How Accurate Appraisal of Behavioral Costs and Benefits Guides Adaptive Pain Coping
Bibliographic record
Abstract
Coping with pain is a complex phenomenon encompassing a variety of behavioral responses and a large network of underlying neural circuits. Whether pain coping is adaptive or maladaptive depends on the type of pain (e.g., escapable or inescapable), personal factors (e.g., individual experiences with coping strategies in the past), and situational circumstances. Keeping these factors in mind, costs and benefits of different strategies have to be appraised and will guide behavioral decisions in the face of pain. In this review we present pain coping as an unconscious decision-making process during which accurately evaluated costs and benefits lead to adaptive pain coping behavior. We emphasize the importance of passive coping as an adaptive strategy when dealing with ongoing pain and thus go beyond the common view of passivity as a default state of helplessness. In combination with passive pain coping, we highlight the role of the reward system in reestablishing affective homeostasis and discuss existing evidence on a behavioral and neural level. We further present neural circuits involved in the decision-making process of pain coping when circumstances are ambiguous and, therefore, costs and benefits are difficult to anticipate. Finally, we address the wider implications of this topic by discussing its relevance for chronic pain patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".