Ideal observer analysis of crowding and the reduction of crowding through learning.

Crowding is a prominent phenomenon in peripheral vision where nearby objects impede one's ability to identify a target of interest. The precise mechanism of crowding is not known. We used ideal observer analysis and a noise-masking paradigm to identify the functional mechanism of crowding. We tested letter identification in the periphery with and without flanking letters and found that crowding increases equivalent input noise and decreases sampling efficiency. Crowding effectively causes the signal from the target to be noisier and at the same time reduces the visual system's ability to make use of a noisy signal. After practicing identification of flanked letters without noise in the periphery for 6 days, subjects' performance for identifying flanked letters improved (reduction of crowding). Across subjects, the improvement was attributable to either a decrease in crowding-induced equivalent input noise or an increase in sampling efficiency, but seldom both. This pattern of results is consistent with a simple model whereby learning reduces crowding by adjusting the spatial extent of a perceptual window used to gather relevant input features. Following learning, subjects with inappropriately large windows reduced their window sizes; while subjects with inappropriately small windows increased their window sizes. The improvement in equivalent input noise and sampling efficiency persists for at least 6 months.