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dc.contributor.authorModelling Animal Decisions Group:
dc.contributor.authorFawcett, TW
dc.contributor.authorFallenstein, B
dc.contributor.authorHigginson, Andrew D.
dc.contributor.authorHouston, AI
dc.contributor.authorMallpress, DE
dc.contributor.authorTrimmer, PC
dc.contributor.authorMcNamara, JM
dc.date.accessioned2016-02-29T09:12:38Z
dc.date.issued2014-03
dc.description.abstractModels and experiments on adaptive decision-making typically consider highly simplified environments that bear little resemblance to the complex, heterogeneous world in which animals (including humans) have evolved. These studies reveal an array of so-called cognitive biases and puzzling features of behaviour that seem irrational in the specific situation presented to the decision-maker. Here we review an emerging body of work that highlights spatiotemporal heterogeneity and autocorrelation as key properties of most real-world environments that may help us understand why these biases evolved. Ecologically rational decision rules adapted to such environments can lead to apparently maladaptive behaviour in artificial experimental settings. We encourage researchers to consider environments with greater complexity to understand better how evolution has shaped our cognitive systems.en_GB
dc.description.sponsorshipThis work was funded by the European Research Council (Advanced Grant 250209 to A.I.H.) and the Engineering and Physical Sciences Research Council (grant number EP/I032622/1 to Iain D. Gilchrist).en_GB
dc.identifier.citationTrends in Cognitive Sciences, 2014, Vol. 18, Issue 3, pp. 153 - 161en_GB
dc.identifier.doi10.1016/j.tics.2013.12.012
dc.identifier.urihttp://hdl.handle.net/10871/20188
dc.language.isoenen_GB
dc.publisherElsevieren_GB
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pubmed/24467913en_GB
dc.subjectAdaptation, Psychologicalen_GB
dc.subjectAnimalsen_GB
dc.subjectBiological Evolutionen_GB
dc.subjectDecision Makingen_GB
dc.subjectEnvironmenten_GB
dc.subjectHumansen_GB
dc.subjectModels, Psychologicalen_GB
dc.titleThe evolution of decision rules in complex environments.en_GB
dc.typeArticleen_GB
dc.date.available2016-02-29T09:12:38Z
dc.identifier.issn1364-6613
exeter.place-of-publicationEngland
dc.descriptionPublisheden_GB
dc.descriptionResearch Support, Non-U.S. Gov'ten_GB
dc.descriptionReviewen_GB
dc.descriptionThis is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.tics.2013.12.012en_GB
dc.identifier.journalTrends in Cognitive Sciencesen_GB


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