Bayesian Descriptions are not Mechanisms: Predictive Processing and the Realisation Gap in Embodied Neural Dynamics

Abstract

Predictive processing casts perception, action, and learning as probabilistic inference. This paper asks when a Bayesian description identifies a mechanism. I distinguish computational description, algorithm, and physical implementation. Mechanism requires a causal mapping from physical transitions to inferential roles; behavioural fit cannot establish it. Priors range over model-supplied alternatives; observation does not reveal the organism’s individuation. Bayesian algorithms may be high-dimensional, continuous, nonparametric, and representation-growing. Every specified model has a fixed reachable closure; neither closure nor size decides mechanism. The question is which differences a proposed controller state treats as irrelevant. If histories assigned to that state respond differently to intervention because of timing, phase, contact, or field structure, the mapping omits part of the mechanism. A prospectively richer Bayesian or hybrid account may include those differences. High-dimensional systems and flexible model families often resist binary falsification when an experiment’s projection loses distinctions required by the claim. I therefore propose convergent tests of specified internal, coupled, and hybrid mappings. Results reject only a mapping within a declared task, scale, horizon, and margin; post-hoc revision creates a new hypothesis. The resource-bounded coupling hypothesis predicts that human-level transfer on contact-rich tasks depends on distinctions retained in organism–environment relations. An adaptive internal controller counts against it when transfer has no preregistered resource disadvantage, relational-history effects vanish in state-collision tests, the relational block adds no meaningful intervention-predictive value, and internal-realiser interventions have distinctive predicted effects. Existing evidence has not established a Bayesian mechanism for unconstrained embodied behaviour.