Infoscience

Thesis

Vision in clutter: neural correlates of visual crowding

Perception of a visual target can strongly deteriorate in the presence of flanking elements (crowding). Crowding is the main limiting factor in reading and a major contributor to poor vision in amblyopia. In crowding, elements appear jumbled and thus are hard to recognize. Classic pooling models propose that crowding occurs via pooling of information from low-level to high-level visual areas. Because of pooling, receptive fields become larger and, at final stages, encompass both the target's and flankers' locations. As a result, the brain is unable to perceive the target separately from the flankers. Pooling models predict that adding flankers or increasing the size of the flankers should always increase crowding strength. In a series of previous studies from our laboratory and other groups, this view was shown to be over-simplistic and incomplete. In many situations, crowding can be reduced and good performance recovered by adding flanking elements or by increasing their size. This can occur when flankers ungroup from the target and group with each other. Grouping between elements is thus a key component in crowding. The neural mechanisms of crowding are unknown at the moment. Here, I used EEG to study the time course of crowding and the brain areas involved in the processing of crowding. Using event-related potentials (ERPs), I showed that crowding is a late visual process. In particular, the N1 component of ERPs (around 190 ms) was suppressed in crowding while earlier components did not correlate with crowding. The N1 suppression was caused by reduced neural activity in high-level visual areas such as the lateral occipital cortex. In a second study, I attempted to replicate the results with gabor stimuli. In a third study, I used steady-state visually evoked potentials (ssVEPs) to disentangle target and flanker processing in crowding. In this study, I showed that the target is selectively suppressed when it groups with the flankers. In addition to these three projects on crowding, I worked on time-frequency analysis that I performed on backward masking data in schizophrenia patients.

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