Visual attention matters during word recognition: A Bayesian modeling approach.

It is striking that visual attention, the process by which attentional resources are allocated in the visual field so as to locally enhance visual perception, is a pervasive component of models of eye movements in reading, but is seldom considered in models of isolated word recognition. We describe BRAID, a new Bayesian word-Recognition model with Attention, Interference and Dynamics. As most of its predecessors, BRAID incorporates three sensory, perceptual, and orthographic knowledge layers together with a lexical membership submodel. Its originality resides in also including three mechanisms that modulate letter identification within strings: an acuity gradient, lateral interference, and visual attention. We calibrated the model such that its temporal scale was consistent with behavioral data, and then explored the model's capacity to generalize to other, independent effects. We evaluated the model's capacity to account for the word length effect in lexical decision, for the optimal viewing position effect, and for the interaction of crowding and frequency effects in word recognition. We further examined how these effects were modulated by variations in the visual attention distribution. We show that visual attention modulates all three effects and that a narrow distribution of visual attention results in performance patterns that mimic those reported in impaired readers. Overall, the BRAID model could be conceived as a core building block, towards the development of integrated models of reading aloud and eye movement control, or of visual recognition of impaired readers, or any context in which visual attention does matter.