title: 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.
authors: Centre National de la Recherche Scientifique and Laboratoire de Psychologie Cognitive, Aix-Marseille University, Marseille, France.
authors: Stéphane
authors: S
authors: Dufau
authors: Jonathan
authors: J
authors: Grainger
authors: Johannes C
authors: JC
authors: Ziegler
mesh_terms: D003198:Computer Simulation; D003657:Decision Making; D006801:Humans; D008962:Models, Theoretical; D011930:Reaction Time