Individual differences in self- and value-based reward processing
Bibliographic record
Abstract
Self- and value-based reward processing were investigated from an individual difference perspective. Participants learnt to associate geometric shapes with three identities (self, friend, stranger) on one occasion and three values (e.g., high, medium, low monetary value) on another occasion. Participants then carried out a perceptual matching task of judging whether shape-label pairings matched (e.g., triangle-self or circle-£16). This personal matching task was followed by a self-report measure concerning personal distance from others. Both self-identification and high value-associations led to better task performance (faster responses with higher perceptual sensitivity) in the matching tasks. Correlations between self- and high value-based reward biases varied as a function of participant ratings of personal distance between themselves and others. For individuals who rated with a large personal distance, there were no correlations between the self- and reward-biases. In contrast, self- and reward-biases did correlate for individuals who rated a close personal distance between themselves and others. These conclusions were supported by cluster analyses, which showed either distinct or common similarity structures for matching, based on personal and value relevance, corresponding to individuals’ self-rating of a large or close distance to others. The data suggest an intertwining model of self-value at the individual level. This model has significant implications for understanding emotion regulation in relation to self and reward interactions and may be relevant for advancing our understanding self in relation to normal and psychopathological processes.
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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 | 0.001 | 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 | 0.000 |
| 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".