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Structure learning enhances concept formation in synthetic Active Inference agents

Neacsu, Victorita; Mirza, M Berk; Adams, Rick A; Friston, Karl J; (2022) Structure learning enhances concept formation in synthetic Active Inference agents. PLOS ONE , 17 (11) , Article e0277199. 10.1371/journal.pone.0277199. Green open access

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Abstract

Humans display astonishing skill in learning about the environment in which they operate. They assimilate a rich set of affordances and interrelations among different elements in particular contexts, and form flexible abstractions (i.e., concepts) that can be generalised and leveraged with ease. To capture these abilities, we present a deep hierarchical Active Inference model of goal-directed behaviour, and the accompanying belief update schemes implied by maximising model evidence. Using simulations, we elucidate the potential mechanisms that underlie and influence concept learning in a spatial foraging task. We show that the representations formed-as a result of foraging-reflect environmental structure in a way that is enhanced and nuanced by Bayesian model reduction, a special case of structure learning that typifies learning in the absence of new evidence. Synthetic agents learn associations and form concepts about environmental context and configuration as a result of inferential, parametric learning, and structure learning processes-three processes that can produce a diversity of beliefs and belief structures. Furthermore, the ensuing representations reflect symmetries for environments with identical configurations.

Type: Article
Title: Structure learning enhances concept formation in synthetic Active Inference agents
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0277199
Publisher version: https://doi.org/10.1371/journal.pone.0277199
Language: English
Additional information: Copyright © 2022 Neacsu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Keywords: Learning, Free energy, Foraging, Sensory perception, Probability distribution, Agent-based modeling, Machine learning, Behavior
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Division of Psychiatry
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Division of Psychiatry > Mental Health Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10160279
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