An active inference model of concept learning

Abstract The algorithmic and neural basis of concept learning remains poorly understood. In this paper, we articulate a novel, biologically plausible approach to concept learning based on active inference, and on the idea that a generative model can be equipped with extra (hidden state or cause) ‘slots’ that can be engaged when an agent learns about novel concepts. This can be combined with a Bayesian model reduction process, in which any concept learning – associated with these slots – can be reset in favor of a simpler model with higher model evidence. We use simulations to illustrate this model’s ability to add new concepts to its state space, increase the granularity of the concepts it currently possesses, and accomplish a simple form of ‘one-shot’ generalization to new stimuli. Although deliberately simple, these results suggest that active inference may offer useful resources in developing neurocomputational models of concept learning.