Computational psychiatry / computational psychosomatics
What the predictive-coding account of interoception looks like when it is pointed at mental illness. Khalsa et al. (2018) fold the wiki’s theoretical machinery — generative models, prediction error, precision, active-inference, allostasis — into a clinical programme: if interoception is inference and control, then interoceptive disorders are specific failures of that inference and control, and the failures are enumerable. This page holds the mechanism; interoceptive-psychopathology holds the clinical nosology it is meant to explain.
The core claim: control needs a model, and disease is where the model fails
The argument the roadmap imports (from Stephan et al. 2016 and Pezzulo, Rigoli & Friston 2015) runs:
- Sensing alone cannot resolve the state of the body, because interosensations are noisy and ambiguous. So the brain runs a generative model and infers.
- The brain’s most fundamental task is not perception but control of bodily states — keeping them in survivable ranges. In information-theoretic terms, choosing actions that minimize the long-term average surprise (entropy) of interosensations.
- Inference and control form a closed loop cast as a hierarchical Bayesian model: predictions, prediction errors, precisions. Reactive homeostatic regulation sits at the bottom (prediction errors trigger reflexes); prospective allostatic regulation modulates the set-points from above.
The mechanistically loaded piece, carried in from Petzschner/Stephan: belief precision determines the force and pace of corrective action — “the tighter the expected range of bodily state, the more vigorous the elicited regulatory action.” A body-state prior held with pathologically high precision therefore drives over-vigorous regulation; this is offered as a novel account of psychosomatic phenomena and placebo effects, and it is where “computational psychosomatics” gets its name (Petzschner et al. 2017 propose it as the basis of a mechanistic differential diagnosis).
Allostatic self-efficacy: a computational route to fatigue and depression
The roadmap’s most concrete clinical hypothesis in this vein is Stephan et al.’s allostatic self-efficacy — a metacognitive belief about one’s own capacity to regulate bodily states. On this account, persistent unresolved interoceptive surprise (dyshomeostasis the system cannot control) is read by higher levels as low self-efficacy over the body, and the downstream psychological expression is fatigue and depression. Depression becomes, in part, a metacognitive verdict about failed bodily control rather than only a mood state. The wiki holds this at one remove (Stephan et al. 2016 is not in raw/), but it connects directly to allostasis, where the precision-weighting account is already recorded, and to the wiki’s own clinical depression material (the Farb & Segal work), which reaches depression from a different, non-computational direction.
The failure mode, in EPIC terms
The roadmap’s psychopathology figure reproduces Barrett & Simmons’s EPIC model and states its illness hypothesis in precision language: interoceptive input (posteriors) becomes decoupled from interoceptive predictions (priors), producing elevated interoceptive prediction error that “may present in the brain as ‘noisy afferent interoceptive inputs’” (Paulus & Stein 2010). Under normal function the agranular visceromotor cortex is relatively insensitive to ascending error, which is why interoceptive predictions stay stable despite bodily fluctuation; the pathological case is that insensitivity breaking down (or over-tightening). See theory-of-constructed-emotion and insular-cortex for the anatomy.
The honest caveat, stated by the authors
The roadmap does not oversell this. “The empirical evidence for hierarchical Bayesian principles of interoception and homeostatic/allostatic control is indirect so far. Studies designed to probe hierarchical Bayesian processes under experimentally controlled homeostatic perturbations will be crucial for finessing (or refuting) current computational concepts.” That is the same status the wiki assigns the predictive-coding account generally on feedforward-vs-predictive-interoception: elegant, unifying, anatomically motivated, and not yet the thing that a decisive experiment has confirmed over the feedforward alternative. What computational psychiatry adds is a reason to run those experiments in patients — because if the framework is right, the precision parameters are where the disorders live.