Computational Signatures of Pain Chronification: Duration-Dependent Decision-Making Shifts Across Acute and Chronic Pain
Bibliographic record
Abstract
Abstract Chronic pain is often characterized by the entrenchment of maladaptive behaviors — avoidance, inactivity, and hypervigilance — that outlast tissue damage. A central but mechanistically underspecified question is how behaviors acquired during acute pain become locked in as pain chronifies. Influential accounts, including the fear-avoidance model, suggest that behavioral patterns learned in a specific context — such as avoidance during acute injury — overgeneralize to situations where they are no longer advantageous. Whether this reflects altered learning or changes in how learned information guides decisions has remained unclear. To address this, we administered a probabilistic reinforcement learning task to 239 individuals with chronic pain, acute pain, or no pain, designed to dissociate two decision strategies: reliance on recent reinforcement history — what was reinforced in a prior context — versus global expected value — the objective worth of an option regardless of context. Learning performance was comparable across all groups, confirming intact associative learning. However, groups differed significantly in decision-making: individuals with chronic pain favored options with stronger context-dependent reinforcement history over those with higher global expected value; however, those without pain showed no preference for one over the other, with the acute pain group showing an intermediate pattern. Computational modeling confirmed this, with chronic pain patients showing significantly reduced weighting toward global expected value. Critically, this shift tracked pain duration rather than intensity — suggesting prolonged pain exposure gradually biases decisions away from global expected value towards a context-dependent history. These findings offer a computational explanation for behavioral persistence in chronic pain, and this duration-dependent shift, already evident in the acute pain group, may represent an early cognitive signature of chronification.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".