Computational mechanisms of curiosity and goal-directed exploration

Schwartenbeck, Philipp, Passecker, Johannes, Hauser, Tobias U, FitzGerald, Thomas Hb ORCID: https://orcid.org/0000-0002-3855-1591, Kronbichler, Martin and Friston, Karl J (2019) Computational mechanisms of curiosity and goal-directed exploration. eLife, 8. ISSN 2050-084X

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Abstract

Successful behaviour depends on the right balance between maximising reward and soliciting information about the world. Here, we show how different types of information-gain emerge when casting behaviour as surprise minimisation. We present two distinct mechanisms for goal-directed exploration that express separable profiles of active sampling to reduce uncertainty. 'Hidden state' exploration motivates agents to sample unambiguous observations to accurately infer the (hidden) state of the world. Conversely, 'model parameter' exploration, compels agents to sample outcomes associated with high uncertainty, if they are informative for their representation of the task structure. We illustrate the emergence of these types of information-gain, termed active inference and active learning, and show how these forms of exploration induce distinct patterns of 'Bayes-optimal' behaviour. Our findings provide a computational framework for understanding how distinct levels of uncertainty systematically affect the exploration-exploitation trade-off in decision-making.

Item Type:	Article
Additional Information:	© 2019, Schwartenbeck et al.
Faculty \ School:	Faculty of Social Sciences > School of Psychology
Depositing User:	LivePure Connector
Date Deposited:	16 May 2019 08:30
Last Modified:	04 Mar 2024 17:48
URI:	https://ueaeprints.uea.ac.uk/id/eprint/71013
DOI:	10.7554/eLife.41703

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