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The right tool for the job? Comparing an evidence accumulation and a naive strategy selection model of decision making
Author(s) -
Newell Ben R.,
Lee Michael D.
Publication year - 2011
Publication title -
journal of behavioral decision making
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 76
eISSN - 1099-0771
pISSN - 0894-3257
DOI - 10.1002/bdm.703
Subject(s) - selection (genetic algorithm) , consistency (knowledge bases) , minimum description length , repertoire , computer science , machine learning , artificial intelligence , sampling (signal processing) , cognition , model selection , sequential sampling , psychology , statistics , mathematics , physics , filter (signal processing) , neuroscience , acoustics , computer vision , spatial distribution
Analyses of multi‐attribute decision problems are dominated by accounts which assume people select from a repertoire of cognitive strategies to make decisions. This paper explores an alternative account based on sequential sampling and evidence accumulation. Two experiments varied aspects of a decision environment to examine competing models of decision behavior. The results highlighted the intra‐participant consistency but inter‐participant differences in the amount of evidence considered in decisions. This pattern was best captured by a sequential evidence accumulation model (SEQ) which treated pure Take‐The‐Best (TTB) and pure “rational” (RAT) models as special cases of a single model. The SEQ model was also preferred by the minimum description length (MDL) criterion to a naive strategy‐selection model (NSS) which assumed that TTB or RAT could be selected with some probability for each decision. Copyright © 2010 John Wiley & Sons, Ltd.