Meta-learning in active inference

Penacchio, Olivier; Clemente, Ana

doi:10.1017/S0140525X24000074

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Meta-learning in active inference

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Clemente, Ana
Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Max Planck Society;

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Penacchio, O., & Clemente, A. (2024). Meta-learning in active inference. Behavioral and Brain Sciences, 47: e159. doi:10.1017/S0140525X24000074.

Cite as: https://hdl.handle.net/21.11116/0000-000F-E895-3

Abstract

Binz et al. propose meta-learning as a promising avenue for modelling human cognition. They provide an in-depth reflection on the advantages of meta-learning over other computational models of cognition, including a sound discussion on how their proposal can accommodate neuroscientific insights. We argue that active inference presents similar computational advantages while offering greater mechanistic explanatory power and biological plausibility.