CompCog: Advancing Understanding of Visual Crowding

大部分的视觉是周边视觉。中央凹仅包括1.7度的视野，留下99.99%的视觉输入落在周边视网膜上。周边视觉以复杂和有趣的方式不同于中央视觉，最重要的是由于“拥挤”，其中识别周边刺激可能会因其他附近刺激的存在而受到严重损害。拥挤是视觉的一个关键瓶颈;大量的行为证据表明，拥挤极大地限制了我们执行大多数现实世界视觉任务的能力。研究小组将把一种占主导地位的模型（称为池化模型）与最近丰富的实验工作进行对比，这些实验工作似乎表明视觉机制可能比以前认为的更复杂、更动态。这项工作可以为老年性黄斑变性患者开发改进的材料和工具，其中患者失去中心视力。它还可以帮助设计抬头显示器，用户界面和信息可视化，如军事决策的情况地图。池模型解释说，拥挤产生于大的局部区域的平均特征，固定的大小，从固定点的距离线性增长。PI在过去的十年里一直在开发计算和行为方法，这些方法允许人们从最先进的池模型中获得定量的、可测试的预测。使用这些工具，PI将研究一组明确的拥挤现象，这些现象似乎对汇集机制方面的统一账户提出了挑战。她将采取三管齐下的方法：1）使用建模和行为实验来检查模型挑战背后的假设; 2）使用行为实验来消除可能的混淆; 3）设计新实验来直接测试替代理论。预期的总体影响是从根本上推进对外围处理中关键瓶颈的机械理解，从而更普遍地促进视觉处理。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Most of vision is peripheral vision. The central fovea comprises only 1.7 degrees of the visual field, leaving 99.99% of the visual input to fall on the peripheral retina. Peripheral vision differs from central vision in complex and interesting ways, most importantly due to 'crowding', in which identifying a peripheral stimulus can be substantially impaired by the presence of other, nearby stimuli. Crowding is a critical bottleneck in vision; substantial behavioral evidence demonstrates that crowding greatly limits our ability to perform most real-world visual tasks. The research team will pit a dominant class of models, known as pooling models, against a rich corpus of recent experimental work which has seemed to suggest that visual mechanisms might be more complicated and dynamic than previously thought. This work could enable development of improved materials and tools for those with age-related macular degeneration, in which patients lose central vision. It could also aid design of heads-up displays, user interfaces, and information visualizations such as situation maps for military decision-making.Pooling models posit that crowding arises from averaging of features over large local regions, fixed in size, which grow linearly with distance from the point of fixation. The PI has spent the last decade developing the computational and behavioral methodologies that allow one to derive quantitative, testable predictions from a state-of-the-art pooling model. Using these tools, the PI will examine a definitive set of crowding phenomena that have appeared to challenge a unifying account in terms of a pooling mechanism. She will take a three-pronged approach: 1) using modeling and behavioral experiments to examine the assumptions underlying the model challenges; 2) using behavioral experiments to eliminate possible confounds; and 3) designing new experiments to directly test alternative theories. The expected overall impact is to fundamentally advance mechanistic understanding of a key bottleneck in peripheral processing, and thus in visual processing more generally.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.