Domain Specificity of Oculomotor Learning after Changes in Sensory Processing.

Humans visually process the world with varying spatial resolution and can program their eye movements optimally to maximize information acquisition for a variety of everyday tasks. Diseases such as macular degeneration can change visual sensory processing, introducing central vision loss (a scotoma). However, humans can learn to direct a new preferred retinal location to regions of interest for simple visual tasks. Whether such learned compensatory saccades are optimal and generalize to more complex tasks, which require integrating information across a large area of the visual field, is not well understood. Here, we explore the possible effects of central vision loss on the optimal saccades during a face identification task, using a gaze-contingent simulated scotoma. We show that a new foveated ideal observer with a central scotoma correctly predicts that the human optimal point of fixation to identify faces shifts from just below the eyes to one that is at the tip of the nose and another at the top of the forehead. However, even after 5000 trials, humans of both sexes surprisingly do not change their initial fixations to adapt to the new optimal fixation points to faces. In contrast, saccades do change for tasks such as object following and to a lesser extent during search. Our findings argue against a central brain motor-compensatory mechanism that generalizes across tasks. They instead suggest task specificity in the learning of oculomotor plans in response to changes in front-end sensory processing and the possibility of separate domain-specific representations of learned oculomotor plans in the brain. The mechanism by which humans adapt eye movements in response to central vision loss is still not well understood and carries importance for gaining a fundamental understanding of brain plasticity. We show that although humans adapt their eye movements for simpler tasks such as object following and search, these adaptations do not generalize to more complex tasks such as face identification. We provide the first computational model to predict where humans with central vision loss should direct their eye movements in face identification tasks, which could become a critical tool in making patient-specific recommendations. Based on these results, we suggest a novel theory for oculomotor learning: a distributed representation of learned eye-movement plans represented in domain-specific areas of the brain.