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A dual process theory of optimistic cognition

dc.contributor.authorSunehag, Peter
dc.contributor.authorHutter, Marcus
dc.date.accessioned2015-08-14T04:53:30Z
dc.date.available2015-08-14T04:53:30Z
dc.date.issued2014-07
dc.description.abstractOptimism is a prevalent bias in human cognition including variations like self-serving beliefs, illusions of control and overly positive views of one's own future. Further, optimism has been linked with both success and happiness. In fact, it has been described as a part of human mental well-being which has otherwise been assumed to be about being connected to reality. In reality, only people suffering from depression are realistic. Here we study a formalization of optimism within a dual process framework and study its usefulness beyond human needs in a way that also applies to artificial reinforcement learning agents. Optimism enables systematic exploration which is essential in an (partially) unknown world. The key property of an optimistic hypothesis is that if it is not contradicted when one acts greedily with respect to it, then one is well rewarded even if it is wrong.en_AU
dc.identifier.isbn978-1-63439-116-0en_AU
dc.identifier.urihttp://hdl.handle.net/1885/14722
dc.publisherThe Cognitive Science Societyen_AU
dc.relationhttp://purl.org/au-research/grants/arc/DP120100950en_AU
dc.relation.ispartof36th Annual Meeting of the Cognitive Science Society (CogSci 2014): Cognitive Science Meets Artificial Intelligence: Human and Artificial Agents in Interactive Contextsen_AU
dc.rights© The Author(s).en_AU
dc.subjectRationalityen_AU
dc.subjectOptimismen_AU
dc.subjectOptimalityen_AU
dc.subjectReinforcement Learningen_AU
dc.titleA dual process theory of optimistic cognitionen_AU
dc.typeConference paperen_AU
local.bibliographicCitation.lastpage2954en_AU
local.bibliographicCitation.startpage2949en_AU
local.contributor.affiliationSunehag, P., Research School of Computer Science, The Australian National Universityen_AU
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National Universityen_AU
local.contributor.authoruidu4350841en_AU
local.type.statusPublished Versionen_AU

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