Department of Biological Structure, University of Washington, Seattle, WA, United States; Washington National Primate Research Center, University of Washington, Seattle, WA, United States.
Department of Biological Structure, University of Washington, Seattle, WA, United States; Washington National Primate Research Center, University of Washington, Seattle, WA, United States.
Curr Opin Neurobiol. 2019 Oct;58:199-208. doi: 10.1016/j.conb.2019.09.009. Epub 2019 Oct 4.
Recognizing a myriad visual objects rapidly is a hallmark of the primate visual system. Traditional theories of object recognition have focused on how crucial form features, for example, the orientation of edges, may be extracted in early visual cortex and utilized to recognize objects. An alternative view argues that much of early and mid-level visual processing focuses on encoding surface characteristics, for example, texture. Neurophysiological evidence from primate area V4 supports a third alternative - the joint, but independent, encoding of form and texture - that would be advantageous for segmenting objects from the background in natural scenes and for object recognition that is independent of surface texture. Future studies that leverage deep convolutional network models, especially focusing on network failures to match biology and behavior, can advance our insights into how such a joint representation of form and surface properties might emerge in visual cortex.
快速识别无数视觉对象是灵长类视觉系统的一个特点。传统的物体识别理论主要集中在早期视觉皮层中如何提取关键的形状特征，例如边缘的方向，并利用这些特征来识别物体。另一种观点认为，早期和中期的视觉处理主要集中在编码表面特征上，例如纹理。来自灵长类动物 V4 区的神经生理学证据支持第三种观点，即形状和纹理的联合但独立编码，这对于从自然场景中的背景中分割物体和进行独立于表面纹理的物体识别是有利的。未来利用深度卷积网络模型进行的研究，特别是专注于网络失败以匹配生物学和行为的研究，可以增进我们对这种形状和表面属性联合表示如何在视觉皮层中出现的理解。
