Proceedings of the Annual Meeting of the Cognitive Science Society

Predictive coding has emerged as a prominent model of how the brain learns through predictions and prediction errors. Traditional predictive coding focused primarily on sensory coding and perception. Here we propose active predictive coding (APC), a unified framework for perception, action and cognition. By learning hierarchical world models, the APC framework addresses important open problems in cognitive science and AI such as: (1) how do we learn compositional representations, e.g., part-whole hierarchies for equivariant vision? and (2) how do we solve large-scale planning problems, which are hard for traditional reinforcement learning, by composing complex action sequences from primitive policies? APC exploits hypernetworks, self-supervised learning and reinforcement learning to learn hierarchical models that combine task-invariant state transition networks and task-dependent policy networks at multiple abstraction levels. We illustrate the applicability of the APC model to active visual perception and hierarchical planning. Our results represent, to our knowledge, the first proof-of-concept demonstration of a unified approach to addressing the part-whole learning problem in vision, the nested reference frames learning problem in cognition, and the integrated state-action hierarchy learning problem in reinforcement learning.