title: The hierarchical sparse selection model of visual crowding.
abstract: Because the environment is cluttered, objects rarely appear in isolation. The visual system must therefore attentionally select behaviorally relevant objects from among many irrelevant ones. A limit on our ability to select individual objects is revealed by the phenomenon of visual crowding: an object seen in the periphery, easily recognized in isolation, can become impossible to identify when surrounded by other, similar objects. The neural basis of crowding is hotly debated: while prevailing theories hold that crowded information is irrecoverable - destroyed due to over-integration in early stage visual processing - recent evidence demonstrates otherwise. Crowding can occur between high-level, configural object representations, and crowded objects can contribute with high precision to judgments about the "gist" of a group of objects, even when they are individually unrecognizable. While existing models can account for the basic diagnostic criteria of crowding (e.g., specific critical spacing, spatial anisotropies, and temporal tuning), no present model explains how crowding can operate simultaneously at multiple levels in the visual processing hierarchy, including at the level of whole objects. Here, we present a new model of visual crowding-the hierarchical sparse selection (HSS) model, which accounts for object-level crowding, as well as a number of puzzling findings in the recent literature. Counter to existing theories, we posit that crowding occurs not due to degraded visual representations in the brain, but due to impoverished sampling of visual representations for the sake of perception. The HSS model unifies findings from a disparate array of visual crowding studies and makes testable predictions about how information in crowded scenes can be accessed.
authors.affiliation: Vision Science Graduate Group, University of California, Berkeley Berkeley, CA, USA.
authors.forename: Wesley
authors.initials: W
authors.lastname: Chaney
authors.affiliation: Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology Cambridge, MA, USA ; Department of Psychology, University of California, Berkeley Berkeley, CA, USA.
authors.forename: Jason
authors.initials: J
authors.lastname: Fischer
authors.affiliation: Vision Science Graduate Group, University of California, Berkeley Berkeley, CA, USA ; Department of Psychology, University of California, Berkeley Berkeley, CA, USA.
authors.forename: David
authors.initials: D
authors.lastname: Whitney
keywords: attention; coarse coding; ensemble coding; neural network; perception; summary statistics; visual attention