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A Diffusion Decision Model Analysis of Evidence Variability in the Lexical Decision Task

Citation

Tillman, G and Osth, A and van Ravenzwaaij, D and Heathcote, A, A Diffusion Decision Model Analysis of Evidence Variability in the Lexical Decision Task, Psychonomic Bulletin and Review ISSN 1069-9384 (2017) [Refereed Article]

Copyright Statement

Copyright 2017 Psychonomic Society, Inc.

DOI: doi:10.3758/s13423-017-1259-y

Abstract

The lexical-decision task is among the most commonly used paradigms in psycholinguistics. In both the signal-detection theory and Diffusion Decision Model (DDM; Ratcliff, Gomez, & McKoon, 2004) frameworks, lexical-decisions are based on a continuous source of word-likeness evidence for both words and non-words. The Retrieving Effectively from Memory model of Lexical-Decision (REM–LD; Wagenmakers et al., 2004) provides a comprehensive explanation of lexical-decision data and makes the prediction that word-likeness evidence is more variable for words than non-words and that higher frequency words are more variable than lower frequency words. To test these predictions, we analyzed five lexical-decision data sets with the DDM. For all data sets, drift-rate variability changed across word frequency and non-word conditions. For the most part, REM–LD’s predictions about the ordering of evidence variability across stimuli in the lexical-decision task were confirmed.

Item Details

Item Type:Refereed Article
Keywords:Lexical-decision task, Diffusion Decision Model, REM-LD
Research Division:Psychology and Cognitive Sciences
Research Group:Cognitive Sciences
Research Field:Computer Perception, Memory and Attention
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Psychology and Cognitive Sciences
Author:Heathcote, A (Professor Andrew Heathcote)
ID Code:114614
Year Published:2017
Deposited By:Psychology
Deposited On:2017-02-22
Last Modified:2017-03-28
Downloads:0

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