Motive control of unconscious inference: The limbic base of adaptive Bayes

Abstract

Current computational models of neocortical processing, described as predictive coding theory, are providing new ways of understanding Helmholtz's classical insight that perception cannot proceed in a data-driven fashion, but instead requires unconscious inference based on prior experience. Predictive coding is a Bayesian process, in which the operations at each lower level of the cortical hierarchy are predicted by prior projections of expectancies from a higher level, and are then updated by error-correction with lower level evidence. To generalize the predictive coding model to the human neocortex as a whole requires aligning the Bayesian negotiation of prior expectancies with sensory and motor evidence not only within the connectional architecture of the neocortex (primary sensory/motor, unimodal association areas, and heteromodal association areas) but also with the limbic cortex that forms the base for the adaptive control of the heteromodal areas and thereby the cerebral hemisphere as a whole. By reviewing the current evidence on the anatomy of the human corticolimbic connectivity (now formalized as the Structural Model) we address the problem of how limbic cortex resonates to the homeostatic, personal significance of events to provide Bayesian priors to organize the operations of predictive coding across the multiple levels of the neocortex. By reviewing both classical evidence and current models of control exerted between limbic and neocortical networks, we suggest a neuropsychological theory of human cognition, the adaptive Bayes process model, in which prior expectancies are not simply rationalized propositions, but rather affectively-charged expectancies that bias the interpretation of sensory data and action affordances to support allostasis, the motive control of expectancies for future events.

Keywords: Allostasis; Association cortex; Homeostasis; Limbic system; Motive control; Neocortex; Predictive coding; Unconscious inference.