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dc.contributor.author
Stankevicius, Aistis
dc.contributor.author
Huys, Quentin J.M.
dc.contributor.author
Kalra, Aditi
dc.contributor.author
Seriès, Peggy
dc.contributor.editor
Loewenstein, Yonatan
dc.date.accessioned
2019-01-18T12:45:45Z
dc.date.available
2017-06-11T10:12:02Z
dc.date.available
2019-01-18T12:45:45Z
dc.date.issued
2014-05-22
dc.identifier.issn
1553-734X
dc.identifier.issn
1553-7358
dc.identifier.other
10.1371/journal.pcbi.1003605
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/85954
dc.identifier.doi
10.3929/ethz-b-000085954
dc.description.abstract
Optimists hold positive a priori beliefs about the future. In Bayesian statistical theory, a priori beliefs can be overcome by experience. However, optimistic beliefs can at times appear surprisingly resistant to evidence, suggesting that optimism might also influence how new information is selected and learned. Here, we use a novel Pavlovian conditioning task, embedded in a normative framework, to directly assess how trait optimism, as classically measured using self-report questionnaires, influences choices between visual targets, by learning about their association with reward progresses. We find that trait optimism relates to an a priori belief about the likelihood of rewards, but not losses, in our task. Critically, this positive belief behaves like a probabilistic prior, i.e. its influence reduces with increasing experience. Contrary to findings in the literature related to unrealistic optimism and self-beliefs, it does not appear to influence the iterative learning process directly.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Public Library of Science
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Optimism as a Prior Belief about the Probability of Future Reward
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
PLoS Computational Biology
ethz.journal.volume
10
en_US
ethz.journal.issue
5
en_US
ethz.journal.abbreviated
PLOS comput. biol.
ethz.pages.start
e1003605
en_US
ethz.size
9 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
005410277
ethz.publication.place
San Francisco, CA, USA
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02631 - Institut für Biomedizinische Technik / Institute for Biomedical Engineering::03955 - Stephan, Klaas E. / Stephan, Klaas E.
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02631 - Institut für Biomedizinische Technik / Institute for Biomedical Engineering::03955 - Stephan, Klaas E. / Stephan, Klaas E.
ethz.date.deposited
2017-06-11T10:13:52Z
ethz.source
ECIT
ethz.identifier.importid
imp5936520aab82023239
ethz.ecitpid
pub:135408
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-31T16:40:55Z
ethz.rosetta.lastUpdated
2018-11-02T15:02:06Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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