Line drawings are easy to recognize, although the only information tc, the visual system is the contour itself. Starting from information theory and a theory of decomposition in parts, we investigated whether certain regions of such a contour are perceptually more relevant than others, using a deletion detection paradigm. In this paradigm, high detectability means poor contour integration, and vice verse. Regions of interest were curvature singularities, namely positive maxima (M+), negative minima (m-) and inflection points (I), of smooth, closed contours. In Experiment 1, we performed a first exploration of the detectability of deletions around these three types of curvature singularities. M+ deletions were easier to detect than the deletions around the other two singularities, a result that is explained using a post hoc combination of both mentioned theoretical frameworks. In Experiment 2, we replicated these findings using figure-background reversal, so that the same physical deletions could either be M+ or m-. Again, the M+ deletions were easier to detect than m- deletions. Although both types of singularities involve regions of high curvature changes, they differ in that m- deletions create gaps that concur with spontaneous segmentation.
展开▼
机译:线条图易于识别,尽管唯一的信息tc视觉系统是轮廓本身。从信息理论和零件分解理论开始,我们使用删除检测范例研究了这种轮廓的某些区域是否在感知上比其他区域更相关。在这种范例中,高可检测性意味着不良的轮廓整合,反之亦然。感兴趣的区域是曲率奇点,即平滑闭合轮廓的正最大值(M +),负最小值(m-)和拐点(I)。在实验1中,我们对这三种曲率奇异点周围的缺失的可检测性进行了首次探索。 M +缺失比其他两个奇异点附近的缺失更易于检测,这一结果可以使用上述两个理论框架的事后组合进行解释。在实验2中,我们使用图形背景反转技术复制了这些发现,因此相同的物理删除可能是M +或m-。同样,M +缺失比m-缺失更易于检测。尽管两种类型的奇异点都涉及高曲率变化的区域,但它们的不同之处在于m缺失会产生与自发分割一致的间隙。
展开▼