Proceedings of the 2001 Fourth International Conference on Cognitive Modeling
Pages (from-to)
205-209
Publisher
Mahwah, NJ, US: Lawrence Erlbaum Associates, Inc., Publishers.
Organisations
Faculty of Social and Behavioural Sciences (FMG) - Psychology Research Institute (PsyRes)
Abstract
from the chapter) A Bayesian-based model for lexical decision, REM-LD, is fit to data from 29 human Ss who participated in 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.