Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings.

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes thr...

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Autores principales:	Elise Payzan-LeNestour, Peter Bossaerts
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/288f655dafb4488386e9caabcdee5ab1
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spelling	oai:doaj.org-article:288f655dafb4488386e9caabcdee5ab12021-11-18T05:50:47ZRisk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings.1553-734X1553-735810.1371/journal.pcbi.1001048https://doaj.org/article/288f655dafb4488386e9caabcdee5ab12011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21283774/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating.Elise Payzan-LeNestourPeter BossaertsPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 7, Iss 1, p e1001048 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Elise Payzan-LeNestour Peter Bossaerts Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings.
description	Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating.
format	article
author	Elise Payzan-LeNestour Peter Bossaerts
author_facet	Elise Payzan-LeNestour Peter Bossaerts
author_sort	Elise Payzan-LeNestour
title	Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings.
title_short	Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings.
title_full	Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings.
title_fullStr	Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings.
title_full_unstemmed	Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings.
title_sort	risk, unexpected uncertainty, and estimation uncertainty: bayesian learning in unstable settings.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/288f655dafb4488386e9caabcdee5ab1
work_keys_str_mv	AT elisepayzanlenestour riskunexpecteduncertaintyandestimationuncertaintybayesianlearninginunstablesettings AT peterbossaerts riskunexpecteduncertaintyandestimationuncertaintybayesianlearninginunstablesettings
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Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings.

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