The hierarchical sparse selection model of visual crowding.
The hierarchical sparse selection model of visual crowding.
复制标题
视觉拥挤的层次稀疏选择模型。
DOI：
10.3389/fnint.2014.00073
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发表时间：
2014
影响因子：
3.5
通讯作者：
Whitney D
中科院分区：
文献类型：
--
作者：
Chaney W;Fischer J;Whitney D
因为环境是杂乱的，所以物体很少孤立地出现。因此，视觉系统必须注意地从许多不相关的对象中选择与行为相关的对象。视觉拥挤现象揭示了我们选择单个物体的能力的局限性：在外围看到的物体，在孤立的情况下很容易识别，当被其他类似的物体包围时，就变得不可能识别了。拥挤的神经基础受到了激烈的争论：虽然主流理论认为拥挤的信息是不可恢复的-由于早期视觉处理的过度整合而被破坏-但最近的证据表明并非如此。拥挤可以发生在高级的、配置的对象表示之间，拥挤的对象可以高精度地判断一组对象的“主旨”，即使它们是无法识别的。虽然现有模型可以解释拥挤的基本诊断标准（例如，特定临界间距、空间各向异性和时间调谐），但目前没有模型解释拥挤如何在视觉处理层次的多个层面上同时发生作用，包括在整个物体的层面上。在这里，我们提出了一个新的视觉拥挤模型-分层稀疏选择（HSS）模型，该模型考虑了对象级拥挤，以及最近文献中的一些令人困惑的发现。与现有理论相反，我们假设拥挤的发生不是由于大脑中视觉表征的退化，而是由于为了感知而对视觉表征的采样不足。HSS模型统一了一系列不同的视觉拥挤研究的结果，并对拥挤场景中的信息如何获取做出了可测试的预测。
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.