Proceedings of the Annual Meeting of the Cognitive Science Society

In visual word processing modeling, few models have success-fully accounted for a large variety of tasks, and large corpora ofbehavioral observations. We consider a dataset from a megas-tudy, in which participants performed three tasks (lexical de-cision, word naming, and word recognition in a progressivedemasking situation), on the same, large set of stimuli. Wedefine the BRAID-Phon model, an extension of a previousprobabilistic model, the BRAID model, whose originality isits visuo-attentional component, in which a visuo-attentionaldistribution spatially deploys sensory processing capabilities.BRAID-Phon includes phonological representations of words,allowing simulating the naming task. We simulated the threetasks on the dataset we considered, and analyzed predicted re-action times in terms of word frequency and word length ef-fects. Simulation results show that BRAID-Phon successfullycaptures the direction and order of magnitude of the observedeffects, in all three tasks.