The aversive value of pain in human decision‐making
Bibliographic record
Abstract
BACKGROUND: In order to decide between avoiding pain or pursuing competing rewards, pain must be assigned an abstract value that can be traded against that of competing goods. To assess the relationship between subjectively perceived pain and its value, we conducted an experiment where participants had to accept or decline different intensities of painful electric shocks in exchange of monetary rewards. METHODS: Participants (n = 90) were divided into three groups that were exposed to different distributions of monetary rewards. Monetary offers ranged linearly from $0 to $5 or $10 in groups 1 and 2, respectively, and exponentially from $0 to $5 in group 3. Pain offers ranged from pain detection to pain tolerance thresholds. The value of pain was assessed by identifying the indifference points corresponding to a 50% chance of accepting a certain level of pain for a given monetary reward. RESULTS: The value of pain increased quadratically as a function of the anticipated pain intensity and was found to be relative to the mean and standard deviation of monetary offers. Moreover, decision times increased as a function of the intensity of accepted painful stimulations. Finally, inter-individual differences in psychological traits related to harm avoidance and persistence influenced the value of pain. CONCLUSIONS: This is the first demonstration that the value of pain follows a curvilinear function and is relative to the mean and standard deviation of competing monetary rewards. These new observations significantly contribute to our understanding of how pain is assigned value when making decisions between avoiding pain and obtaining rewards. SIGNIFICANCE: This work provides a description of the pain value function indicating how much people are willing to pay to avoid different intensities of pain. We found that the function was curvilinear, suggesting that the same unit of subjective pain has more value in the high vs. low pain range. Moreover, the pain value was influenced by the experimental manipulation of the rewards distribution and of the inter-individual differences in harm avoidance and persistence. Altogether, the present study provides a detailed account of how subjectively experienced pain is assigned value.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".