Central overload: disrupted interoceptive multi-timescale inference in post-infectious ME/CFS

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is marked by fluctuating, state-dependent symptoms, including post-exertional malaise (PEM), orthostatic intolerance, cognitive dysfunction ("brain fog"), and sensory hypersensitivity. Despite growing evidence for persistent post-infectious immune, autonomic, metabolic, and neurobiological abnormalities, no unifying mechanistic framework yet explains how these disturbances become translated into the characteristic temporal dynamics of the illness. Recent neuroimaging and neurochemical findings remain largely descriptive and do not explain why PEM emerges with a delay of hours to days, why symptoms worsen nonlinearly, or why autonomic, cognitive, and sensory domains deteriorate in concert.Here, I propose a biologically plausible and explicitly testable framework in which heterogeneous post-infectious disturbances converge on a disorder of interoceptive inference across nested physiological timescales. The central claim is that ME/CFS may be understood as a state of central overload: post-infectious neuroimmune and neuromodulatory processes bias precision control, such that expected physiological variability is insufficiently attenuated and bodily deviations acquire disproportionate inferential weight. The framework is formalised within a hierarchical Bayesian architecture in which a slowly updating capacity hyperprior constrains moment-to-moment interoceptive inference, and in which neuroinflammation-biased precision amplification at lower levels drives excessive updating at higher levels. Exertion may thereby trigger slow peripheral perturbations whose delayed consequences progressively update higher-order capacity estimates, coupling symptoms across domains and producing PEM. The framework is distinguished from central sensitisation, functional neurological disorder, and conditioned fear accounts by specific, falsifiable predictions regarding the temporal dynamics, computational parameters, and neurophysiological signatures of the proposed inferential disturbance.