A hierarchical Bayesian modeling approach to searching and stopping in multi-attribute judgment

van Ravenzwaaij, D., Moore, C. P., Lee, M. D. & Newell, B. R., 2014, In : Cognitive Science. 38, 7, p. 1384-1405 22 p.

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  • A Hierarchical Bayesian Modeling Approach to Searching and Stopping in Multi‐Attribute Judgment

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In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with the names of the cities, but they were able to collect different kinds of cues for both response alternatives (e. g., "Does this city have a university?") before making a decision. Our experiments differed in whether participants were free to determine the number of cues they examined. We demonstrate that a novel model, using hierarchical latent mixtures and Bayesian inference (Lee & Newell, 2011) provides a more complete description of the data from both experiments than simple conventional strategies, such as the take-the-best or the Weighted Additive heuristics.
Original languageEnglish
Pages (from-to)1384-1405
Number of pages22
JournalCognitive Science
Issue number7
Publication statusPublished - 2014
Externally publishedYes



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