A Model for Evidence Accumulation 1 Running head: A Model for Evidence Accumulation A Model for Evidence Accumulation in the Lexical Decision Task

We present a new model for lexical decision, REM-LD, that is based on REM theory (e.g., Shiffrin & Steyvers, 1997). REM-LD uses a principled (i.e., Bayes’ rule) decision process that simultaneously considers the diagnosticity of the evidence for the ‘WORD’ response and the ‘NONWORD’ response. The model calculates the odds ratio that the presented stimulus is a word or a nonword by accumulating likelihood ratios for each lexical entry in a small neighborhood of similar words. We report two experiments that used the signal-to-respond paradigm to obtain information about the time course of lexical processing. Experiment 1 verified the prediction of the model that the frequency of the word stimuli affects performance for nonword stimuli. Experiment 2 was done to study the effects of nonword lexicality, word frequency, and repetition priming and to demonstrate how REM-LD can account for the observed results. We discuss how REM-LD can be extended to account for effects of phonology such as the pseudohomophone effect, and how REM-LD can predict response times in the popular ‘respond-when-ready’ paradigm. Several other quantitative models of lexical decision are evaluated with respect to the findings reported here. A Model for Evidence Accumulation 3 A Model for Evidence Accumulation in the Lexical Decision Task In this paper, we propose a new model for lexical decision, REM-LD (standing for retrieving effectively from memory – lexical decision). The REM-LD model is a global familiarity model based on Bayesian principles similar to those used in the recently developed REM models for recognition memory (Diller, Nobel, & Shiffrin, 2001; Nobel & Shiffrin, 2001; Shiffrin & Steyvers, 1997; see also McClelland & Chappell, 1998), recall (e.g., Diller et al., 2001; Malmberg & Shiffrin, in press; Nobel & Shiffrin, 2001), long-term priming in perceptual identification (Schooler, Shiffrin, & Raaijmakers, 2001), and shortterm priming in perceptual identification (Huber, Shiffrin, Lyle, & Ruys, 2001). The REM models constitute a general framework that describes how information is stored and retrieved from memory, and how an optimal decision can be made based on noisy information. The concept of optimal decision making provides a principled basis for modeling the functioning of human memory (cf. ACT-R, Anderson & Lebiere, 1998). We aim to show how the REM principles can be applied in a straightforward fashion to describe performance in a lexical