Learning to Represent a Multi-Context Environment: More than Detecting Changes
Identifieur interne : 002359 ( Main/Merge ); précédent : 002358; suivant : 002360Learning to Represent a Multi-Context Environment: More than Detecting Changes
Auteurs : Ting Qian [États-Unis] ; T. Florian Jaeger [États-Unis] ; Richard N. Aslin [États-Unis]Source :
- Frontiers in Psychology [ 1664-1078 ] ; 2012.
Abstract
Learning an accurate representation of the environment is a difficult task for both animals and humans, because the causal structures of the environment are unobservable and must be inferred from the observable input. In this article, we argue that this difficulty is further increased by the multi-context nature of realistic learning environments. When the environment undergoes a change in context without explicit cueing, the learner must detect the change and employ a new causal model to predict upcoming observations correctly. We discuss the problems and strategies that a rational learner might adopt and existing findings that support such strategies. We advocate hierarchical models as an optimal structure for retaining causal models learned in past contexts, thereby avoiding relearning familiar contexts in the future.
Url:
DOI: 10.3389/fpsyg.2012.00228
PubMed: 22833727
PubMed Central: 3400979
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Florian Jaeger</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Department of Brain and Cognitive Sciences, University of Rochester</institution><country>Rochester, NY, USA</country></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Department of Computer Science, University of Rochester</institution><country>Rochester, NY, USA</country></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author><author><name sortKey="Aslin, Richard N" sort="Aslin, Richard N" uniqKey="Aslin R" first="Richard N." last="Aslin">Richard N. 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Florian" last="Jaeger">T. Florian Jaeger</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Department of Brain and Cognitive Sciences, University of Rochester</institution><country>Rochester, NY, USA</country></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Department of Computer Science, University of Rochester</institution><country>Rochester, NY, USA</country></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author><author><name sortKey="Aslin, Richard N" sort="Aslin, Richard N" uniqKey="Aslin R" first="Richard N." last="Aslin">Richard N. Aslin</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Department of Brain and Cognitive Sciences, University of Rochester</institution><country>Rochester, NY, USA</country></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author></analytic><series><title level="j">Frontiers in Psychology</title><idno type="eISSN">1664-1078</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Learning an accurate representation of the environment is a difficult task for both animals and humans, because the causal structures of the environment are unobservable and must be inferred from the observable input. In this article, we argue that this difficulty is further increased by the multi-context nature of realistic learning environments. When the environment undergoes a change in context without explicit cueing, the learner must detect the change and employ a new causal model to predict upcoming observations correctly. We discuss the problems and strategies that a rational learner might adopt and existing findings that support such strategies. We advocate hierarchical models as an optimal structure for retaining causal models learned in past contexts, thereby avoiding relearning familiar contexts in the future.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Abad, M J F" uniqKey="Abad M">M. J. F. Abad</name></author><author><name sortKey="Ramos Alvarez, M M" uniqKey="Ramos Alvarez M">M. M. Ramos-Alvarez</name></author><author><name sortKey="Rosas, J M" uniqKey="Rosas J">J. M. 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Yu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Yu, A J" uniqKey="Yu A">A. J. Yu</name></author><author><name sortKey="Cohen, J D" uniqKey="Cohen J">J. D. Cohen</name></author></analytic></biblStruct></listBibl></div1></back></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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