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Accumulating advantages: a new conceptualization of rapid multiple choice


van Ravenzwaaij, D and Brown, SD and Marley, AAJ and Heathcote, A, Accumulating advantages: a new conceptualization of rapid multiple choice, Psychological Review, 127, (2) pp. 186-215. ISSN 0033-295X (2020) [Refereed Article]

Copyright Statement

Copyright 2019 American Psychological Association

DOI: doi:10.1037/rev0000166


Independent racing evidence-accumulator models have proven fruitful in advancing understanding of rapid decisions, mainly in the case of binary choice, where they can be relatively easily estimated and are known to account for a range of benchmark phenomena. Typically, such models assume a one-to-one mapping between accumulators and responses. We explore an alternative independent-race framework where more than one accumulator can be associated with each response, and where a response is triggered when a sufficient number of accumulators associated with that response reach their thresholds. Each accumulator is primarily driven by the difference in evidence supporting one vs. another response (i.e., that response’s "advantage"), with secondary inputs corresponding to the total evidence for both responses and a constant term. We use Brown and Heathcote’s (2008) LBA to instantiate the framework in a mathematically tractable measurement model (i.e., a model whose parameters can be successfully recovered from data). We show this "Advantage LBA" model provides a detailed quantitative account of a variety of benchmark binary and multiple choice phenomena that traditional independent accumulator models struggle with; in binary choice the effects of additive versus multiplicative changes to input values, and in multiple choice the effects of manipulations of the strength of lure (i.e., non-target) stimuli and Hick’s Law. We conclude that the Advantage LBA provides a tractable new avenue for understanding the dynamics of decisions among multiple choices.

Item Details

Item Type:Refereed Article
Keywords:evidence accumulation models, RT tasks, Hick’s Law, lateral inhibition, max-next
Research Division:Psychology
Research Group:Cognitive and computational psychology
Research Field:Decision making
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in psychology
UTAS Author:Heathcote, A (Professor Andrew Heathcote)
ID Code:134799
Year Published:2020
Web of Science® Times Cited:6
Deposited By:Psychology
Deposited On:2019-09-05
Last Modified:2021-09-07

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