ArticlePloS one2022
Structure learning enhances concept formation in synthetic Active Inference agents.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- It Is What It Isn't: Introducing a Constraint-Based Approach to Structure Learning.Entropy (Basel, Switzerland) · 2026Article
- Introducing ActiveInference.jl: A Julia Library for Simulation and Parameter Estimation with Active Inference Models.Entropy (Basel, Switzerland) · 2025Article
- A model of time-varying music engagement.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Spatial and Temporal Hierarchy for Autonomous Navigation Using Active Inference in Minigrid Environment.Entropy (Basel, Switzerland) · 2024Article
- Learning dynamic cognitive map with autonomous navigation.Frontiers in computational neuroscience · 2024Article
- A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network.Brain structure & function · 2023Article
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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