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Symmetric Objects Become Special in Perception Because of GenericComputations in Neurons

机译:由于泛型对称对象在感知中变得特别神经元中的计算

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摘要

Symmetry is a salient visual property: It is easy to detect and influences perceptual phenomena from segmentation to recognition. Yet researchers know little about its neural basis. Using recordings from single neurons in monkey IT cortex, we asked whether symmetry—being an emergent property—induces nonlinear interactions between object parts. Remarkably, we found no such deviation: Whole-object responses were always the sum of responses to the object’s parts, regardless of symmetry. The only defining characteristic of symmetric objects was that they were more distinctive compared with asymmetric objects. This was a consequence of neurons preferring the same part across locations within an object. Just as mixing diverse paints produces a homogeneous overall color, adding heterogeneous parts within an asymmetric object renders it indistinct. In contrast, adding identical parts within a symmetric object renders it distinct. This distinctiveness systematically predicted human symmetry judgments, and it explains many previous observations about symmetry perception. Thus, symmetry becomes special in perception despite being driven by generic computations at the level of single neurons.
机译:对称性是一种显着的视觉特性:它易于检测并影响从分割到识别的感知现象。但是研究人员对其神经基础知之甚少。使用猴子IT皮质中单个神经元的记录,我们问对称性(作为一种新兴属性)是否会引起对象部分之间的非线性相互作用。值得注意的是,我们没有发现这种偏差:整个对象的响应始终是对对象各个部分的响应之和,而不考虑对称性。对称对象的唯一定义特征是,与非对称对象相比,它们更具特色。这是神经元偏爱对象内各个位置的相同部分的结果。就像混合各种涂料会产生均匀的整体颜色一样,在不对称的对象中添加异质部分也会使其模糊。相反,在对称对象中添加相同的部分会使它与众不同。这种独特性系统地预测了人类的对称性判断,并解释了许多以前关于对称性感知的观察。因此,对称性在感知中变得特别,尽管它受到单个神经元级别的通用计算的驱动。

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