Key result
A partially observable Markov decision process model demonstrates that information restriction and aberrant priors can sustain incorrect beliefs about injury resolution, driving the transition to chronic pain.
Why the study?
Appropriately reacting to injury requires an internal control system coordinating physiology and behaviour, but formal computational models capturing uncertainty and control in post-injury pain states were needed.
This computational framework provides a quantitative model for understanding how the brain represents injury states, potentially identifying novel targets for treating chronic pain.
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. A uthor S ummary Several 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 body’s healing processes to their underlying neural substrates. This has the potential to help researchers identify novel targets for treating and possibly preventing chronic pain.
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Mahajan et al. (2025) studied Post-injury pain and chronic pain. Partially observable Markov decision process (POMDP) modeling was evaluated. A partially observable Markov decision process model demonstrates that information restriction and aberrant priors can sustain incorrect beliefs about injury resolution, driving the transition to chronic pain.
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