1. Structure learning enhances concept formation in synthetic Active Inference agents
- Author
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Neacsu, Victorita, Mirza, M Berk, Adams, Rick A, Friston, Karl J, Neacsu, Victorita [0000-0002-3749-5743], Friston, Karl J [0000-0001-7984-8909], and Apollo - University of Cambridge Repository
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FOS: Computer and information sciences ,Computer and information sciences ,Multidisciplinary ,Biology and life sciences ,Concept Formation ,FOS: Social sciences ,FOS: Physical sciences ,Bayes Theorem ,Social sciences ,Physical sciences ,Research and analysis methods ,Humans ,Learning ,Research Article - Abstract
Acknowledgements: Special thanks to Thomas Parr for our virtual conversations about Active Inference., 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.
- Published
- 2022
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