Most people easily learn to recognize new faces and places, and with more extensive practice they can become experts at visual tasks as complex as radiological diagnosis and action video games. Such perceptual plasticity has been thoroughly studied in the context of training paradigms that require constant fixation. In contrast, when observers learn under more natural conditions, they make frequent saccadic eye movements. Here we show that such eye movements can play an important role in visual learning. Observers performed a task in which they executed a saccade while discriminating the motion of a cued visual stimulus. Additional stimuli, presented simultaneously with the cued one, permitted an assessment of the perceptual integration of information across visual space. Consistent with previous results on perisaccadic remapping [M. Szinte, D. Jonikaitis, M. Rolfs, P. Cavanagh, H. Deubel,J. Neurophysiol.116, 1592–1602 (2016)], most observers preferentially integrated information from locations representing the presaccadic and postsaccadic retinal positions of the cue. With extensive training on the saccade task, these observers gradually acquired the ability to perform similar motion integration without making eye movements. Importantly, the newly acquired pattern of spatial integration was determined by the metrics of the saccades made during training. These results suggest that oculomotor influences on visual processing, long thought to subserve the function of perceptual stability, also play a role in visual plasticity.

The primate visual cortex contains various regions that exhibit specialization for different stimulus properties, such as motion, shape, and color. Within each region there is often further specialization, such that particular stimulus features, such as horizontal and vertical orientations, are overrepresented. These asymmetries are associated with well-known perceptual biases, but little is known about how they influence visual learning. Most theories would predict that learning is optimal, in the sense that it is unaffected by these asymmetries. But other approaches to learning would result in specific patterns of perceptual biases. To distinguish between these possibilities, we trained human observers to discriminate between expanding and contracting motion patterns, which have a highly asymmetrical representation in visual cortex. Observers exhibited biased percepts of these stimuli, and these biases were affected by training in ways that were often suboptimal. We simulated different neural network models and found that a learning rule that involved only adjustments to decision criteria, rather than connection weights, could account for our data. These results suggest that cortical asymmetries influence visual perception and that human observers often rely on suboptimal strategies for learning.