CompCog: Advancing Understanding of Visual Crowding

大部分的视觉是周边视觉。中央中央凹只包含1.7度的视野，剩下99.99%的视觉输入落在周围视网膜上。周边视觉与中心视觉在复杂而有趣的方面有所不同，最重要的是由于“拥挤”，在这种情况下，识别外围刺激可能会因附近其他刺激的存在而严重受损。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。该研究小组将利用一个被称为汇集模型的占主导地位的模型类别，与最近大量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。这项工作可以为那些失去中心视力的老年性黄斑变性患者开发改进的材料和工具。它还可以帮助设计平视显示器、用户界面和信息可视化，如军事决策的态势图。池化模型假定拥挤是由大的局部区域的特征平均引起的，这些区域的大小是固定的，随着距离固定点的距离线性增长。在过去的十年里，PI一直在发展计算和行为方法，使人们能够从最先进的池化模型中得出定量的、可测试的预测。利用这些工具，PI将研究一组明确的拥挤现象，这些现象似乎对池化机制的统一解释提出了挑战。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)利用行为实验消除可能的混杂因素；3)设计新的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关键瓶颈的机制理解，从而更广泛地促进视觉处理。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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.