eprintid: 10160279 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/16/02/79 datestamp: 2022-11-24 17:14:12 lastmod: 2022-11-24 17:14:12 status_changed: 2022-11-24 17:14:12 type: article metadata_visibility: show sword_depositor: 699 creators_name: Neacsu, Victorita creators_name: Mirza, M Berk creators_name: Adams, Rick A creators_name: Friston, Karl J title: Structure learning enhances concept formation in synthetic Active Inference agents ispublished: pub divisions: UCL divisions: B02 divisions: C07 divisions: D79 divisions: D07 divisions: F83 divisions: FH4 keywords: Learning, Free energy, Foraging, Sensory perception, Probability distribution, Agent-based modeling, Machine learning, Behavior note: 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. 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. date: 2022-11-14 date_type: published publisher: Public Library of Science (PLoS) official_url: https://doi.org/10.1371/journal.pone.0277199 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1990340 doi: 10.1371/journal.pone.0277199 medium: Electronic-eCollection pii: PONE-D-21-27876 lyricists_name: Friston, Karl lyricists_name: Adams, Richard lyricists_id: KJFRI52 lyricists_id: RAADA06 actors_name: Flynn, Bernadette actors_id: BFFLY94 actors_role: owner funding_acknowledgements: MR/S007806/1 [Medical Research Council]; 088130/Z/09/Z [Wellcome Trust] full_text_status: public publication: PLOS ONE volume: 17 number: 11 article_number: e0277199 event_location: United States issn: 1932-6203 citation: 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 <https://doi.org/10.1371/journal.pone.0277199>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10160279/1/journal.pone.0277199.pdf