The Neural and Perceptual Mechanisms Underlying Spatial Integration

• Main Author: Blanc-Goldhammer, Daryn
• Other Authors: Dassonville, Paul
• Language: en_US
• Published: University of Oregon 2019
• Subjects: Spatial integration; Visual perception
• Online Access: http://hdl.handle.net/1794/24536

The visual system integrates information over space to see surfaces, contours and edges. This integration can be described by a divisive normalization framework in which surrounding contextual information normalizes response to a central target. We ran a set of studies examining perceptual illusions with the intention of better understanding the neural mechanisms responsible for how the visual system integrates information over space. We measured surround integration using the Simultaneous Tilt Illusion. In the first study, we determined the extent to which the probability that different surround regions were co-assigned to the same object as the center impacts how much they are integrated. We found that the magnitude of the illusion was a sum of regional surround effects weighted by their dependency to the center. These results are consistent with a system that uses prior experience with natural scene statistics to integrate regions of space. In the second study, we measured the relationship between individual differences in spatial integration and autistic traits. We found no evidence for reduced normalization in people who score high on autistic traits. In the third study, we determined the extent to which arousal modulates spatial integration. Although we did not observe an effect of natural fluctuations in arousal, as indexed by pupil diameter, we observed a reduction in the magnitude of the illusion following an alerting tone. While more work is still needed to verify this effect, it suggests that we context information less under moderately alert states. We interpret these results in the context of the neural and perceptual mechanisms underlying spatial integration. Specifically, these results seem to indicate that the normalization process is gated by our expectancies about the structure of a scene and by our internal brain state. These results are consistent with a system that uses prior experience with scene statistics to represent patterns more efficiently.