Reviewing Structure Learning in and Out of the Active Inference Framework

Abstract Biological and artificial agents operating in complex environments have to leverage environmental structures to accomplish vital tasks. Recent research across a variety of domains—from the study of animal and human behaviour in different developmental periods and for different tasks, to computational studies of learning—has unveiled many ways in which structures are processed. This gave rise to a burgeoning field of study—structure learning. However, the diversity of phenomena studied, and the different aims and focuses of the researchers, have led to ambiguity and limited consensus on the nature of structure learning and its underlying mechanisms. In this paper we provide a synopsis of illustrative examples of structure learning, introduce the Active Inference Framework (AIF) with a focus on Structure Learning, and discuss points of contact between the two. The Active Inference Framework provides a mechanistic theory which distinguishes three levels of learning: Active Inference, Parametric Learning, and Bayesian Model Selection (a.k.a., Structure Learning), a method for the comparison and selection of models based on model evidence. We argue that when formalised under the Active Inference Framework, Structure Learning provides not only an underlying computational mechanism with aims of ecological validity, but also provides features relevant to computational accounts of structure learning more generally. The unifying aspect of the AIF in terms of having a single objective function for optimising behaviour should not be confounded with the exclusivity of this framework. The integration with other computational accounts is advised.