The visual system is equipped with a powerful plasticity mechanism, perceptual learning, which serves to improve perception of consistent inputs. However, the signals it receives are extremely variable. How variability affects perceptual learning is unclear. Here, I ask how the visual system tackles the challenge of variability for learning: variability could impair perceptual learning, or, like in language and motor learning, result in the ability to generalize from trained to new materials. To create effective training programs, e.g., for clinical applications, it is crucial to know how to reap the benefits of variability, or, conversely, to overcome the challenges variability poses. Yet, the neural mechanisms by which the visual system copes with variability are unknown, hampering this endeavor. To close this gap, I propose a new theory, derived from the architecture of cortex: I hypothesize that perceptual learning is not limited to early visual areas, but flexibly occurs at a ‘sweet spot’ along the visual hierarchy whose functional properties match the variability in the given environment. To test this theory, I build on a multimodal, multispecies approach I have previously developed to study learning: I will identify general principles by which variability affects perceptual learning in behavior, dissect the critical neural circuits in macaque monkeys and humans with neuroimaging, determine the functional characteristics of neurons contributing to learning by electrophysiology, and establish their causal relevance using electrical stimulation. This unique combination of species and techniques is ideally suited to unravel the neural mechanism for coping with variability in perceptual learning. By elucidating the computations and mechanisms by which the visual system handles one of the most characteristic aspects of its inputs, I aim to provide the basis for neuroscience-based training paradigms that help alleviate vision deficits.
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Funding SchemeERC-STG - Starting Grant