Neural representation in active inference: Using generative models to interact with-and understand-the lived world.
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| Title: | Neural representation in active inference: Using generative models to interact with-and understand-the lived world. |
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| Authors: | Pezzulo G; Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy., D'Amato L; Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.; Polytechnic University of Turin, Turin, Italy., Mannella F; Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy., Priorelli M; Institute of Cognitive Sciences and Technologies, National Research Council, Padua, Italy., Van de Maele T; IDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium., Stoianov IP; Institute of Cognitive Sciences and Technologies, National Research Council, Padua, Italy., Friston K; Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK.; VERSES Research Lab, Los Angeles, California, USA. |
| Source: | Annals of the New York Academy of Sciences [Ann N Y Acad Sci] 2024 Apr; Vol. 1534 (1), pp. 45-68. Date of Electronic Publication: 2024 Mar 25. |
| Publication Type: | Journal Article; Review |
| Language: | English |
| Journal Info: | Publisher: New York Academy of Sciences Country of Publication: United States NLM ID: 7506858 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1749-6632 (Electronic) Linking ISSN: 00778923 NLM ISO Abbreviation: Ann N Y Acad Sci Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2006- : New York, NY : Malden, MA : New York Academy of Sciences ; Blackwell Original Publication: New York, The Academy. |
| MeSH Terms: | Cognition* , Brain*, Humans ; Sensation ; Learning |
| Abstract: | This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function. It delves into how living organisms employ generative models to minimize the discrepancy between predictions and observations (as scored with variational free energy). The ensuing analysis suggests that the brain learns generative models to navigate the world adaptively, not (or not solely) to understand it. Different living organisms may possess an array of generative models, spanning from those that support action-perception cycles to those that underwrite planning and imagination; namely, from explicit models that entail variables for predicting concurrent sensations, like objects, faces, or people-to action-oriented models that predict action outcomes. It then elucidates how generative models and belief dynamics might link to neural representation and the implications of different types of generative models for understanding an agent's cognitive capabilities in relation to its ecological niche. The paper concludes with open questions regarding the evolution of generative models and the development of advanced cognitive abilities-and the gradual transition from pragmatic to detached neural representations. The analysis on offer foregrounds the diverse roles that generative models play in cognitive processes and the evolution of neural representation. (© 2024 The New York Academy of Sciences.) |
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| Grant Information: | 820213 International ERC_ European Research Council |
| Contributed Indexing: | Keywords: action‐oriented models; active inference; explicit models; generative model; neural representation; predictive coding |
| Entry Date(s): | Date Created: 20240326 Date Completed: 20240419 Latest Revision: 20240419 |
| Update Code: | 20250114 |
| DOI: | 10.1111/nyas.15118 |
| PMID: | 38528782 |
| Database: | MEDLINE |
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