Searching for rewards in graph-structured spaces
Keyword(s):
How do people generalize and explore structured spaces? We study human behavior on a multi-armed bandit task, where rewards are influenced by the connectivity structure of a graph. A detailed predictive model comparison shows that a Gaussian Process regression model using a diffusion kernel is able to best describe participant choices, and also predict judgments about expected reward and confidence. This model unifies psychological models of function learning with the Successor Representation used in reinforcement learning, thereby building a bridge between different models of generalization.
2014 ◽
Vol 109
(507)
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pp. 1123-1133
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2015 ◽
Vol 29
(06)
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pp. 1555011
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2021 ◽
Vol 284
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pp. 124710
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2019 ◽
Vol 33
(11)
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pp. 3929-3947
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2018 ◽
Vol 71
(5)
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pp. 1055-1068
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2018 ◽
Vol 56
(14)
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pp. 4860-4873
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2016 ◽
Vol III-3
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pp. 271-277