The Generative Posture Framework: A Unified Account of Postural Generation, Persistence, and Change

Posture is generated, not held. Multiple independent research programs in pain science, postural neuroscience, and trauma-informed clinical practice have recently converged on a framing that treats postural and persistent symptom phenomena as products of distorted predictive processing rather than of structural pathology. The Fit-for-Purpose model (Wand et al. 2023) proposes that chronic nonspecific low back pain represents a state in which patients hold intransient internal models of a damaged back. Two randomized clinical trials published in JAMA in 2022 provide RCT-level evidence for non-pharmacological back pain interventions from different theoretical traditions: the RESOLVE trial (Bagg et al. 2022) for graded sensorimotor retraining (1.0-point between-group pain reduction at 18 weeks, meeting the minimum clinically important difference threshold), and the SPINE CARE trial (Choudhry et al. 2022) for Individualized Postural Therapy via the Egoscue Method (-4.3 Oswestry Disability Index points at 3 months and -6.7 points at 12 months, n=2,971). A parallel research program in mild traumatic brain injury (Kontos et al. 2026, JAMA Network Open) has produced similar evidence at the randomized trial level. We propose the Generative Posture Framework (GPF), a unified computational account under which these convergent findings become instances of a common principle: the body schema generates postural output as the precision-weighted prediction of an active inference process operating on whatever sensory precision the nervous system currently has available. This framework offers the first conceptual bridge we are aware of between Porges's polyvagal theory and Friston's active inference framework, integrating autonomic state dependencies into the precision-weighting architecture as a testable proposal rather than a formal mathematical derivation. A set of operative conditions governs when the body schema revises its postural prediction, organized into three functional categories: five prerequisite principles that must be substantively present for slow-loop update to occur, one interference mechanism (efference copy suppression) that actively blocks update when triggered by high-effort voluntary correction, and two amplifiers (co-regulated relational context and Interoceptive Attention) that scale update magnitude when available. Six postural conditions (adolescent idiopathic scoliosis, kyphosis, chronic pain, post-traumatic freezing, age-related postural collapse, post-concussive postural change) are mapped as instances of the same computational architecture. Ten testable predictions with proposed study designs are included. The framework is offered as a complement to the clinical traditions of the past century rather than as a replacement.