ArticleFrontiers in psychology2017
A Goal-Directed Bayesian Framework for Categorization.
Article in Frontiers in psychology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Active inference with reusable state-dependent value profiles.Biological cybernetics · 2026Article
- Hierarchical Summary Statistics Encoding Across Primary Visual and Posterior Parietal Cortices.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The interplay of uncertainty, relevance and learning influences auditory categorization.Scientific reports · 2025Article
- What is optimal in optimal inference?Current opinion in behavioral sciences · 2019Article
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Authors and funding
4 authors.
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Abstract
Categorization is a fundamental ability for efficient behavioral control. It allows organisms to remember the correct responses to categorical cues and not for every stimulus encountered (hence eluding computational cost or complexity), and to generalize appropriate responses to novel stimuli dependant on category assignment. Assuming the brain performs Bayesian inference, based on a generative model of the external world and future goals, we propose a computational model of categorization in which important properties emerge. These properties comprise the ability to infer latent causes of sensory experience, a hierarchical organization of latent causes, and an explicit inclusion of context and action representations. Crucially, these aspects derive from considering the environmental statistics that are relevant to achieve goals, and from the fundamental Bayesian principle that any generative model should be preferred over alternative models based on an accuracy-complexity trade-off. Our account is a step toward elucidating computational principles of categorization and its role within the Bayesian brain hypothesis.
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