A Bayesian model for the time course of lexical processing.
A Bayesian-based model for lexical decision, REM-LD, is fit to data from a novel version of a signal-to-respond paradigm. REM-LD calculates the odds that a test item is a word, by accumulating likelihood ratios for each lexical entry in a small neighborhood of similar words. The new model predicts the time course of observed effects of nonword lexicality, word frequency and repetition priming. It can also make qualitative predictions for the response time distributions in tasks with subject paced responding.