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  Generalization and search in risky environments

Schulz, E., Wu, C., Huys, Q., Krause, A., & Speekenbrink, M. (2018). Generalization and search in risky environments. Cognitive Science, 42(8), 2592-2620. doi:10.1111/cogs.12695.

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Schulz, E1, Author           
Wu, CM, Author           
Huys, QJM, Author
Krause, A, Author
Speekenbrink, M, Author
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1External Organizations, ou_persistent22              

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 Abstract: How do people pursue rewards in risky environments, where some outcomes should be avoided at all costs? We investigate how participant search for spatially correlated rewards in scenarios where one must avoid sampling rewards below a given threshold. This requires not only the balancing of exploration and exploitation, but also reasoning about how to avoid potentially risky areas of the search space. Within risky versions of the spatially correlated multi-armed bandit task, we show that participants' behavior is aligned well with a Gaussian process function learning algorithm, which chooses points based on a safe optimization routine. Moreover, using leave-one-block-out cross-validation, we find that participants adapt their sampling behavior to the riskiness of the task, although the underlying function learning mechanism remains relatively unchanged. These results show that participants can adapt their search behavior to the adversity of the environment and enrich our understanding of adaptive behavior in the face of risk and uncertainty.

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 Dates: 2018-11
 Publication Status: Published in print
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 Identifiers: DOI: 10.1111/cogs.12695
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Title: Cognitive Science
Source Genre: Journal
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Publ. Info: Kidlington, Oxford, UK [etc.] : No longer published by Elsevier
Pages: - Volume / Issue: 42 (8) Sequence Number: - Start / End Page: 2592 - 2620 Identifier: ISSN: 0364-0213
CoNE: https://pure.mpg.de/cone/journals/resource/954925523741