TY  - JOUR
KW  - Learning
KW  -  Free energy
KW  -  Foraging
KW  -  Sensory perception
KW  -  Probability distribution
KW  -  Agent-based modeling
KW  -  Machine learning
KW  -  Behavior
N2  - 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.
SN  - 1932-6203
A1  - Neacsu, Victorita
A1  - Mirza, M Berk
A1  - Adams, Rick A
A1  - Friston, Karl J
IS  - 11
AV  - public
Y1  - 2022/11/14/
UR  - https://doi.org/10.1371/journal.pone.0277199
ID  - discovery10160279
N1  - 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.
TI  - Structure learning enhances concept formation in synthetic Active Inference agents
VL  - 17
JF  - PLOS ONE
PB  - Public Library of Science (PLoS)
ER  -