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Abstract

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

Item Type:	Article
Authors/Creators:	Tillman, G and Osth, A and van Ravenzwaaij, D and Heathcote, A
Keywords:	Lexical-decision task, Diffusion Decision Model, REM-LD
Journal or Publication Title:	Psychonomic Bulletin and Review
Publisher:	Psychonomic Soc Inc
ISSN:	1069-9384
DOI / ID Number:	10.3758/s13423-017-1259-y
Copyright Information:	Copyright 2017 Psychonomic Society, Inc.
Related URLs:	Google Scholar: Heathcote, A UTAS Profile: Heathcote, A
Item Statistics:	View statistics for this item

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