Journal article
Homeostasis after injury: how intertwined inference and control underpin post-injury pain and behaviour
- Abstract:
- Injuries are an unfortunate but inevitable fact of life, leading to an evolutionary mandate for powerful homeostatic processes of recovery and recuperation. The physiological responses of the body and the immune system must be coordinated with behaviour to allow protected time for this to happen, and to prevent further damage to the affected bodily parts. Reacting appropriately requires an internal control system that represents the nature and state of the injury and specifies and withholds actions accordingly. We bring the formal uncertainties embodied in this system into the framework of a partially observable Markov decision process. We discuss nociceptive phenomena in light of this analysis, noting particularly the counter-intuitive behaviours associated with injury investigation, and the propensity for transitions from normative, tonic, to pathological, chronic pain states. Importantly, these simulation results provide a quantitative account and enable us to sketch a much needed roadmap for future theoretical and experimental studies on injury, tonic pain, and the transition to chronic pain.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 4.0MB, Terms of use)
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- Publisher copy:
- 10.1371/journal.pcbi.1013538
Authors
+ Japan Society for the Promotion of Science London
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- Funder identifier:
- https://ror.org/02m7axw05
- Publisher:
- Public Library of Science
- Journal:
- PLoS Computational Biology More from this journal
- Volume:
- 22
- Issue:
- 1
- Pages:
- e1013538-e1013538
- Article number:
- e1013538
- Publication date:
- 2026-01-22
- Acceptance date:
- 2025-09-19
- DOI:
- EISSN:
-
1553-7358
- ISSN:
-
1553734X, 1553-734X
- Language:
-
English
- Keywords:
- UUID:
-
uuid_6de26e35-39ca-41a0-a00e-15a0efe09733
- Source identifiers:
-
3704335
- Deposit date:
-
2026-01-28
- ARK identifier:
Terms of use
- Copyright date:
- 2026
- Notes:
- This work is related to the thesis Safe learning in humans and machines.
- Licence:
- CC Attribution (CC BY)
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