Practice Effects in Large-Scale Visual Word Recognition Studies: A Lexical Decision Study on 14,000 Dutch Mono- and Disyllabic Words and Nonwords

Emmanuel Keuleers,Kevin Diependaele,Marc Brysbaert

doi:10.3389/fpsyg.2010.00174

Abstract

In recent years, psycholinguistics has seen a remarkable growth of research based on the analysis of data from large-scale studies of word recognition, in particular lexical decision and word naming. We present the data of the Dutch Lexicon Project (DLP) in which a group of 39 participants made lexical decisions to 14,000 words and the same number of nonwords. To examine whether the extensive practice precludes comparison with the traditional short experiments, we look at the differences between the first and the last session, compare the results with the English Lexicon Project (ELP) and the French Lexicon Project (FLP), and examine to what extent established findings in Dutch psycholinguistics can be replicated in virtual experiments. Our results show that when good nonwords are used, practice effects are minimal in lexical decision experiments and do not invalidate the behavioral data. For instance, the word frequency curve is the same in DLP as in ELP and FLP. Also, the Dutch–English cognate effect is the same in DLP as in a previously published factorial experiment. This means that large-scale word recognition studies can make use of psychophysical and psychometrical approaches. In addition, our data represent an important collection of very long series of individual reaction times that may be of interest to researchers in other areas.

Highlights

In contrast with previous large word recognition studies that used many participants responding to a small part of the stimuli, the participants in our study responded to all stimuli
Many studies nowadays make use of mixed effects methods to analyze the data. These methods do not rely on average data per stimulus, but take into account participants and items as random effects
Having the same participants for all items allows for less complex interactions between participants and items and, for a better estimation of these random effects

Summary

Introduction

These methods do not rely on average data per stimulus, but take into account participants and items as random effects. We were able to replicate the core findings of Dutch studies using lexical decisions to printed words (Tables 4–8). We used linear mixed effects models on trial level data, a method of analysis that may be more powerful than the item-level analysis used by Sibley et al Second, Sibley et al looked only at the frequency-regularity interaction in naming, leaving open the possibility that variables related to this particular effect may make it hard to replicate the findings using megastudy data.

Results

Conclusion