During reading acquisition, beginning readers transition from serial to more parallel processing. The acquisition of word specific knowledge through orthographic learning is critical for this transition. However, the processes by which orthographic representations are acquired and fine-tuned as learning progresses are not well understood. Our aim was to explore the role of visual attention in this transition through computational modeling. We used the BRAIDLearn model, a Bayesian model of visual word recognition, to simulate the orthographic learning of 700 4-to 10-letter English known words and novel words, presented 5 times each to the model. The visual attention quantity available for letter identification was manipulated in the simulations to assess its influence on the learning process. We measured the overall processing time and number of attentional fixations simulated by the model across exposures and their impact on two markers of serial processing, the lexicality and length effects, depending on visual attention quantity. Results showed that the two lexicality and length effects were modulated by visual attention quantity. The quantity of visual attention available for processing further modulated novel word orthographic learning and the evolution of the length effect on processing time and number of attentional fixations across repeated exposures to novel words. The simulated patterns are consistent with behavioral data and the developmental trajectories reported during reading acquisition. Overall, the model predicts that the efficacy of orthographic learning depends on visual attention quantity and that visual attention may be critical to explain the transition from serial to more parallel processing.