The word length effect in Lexical Decision (LD) has been studied in many behavioral experiments but no computational models has yet simulated this effect. We use a new Bayesian model of visual word recognition, the BRAID model, that simulates expert readers performance. BRAID integrates an attentional component modeled by a Gaus-sian probability distribution, a mechanism of lateral interference between adjacent letters and an acuity gradient, but no phonological component. We explored the role of visual attention on the word length effect using 1,200 French words from 4 to 11 letters. A series of five simulations was carried out to assess (a) the impact of a single attentional focus versus multiple shifts of attention on the word length effect and (b) how this effect is modulated by variations in the distribution of attention. Results show that the model successfully simulates the word length effect reported for humans in the French Lexicon Project when allowing multiple shifts of attention for longer words. The magnitude and direction of the effect can be modulated depending on the use of a uniform or narrow distribution of attention. The present study provides evidence that visual attention is critical for the recognition of single words and that a narrowing of the attention distribution might account for the exaggerated length effect reported in some reading disorders.