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  Inference and search on graph-structured spaces

Wu, C., Schulz, E., & Gershman, S. (2021). Inference and search on graph-structured spaces. Computational Brain & Behavior, 4, 125-147. doi:10.1007/s42113-020-00091-x.

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 Creators:
Wu, CM, Author              
Schulz, E1, 2, Author              
Gershman, SJ, Author
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1Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3189356              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: How do people learn functions on structured spaces? And how do they use this knowledge to guide their search for rewards in situations where the number of options is large? We study human behavior on structures with graph-correlated values and propose a Bayesian model of function learning to describe and predict their behavior. Across two experiments, one assessing function learning and one assessing the search for rewards, we find that our model captures human predictions and sampling behavior better than several alternatives, generates human-like learning curves, and also captures participants’ confidence judgements. Our results extend past models of human function learning and reward learning to more complex, graph-structured domains.

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 Dates: 2020-112021-06
 Publication Status: Published in print
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 Identifiers: DOI: 10.1007/s42113-020-00091-x
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Title: Computational Brain & Behavior
Source Genre: Journal
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Pages: 2 Volume / Issue: 4 Sequence Number: - Start / End Page: 125 - 147 Identifier: -