488 P SUnderstanding the cognitive processes underlying visual word recognition remains a major challenge in psycholinguistics, cognitive psychology, and cognitive science. In fewer than 30 years, a large amount of work has identified a number of relevant variables that affect the speed and accuracy with which words can be processed (for reviews, see Balota, Yap, & Cortese, 2006;Rastle, 2007). However, at the same time, it is becoming clear that the existing approach has its limitations. Nearly all research has been based on small studies involving a limited set of monosyllabic, monomorphemic words selected according to factorial designs with a limited number of independent variables matched on a series of control variables.The emphasis on monosyllabic words can easily be understood by the facts that these words are relatively simple stimuli to work with, that researchers wanted to limit the number of words implemented in their computational models, and that, for these words, a lot of ratings about their lexical characteristics are available (such as subjective frequency, age of acquisition, imageability, etc.; e.g., Balota, Pilotti, & Cortese, 2001;Brysbaert & New, 2009;Cortese & Fugett, 2004;Cortese & Khanna, 2008;Desrochers & Thompson, 2009;Ferrand et al., 2008;New, Pallier, Brysbaert, & Ferrand, 2004;Stadthagen-Gonzalez & Davis, 2006).The strong emphasis on monosyllabic monomorphemic words is a serious limitation, however, given that they represent fewer than 15% of the words known. As Yap and Balota (2009) recently argued, the important next step is to understand the cognitive processes underlying the visual word recognition of more complex words-that is, polysyllabic and polymorphemic words (for such a view, see also Assink & Sandra, 2003;Ferrand & New, 2003;Ferrand & Segui, 2003). The English Lexicon ProjectAn interesting alternative approach was initiated by Balota et al. (2007) The French Lexicon Project involved the collection of lexical decision data for 38,840 French words and the same number of nonwords. It was directly inspired by the English Lexicon Project (Balota et al., 2007) and produced very comparable frequency and word length effects. The present article describes the methods used to collect the data, reports analyses on the word frequency and the word length effects, and describes the Excel files that make the data freely available for research purposes. The word and pseudoword data from this article may be downloaded from http://brm.psychonomic-journals.org/content/supplemental.

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Recent studies have shown that the suggestion for participants to construe words as meaningless symbols reduces, or even eliminates, standard Stroop interference in highly suggestible individuals (Raz, Fan, & Posner, 2005;Raz, Kirsch, Pollard, & Nitkin-Kaner, 2006). In these studies, the researchers consequently concluded that this suggestion de-automatizes word reading. The aim of the present study was to closely examine this claim. To this end, highly suggestible individuals completed both standard and semantically based Stroop tasks, either with or without a suggestion to construe the words as meaningless symbols (manipulated in both a between-participants [Exp. 1] and a within-participants [Exp. 2] design). By showing that suggestion substantially reduced standard Stroop interference, these two experiments replicated Raz et al. 's (2006) results. However, in both experiments we also found significant semantically based Stroop effects of similar magnitudes in all suggestion conditions. Taken together, these results indicate that the suggestion to construe words as meaningless symbols does not eliminate, or even reduce, semantic activation (assessed by the semantically based Stroop effect) in highly suggestible individuals, and that such an intervention most likely reduces nonsemantic taskrelevant response competition related to the standard Stroop task. In sum, contrary to Raz et al.'s claim, suggestion does not de-automatize or prevent reading (as shown by a significant amount of semantic processing), but rather seems to influence response competition. These results also add to the growing body of evidence showing that semantic activation in the Stroop task is indeed automatic.