Subjective utility moderates bidirectional effects of conflicting motivations on pain perception
Bibliographic record
Abstract
Minimizing pain and maximizing pleasure are conflicting motivations when pain and reward co-occur. Decisions to prioritize reward consumption or pain avoidance are assumed to lead to pain inhibition or facilitation, respectively. Such decisions are a function of the subjective utility of the stimuli involved, i.e. the relative value assigned to the stimuli to compare the potential outcomes of a decision. To test perceptual pain modulation by varying degrees of motivational conflicts and the role of subjective utility, we implemented a task in which healthy volunteers had to decide between accepting a reward at the cost of receiving a nociceptive electrocutaneous stimulus or rejecting both. Subjective utility of the stimuli was assessed by a matching task between the stimuli. Accepting reward coupled to a nociceptive stimulus resulted in decreased perceived intensity, while rejecting the reward to avoid pain resulted in increased perceived intensity, but in both cases only if a high motivational conflict was present. Subjective utility of the stimuli involved moderated these bidirectional perceptual effects: the more a person valued money over pain, the more perceived intensity increased or decreased. These findings demonstrate pain modulation when pain and reward are simultaneously present and highlight the importance of subjective utility for such modulation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".