On the minimal theory of consciousness implicit in active inference.

Subjective experience is multifaceted, making it hard for traditional neuroscientific theories of consciousness to be compared because each focuses on different aspects like perceptual awareness or global states. This work instead starts from active inference, a first-principles framework that models behavior as approximate Bayesian inference, and builds a minimal theory of consciousness from shared features of computational models derived under active inference. By reviewing studies that apply active inference models to consciousness, the authors identify a small set of theoretical commitments implicit in these models, pointing toward a minimal and testable theory of consciousness.

Study at a glance

Abstract

The multifaceted nature of subjective experience poses a challenge to the study of consciousness. Traditional neuroscientific approaches often concentrate on isolated facets, such as perceptual awareness or the global state of consciousness and construct a theory around the relevant empirical paradigms and findings. Theories of consciousness are, therefore, often difficult to compare; indeed, there might be little overlap in the phenomena such theories aim to explain. Here, we take a different approach: starting with active inference, a first principles framework for modelling behaviour as (approximate) Bayesian inference, and building up to a minimal theory of consciousness, which emerges from the shared features of computational models derived under active inference. We review a body of work applying active inference models to the study of consciousness and argue that there is implicit in all these models a small set of theoretical commitments that point to a minimal (and testable) theory of consciousness.