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dc.contributor.advisorDougherty, Michael Ren_US
dc.contributor.authorChrabaszcz, Jeffrey Stephenen_US
dc.date.accessioned2016-06-22T05:55:05Z
dc.date.available2016-06-22T05:55:05Z
dc.date.issued2016en_US
dc.identifierhttps://doi.org/10.13016/M2SF55
dc.identifier.urihttp://hdl.handle.net/1903/18270
dc.description.abstractAn inference task in one in which some known set of information is used to produce an estimate about an unknown quantity. Existing theories of how humans make inferences include specialized heuristics that allow people to make these inferences in familiar environments quickly and without unnecessarily complex computation. Specialized heuristic processing may be unnecessary, however; other research suggests that the same patterns in judgment can be explained by existing patterns in encoding and retrieving memories. This dissertation compares and attempts to reconcile three alternate explanations of human inference. After justifying three hierarchical Bayesian version of existing inference models, the three models are com- pared on simulated, observed, and experimental data. The results suggest that the three models capture different patterns in human behavior but, based on posterior prediction using laboratory data, potentially ignore important determinants of the decision process.en_US
dc.language.isoenen_US
dc.titleUnderstanding information use in multiattribute decision makingen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentNeuroscience and Cognitive Scienceen_US
dc.subject.pqcontrolledCognitive psychologyen_US
dc.subject.pquncontrolledBayesianen_US
dc.subject.pquncontrolledDecisionen_US
dc.subject.pquncontrolledInferenceen_US


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