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. AO_SCPLOWUTHORC_SCPLOW SO_SCPLOWUMMARYC_SCPLOWSeveral arguments suggest that the brain has a dedicated representation of the state of an injury. This provides an internal control system to modulate our behaviour, influencing the pain, anxiety, and mood changes appropriate to the need for heightened protection and recuperation during healing. Here, we propose a computational architecture for how this system might be constructed. We model the injury as a problem where its true state is only partially observable, requiring the brain to combine inference with optimal control to make decisions. We show how this framework offers an explanation of two core paradoxical observations: it explains behaviours such as rubbing or probing an injured area so as to gain information and reduce uncertainty, and it accounts for the high propensity of an injury to transition into a pathological, chronic pain state via information restriction. Overall, this provides a quantitative framework for mapping the bodys healing processes to their underlying neural substrates. This has the potential to help researchers identify novel targets for treating and possibly preventing chronic pain.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 3
- FWCI
- 4.51
- Field percentile
- top 11%
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Pain Matters: Nociceptive signaling drives neural encoding of inflammatory responses
Megan Sammons, Rani Assa Polansky, … Asya Rolls·bioRxiv·Nov 3, 2026
- A stimulus-state geometry in somatosensory cortex reorganizes during inflammatory pain
Ara Schorscher-Petcu, Isobel Parkes, Liam E. Browne·bioRxiv·Aug 19, 2026
- The Control of Tonic Pain by Active Relief Learning
Suyi Zhang, Hiroaki Mano, … Ben Seymour·eLifePeer-reviewed·Feb 26, 2018
- A recurrent neural network model of chronic pain development and recovery
Katherine Huang, Guy Marmor, … Paul K. Hansma·bioRxiv·Apr 22, 2026
- Active inference, stressors, and psychological trauma: A neuroethological model of (mal)adaptive explore-exploit dynamics in ecological context
Adam Linson, Thomas Parr, Karl John Friston·Behavioural Brain ResearchPeer-reviewed·Dec 9, 2019
- Reward-induced endogenous pain inhibition scales with action-outcome certainty in humans
Fabrice Hubschmid, Simon Desch, … Susanne Becker·PainPeer-reviewed·Jun 29, 2026