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A diffusion decision model analysis of evidence variability in the lexical decision task

A diffusion decision model analysis of evidence variability in the lexical decision task

Published in: Psychonomic Bulletin & Review, Volume 24, Issue 6, December 2017, 1949-1956

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, Psychological Review, 111, 159–182, 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., Cognitive Psychology, 48(3), 332–367, 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.”

Written by: Gabriel Tillman, Adam F. Osth, Don van Ravenzwaaij, Andrew Heathcote
For full text: https://doi.org/10.3758/s13423-017-1259-y

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