The question of what peripheral vision is good for, especially in pattern recognition, is one of the most important and controversial issues in cognitive science. In a series of experiments, we provide substantial evidence that observers' behavioral performance in the periphery is consistently superior to central vision for topological change detection, while nontopological change detection deteriorates with increasing eccentricity. These experiments generalize the topological account of object perception in the periphery to different kinds of topological changes (i.e., including introduction, disappearance, and change in number of holes) in comparison with a broad spectrum of geometric properties (e.g., luminance, similarity, spatial frequency, perimeter, and shape of the contour). Moreover, when the stimuli were scaled according to cortical magnification factor and the task difficulty was well controlled by adjusting luminance of the background, the advantage of topological change detection in the periphery remained. The observed advantage of topological change detection in the periphery supports the view that the topological definition of objects provides a coherent account for object perception in peripheral vision, allowing pattern recognition with limited acuity.

中文翻译：

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周边视觉的拓扑优势。

周边视觉有什么好处的问题，尤其是在模式识别中，是认知科学中最重要和最具争议的问题之一。在一系列实验中，我们提供了大量证据表明观察者在外围的行为表现始终优于中心视觉的拓扑变化检测，而非拓扑变化检测随着离心率的增加而恶化。这些实验将外围物体感知的拓扑解释概括为与广泛的几何特性（例如亮度、相似性、空间轮廓的频率、周长和形状）。而且，当根据皮层放大倍数缩放刺激，并通过调整背景亮度很好地控制任务难度时，边缘拓扑变化检测的优势仍然存在。观察到的周边拓扑变化检测的优势支持这样一种观点，即物体的拓扑定义为周边视觉中的物体感知提供了一个连贯的解释，允许以有限的敏锐度进行模式识别。