign-tracking was measured in a Pavlovian conditioning paradigm as the amount of eye gaze fixation on the reward-predictive cue versus the location of impending reward delivery. In Study 1 (Cherkasova et al, 2018), participants were randomly assigned to perform a binary choice task in which rewards were either accompanied (cued, n=63) or unaccompanied (uncued, n=68) by money images and casino jingles. In Study 2, participants (n=58) performed cued and uncued versions of the task in a within-subjects design. Across both studies, cues promoted riskier choice. Sign-tracking was not associated with risky choice in either study. Goal-tracking rather than sign-tracking was significantly associated with greater risk-promoting effects of cues in Study 1 but not in Study 2, although the direction of findings was consistent across both studies. These findings are at odds with the notion of sign-trackers being preferentially susceptible to the influence of reward cues on behavior and point to the role of mechanisms besides incentive salience in mediating such influences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".