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

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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 |
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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. This is a post-peer-review, pre-copyedit version of an article published in Psychonomic bulletin and review. The final authenticated version is available online at: http://dx.doi.org/10.3758/s13423-017-1259-y |
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