Structure Learning in Predictive Processing Needs Revision

Abstract

The predictive processing account aspires to explain all of cognition using a single, unifying principle. Among the major challenges is to explain how brains are able to infer the structure of their generative models. Recent attempts to further this goal build on existing ideas and techniques from engineering fields, like Bayesian statistics and machine learning. While apparently promising, these approaches make specious assumptions that effectively confuse structure learning with Bayesian parameter estimation in a fixed state space. We illustrate how this leads to a set of theoretical problems for the predictive processing account. These problems highlight a need for developing new formalisms specifically tailored to the theoretical aims of scientific explanation. We lay the groundwork for a possible way forward.