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• W2024101135 abstract "The goal of the current research is to use the experimental methods and mathematical models of the information integration framework to precisely determine how category and feature information are combined when making an inference. In three experiments, participants were trained on a probabilistic relationship between a category label and the presence of a property and, separately, the relationship between a visual feature and the presence of the property. Participants were then shown the category label alone, the feature alone, or both in combination, and asked to infer the presence or absence of the property. Two information integration models, the fuzzy logical model of perception and the linear integration model, were fit to the data. The modeling results show that participants were non-Bayesian in their combination of the two sources of information, showed diversity in the relative weight placed on category information, and consistently used each source of information to the extent to which it was known." @default.
• W2024101135 created "2016-06-24" @default.
• W2024101135 creator A5043321979 @default.
• W2024101135 creator A5081108174 @default.
• W2024101135 date "2014-12-01" @default.
• W2024101135 modified "2023-10-14" @default.
• W2024101135 title "Modeling the Roles of Category and Feature Information in Inference" @default.
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• W2024101135 doi "https://doi.org/10.1027/1618-3169/a000247" @default.
• W2024101135 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24351986" @default.
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