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David Brown, Bosco Tjan; Sparse Coding under Saccade-Confounded Statistics. Journal of Vision 2016;16(12):232. doi: 10.1167/16.12.232.
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© 2017 Association for Research in Vision and Ophthalmology.
Image encoding in visual cortex is thought to be adapted to natural scene statistics. Spatial attention may selectively enhance the acquisition of these statistics. The effect of such attention-gated learning may be especially pronounced in peripheral vision, as the shift of attention to a peripheral visual location is tightly coupled to the initiation of a saccade. We hypothesize that this attention-saccade link biases peripheral visual input, introducing an eye-movement-related confound to the veridical image statistics (Nandy & Tjan, 2012). This, in turn, is predicted to shape learned spatiotemporal receptive fields and lateral interactions in ways that predict deficits of peripheral form vision such as crowding. To model the effect of saccade-confounded statistics on image encoding in V1, we simulated saccades across natural image data and mapped the resulting videos to a model cortical surface with eccentricity-dependent magnification and resolution. We then learned a space-time sparse coding dictionary on samples from a circular patch of model cortex – representing a target location and a laterally-interacting surround, all under an attentional spotlight centered on the target – in a temporal window adjusted to the duration of a transient spatial attention episode (Sperling & Weichselgartner, 1995). This peripheral spacetime dictionary is spatially similar to foveal dictionaries, but with a preference for movement toward the fovea. For a given basis trained on peripheral inputs, only a subinterval in time contains a structured pattern. This subinterval can appear at any time in the basis. The unique features of this dictionary – anisotropic movement preference and a narrow temporal window with variable onset delay – point toward a role for peripheral vision that may differ more substantially from foveal vision than previously thought.
Meeting abstract presented at VSS 2016
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