Reimagining pain as an allostatic imperative: perspectives from contemplative traditions
Bibliographic record
Abstract
The motivational force of pain is undeniable. But what pain commands us to do, how we might satisfy this command, and if our experience of pain is inherently linked to suffering are far murkier topics. This paper brings together empirical studies of pain reprocessing during advanced meditation, the rise of allostatic paradigms to account for biological self-regulation, and the philosophy of pain in the classical Sanskrit philosophical tradition of Pratyabhijñā Śaivism to argue that pain is an allostatic imperative to adapt a part of one's body. We theorize two components of an allostatic response, heterostatic and homeostatic, that work in tandem to address pain as an allostatic command. Homeostatic responses are error-corrective in that they seek to protect an organism by returning to a previously stable steady state. Heterostatic responses are anticipatory in that they seek to better prepare an organism to meet future challenges by proactively shifting to a new steady state. We note that an organism's successful adaptation to its environment depends not just on error-correction, but also on anticipatory change. We theorize that a broad range of affect properly accompanies pain. We propose potential directions for empirically developing this model. We also note the possibility that this model could be extended to account for mental pain.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.047 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".