Bayesian modeling of lexical knowledge acquisition in BRAID, a visual word recognition model

International audienceModeling word orthographic knowledge acquisition is one of the main current challenges. While many computational models simulate reading, word recognition, phonological transcoding or eye movement control in text reading, only one computational study (Ziegler, Perry, & Zorzi, 2014) has attempted to model the acquisition of word orthographic knowledge in children by proposing a dedicated model based on the Self-Teaching hypothesis (Share, 1995, 1999, 2004). This model postulates a fundamental role of phonological processing in the acquisition of word orthographic knowledge. Our team has recently developed BRAID, a new Bayesian model of word recognition ("Bayesian word Recognition with Attention, Interference and Dynamics"), to simulate the performance of expert readers (Phénix, Valdois, & Diard, submitted; Ginestet, Phénix, Diard, & Valdois, submitted). Here, we propose an extension of BRAID by implementing a mechanism for the acquisition of new orthographic knowledge based on predictive calculations of visual-attention parameters. Extension of the BRAID model for orthographic learning BRAID is a hierarchical probabilistic model of visual word recognition composed of 4 submodels. We introduce a top-down dependency, so that contents of the perceptual letter submodel control the attentional parameters during orthographic learning