{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T15:44:21Z","timestamp":1783698261188,"version":"3.55.0"},"reference-count":92,"publisher":"MIT Press","issue":"4","license":[{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,3,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Recent advances in theoretical biology suggest that key definitions of basal cognition and sentient behavior may arise as emergent properties of in vitro cell cultures and neuronal networks. Such neuronal networks reorganize activity to demonstrate structured behaviors when embodied in structured information landscapes. In this article, we characterize this kind of self-organization through the lens of the free energy principle, that is, as self-evidencing. We do this by first discussing the definitions of reactive and sentient behavior in the setting of active inference, which describes the behavior of agents that model the consequences of their actions. We then introduce a formal account of intentional behavior that describes agents as driven by a preferred end point or goal in latent state-spaces. We then investigate these forms of (reactive, sentient, and intentional) behavior using simulations. First, we simulate the in vitro experiments, in which neuronal cultures modulated activity to improve gameplay in a simplified version of Pong by implementing nested, free energy minimizing processes. The simulations are then used to deconstruct the ensuing predictive behavior, leading to the distinction between merely reactive, sentient, and intentional behavior with the latter formalized in terms of inductive inference. This distinction is further studied using simple machine learning benchmarks (navigation in a grid world and the Tower of Hanoi problem) that show how quickly and efficiently adaptive behavior emerges under an inductive form of active inference.<\/jats:p>","DOI":"10.1162\/neco_a_01738","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T21:32:31Z","timestamp":1741037551000},"page":"666-700","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":16,"title":["Active Inference and Intentional Behavior"],"prefix":"10.1162","volume":"37","author":[{"given":"Karl J.","family":"Friston","sequence":"first","affiliation":[{"name":"Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London WC1N 3AR, U.K."},{"name":"VERSES AI Research Lab, Los Angeles, California, 90016, U.S. k.friston@ucl.ac.uk"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tommaso","family":"Salvatori","sequence":"additional","affiliation":[{"name":"VERSES AI Research Lab, Los Angeles, California, 90016, U.S. tommaso.salvatori@verses.ai"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takuya","family":"Isomura","sequence":"additional","affiliation":[{"name":"Brain Intelligence Theory Unit, RIKEN Center for Brain Science, Wako, Saitama, 351-0198, Japan takuya.isomura@riken.jp"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Tschantz","sequence":"additional","affiliation":[{"name":"VERSES AI Research Lab, Los Angeles, California, 90016, U.S.A. alexander.tschantz@verses.ai"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Kiefer","sequence":"additional","affiliation":[{"name":"VERSES AI Research Lab, Los Angeles, California, 90016, U.S.A. alex.kiefer@verses.ai"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tim","family":"Verbelen","sequence":"additional","affiliation":[{"name":"VERSES AI Research Lab, Los Angeles, California, 90016, U.S.A. tim.verbelen@verses.ai"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Magnus","family":"Koudahl","sequence":"additional","affiliation":[{"name":"VERSES AI Research Lab, Los Angeles, California, 90016, U.S.A. magnus.koudahl@verses.ai"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aswin","family":"Paul","sequence":"additional","affiliation":[{"name":"VERSES AI Research Lab, Los Angeles, California, 90016, U.S."},{"name":"Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC 3800 Australia"},{"name":"IITB-Monash Research Academy, Mumbai-4000076, India aswin.paul@verses.ai"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Parr","sequence":"additional","affiliation":[{"name":"Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU, U.K. thomas.parr.12@alumni.ucl.ac.uk"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adeel","family":"Razi","sequence":"additional","affiliation":[{"name":"Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC 3800 Australia"},{"name":"Monash Biomedical Imaging, Monash University, Clayton, VIC 3800 Australia"},{"name":"CIFAR Azrieli Global Scholars Program, Toronto, ON M5G 1M1 adeel.razi@monash.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brett J.","family":"Kagan","sequence":"additional","affiliation":[{"name":"Cortical Labs, Melbourne, Australia Brett@corticallabs.com"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher L.","family":"Buckley","sequence":"additional","affiliation":[{"name":"VERSES AI Research Lab, Los Angeles, California, 90016, U.S.A. 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