When silent letters say more than a thousand words: An implementation and evaluation of CDP++ in French

International audience; Cross-language comparisons can provide important constraints on our understanding of how people read aloud. French is an interesting case because it differs from most other writing systems in that it uses a large number of multi-letter vowel graphemes and consonants that are systematically silent (i.e., do not map to any lexical phonology; e.g., (Top). Here, we developed a French version of the Connectionist Dual Process Model of Reading Aloud (CDP++) that can handle multisyllabic stimuli (up to three syllables) and has a large-scale lexicon of more than 100,000 words. We tested the model on extant data and an additional experiment examining the reading aloud of nonwords with potentially silent letters. The results from the extant data showed that the model was able to capture a number of important psycholinguistic effects in the literature and explained between 52% and 67% of the item-specific variance in two large databases. The results of the silent-letter experiment showed that, contrary to what would be predicted on the basis of lexical database statistics, people generally pronounce ``silent'' consonants in nonwords. We show that the French CDP++ model faithfully predicted this effect because it implements a linear mapping between orthography and phonology. These findings highlight the theoretical and practical significance of using computational models to help determine the processes and representations that underlie skilled reading. (C) 2014 Elsevier Inc. All rights reserved.