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How to say "no" to a nonword: A leaky competing accumulator model of lexical decision

Abstract : We describe a leaky competing accumulator (LCA) model of the lexical decision task that can be used as a response/decision module for any computational model of word recognition. The LCA model uses evidence for a word, operationalized as some measure of lexical activity, as input to the YES decision node. Input to the NO decision node is simply a constant value minus evidence for a word. In this way, evidence for a nonword is a function of time from stimulus onset (as in standard deadline models) modulated by lexical activity via the competitive dynamics of the LCA. We propose a simple mechanism for determining the value of this constant online during the first trials of a lexical decision experiment, such that the model can rapidly optimize speed and accuracy in discriminating words from nonwords. Further optimization is achieved via trial-by-trial adjustments in response criteria as a function of task demands and list context. We show that the LCA model can simulate mean response times and response distributions for correct and incorrect YES and NO decisions for a number of benchmark experiments that have been shown to be fatal for deadline models of lexical decision. Finally, using lexical activity calculated by a computational model of word recognition as input to the LCA decision module, we provide the first item-level simulation of both word and nonword responses in a large-scale database.
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https://hal.archives-ouvertes.fr/hal-01152178
Contributor : Stéphane Dufau Connect in order to contact the contributor
Submitted on : Friday, May 15, 2015 - 1:53:10 PM
Last modification on : Tuesday, October 19, 2021 - 10:58:51 PM

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Stéphane Dufau, Jonathan Grainger, Johannes C. Ziegler. How to say "no" to a nonword: A leaky competing accumulator model of lexical decision. Journal of Experimental Psychology: Learning, Memory, and Cognition, American Psychological Association, 2012, 38 (4), pp.1117--1128. ⟨10.1037/a0026948⟩. ⟨hal-01152178⟩

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