Constant curvature segments as building blocks of 2D shape representation.
Constant curvature segments as building blocks of 2D shape representation.
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DOI：
10.1037/xge0001007
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发表时间：
2021-08
期刊：
影响因子：
--
通讯作者：
Kellman PJ
中科院分区：
文献类型：
--
作者：
Baker N;Garrigan P;Kellman PJ
视觉系统如何表示形状，以及形状表征如何通过神经机制计算，是基本的和未解决的问题。在这里，我们研究了二维轮廓形状被结构编码的假设，作为一组连接的常曲率段。我们报告了三个实验，研究恒定曲率段作为人类感知中轮廓形状表征的基本单位。我们的研究结果表明，与缺乏这种全局规律性的局部匹配目标相比，恒定曲率目标的路径检测范式具有更好的性能（实验1），并且参与者可以学习将轮廓分割成具有不同曲率值的恒定曲率部分，但不能分割成具有线性增加曲率的相似不同部分（实验2）。我们提出了一个基于恒定曲率的神经上可行的轮廓形状表示模型，该模型基于已知存在于早期皮层区域的定向单元，并且我们证实了该模型的预测，即片段的角度范围的变化比相对曲率的变化更容易检测（实验3）。总之，这些发现表明人类视觉系统特别适合于检测和编码恒定曲率区域，并支持恒定曲率片段是抽象轮廓形状表征组成的构建块的概念。
How the visual system represents shape, and how shape representations might be computed by neural mechanisms, are fundamental and unanswered questions. Here, we investigated the hypothesis that 2D contour shapes are encoded structurally, as sets of connected constant curvature segments. We report three experiments investigating constant curvature segments as fundamental units of contour shape representations in human perception. Our results showed better performance in a path detection paradigm for constant curvature targets, as compared to locally-matched targets that lacked this global regularity (Experiment 1), and that participants can learn to segment contours into constant curvature parts with different curvature values, but not into similarly different parts with linearly increasing curvatures (Experiment 2). We propose a neurally plausible model of contour shape representation based on constant curvature, built from oriented units known to exist in early cortical areas, and we confirmed the model’s prediction that changes to the angular extent of a segment will be easier to detect than changes to relative curvature (Experiment 3). Together, these findings suggest the human visual system is specially adapted to detect and encode regions of constant curvature and support the notion that constant curvature segments are the building blocks from which abstract contour shape representations are composed.
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