Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA.
J Vis. 2023 Apr 3;23(4):8. doi: 10.1167/jov.23.4.8.
Representations of visual and semantic information can overlap in human visual cortex, with the same neural populations exhibiting sensitivity to low-level features (orientation, spatial frequency, retinotopic position) and high-level semantic categories (faces, scenes). It has been hypothesized that this relationship between low-level visual and high-level category neural selectivity reflects natural scene statistics, such that neurons in a given category-selective region are tuned for low-level features or spatial positions that are diagnostic of the region's preferred category. To address the generality of this "natural scene statistics" hypothesis, as well as how well it can account for responses to complex naturalistic images across visual cortex, we performed two complementary analyses. First, across a large set of rich natural scene images, we demonstrated reliable associations between low-level (Gabor) features and high-level semantic categories (faces, buildings, animate/inanimate objects, small/large objects, indoor/outdoor scenes), with these relationships varying spatially across the visual field. Second, we used a large-scale functional MRI dataset (the Natural Scenes Dataset) and a voxelwise forward encoding model to estimate the feature and spatial selectivity of neural populations throughout visual cortex. We found that voxels in category-selective visual regions exhibit systematic biases in their feature and spatial selectivity, which are consistent with their hypothesized roles in category processing. We further showed that these low-level tuning biases are not driven by selectivity for categories themselves. Together, our results are consistent with a framework in which low-level feature selectivity contributes to the computation of high-level semantic category information in the brain.
视觉和语义信息的表示可以在人类视觉皮层中重叠，相同的神经群体表现出对低水平特征（方向、空间频率、视网膜位置）和高水平语义类别（面孔、场景）的敏感性。人们假设，这种低水平视觉和高水平类别神经选择性之间的关系反映了自然场景统计，即给定类别选择性区域中的神经元针对区域偏好类别的低水平特征或空间位置进行调整。为了解决这个“自然场景统计”假设的普遍性，以及它在多大程度上可以解释整个视觉皮层对复杂自然图像的反应，我们进行了两项互补分析。首先，在一组丰富的自然场景图像中，我们证明了低水平（Gabor）特征和高水平语义类别（面孔、建筑物、有生命/无生命物体、小/大物体、室内/室外场景）之间存在可靠的关联，这些关系在整个视野中具有空间变化。其次，我们使用了一个大规模的功能磁共振成像数据集（自然场景数据集）和一个体素级别的正向编码模型来估计整个视觉皮层中神经群体的特征和空间选择性。我们发现，类别选择性视觉区域中的体素表现出其特征和空间选择性的系统偏差，这与其在类别处理中的假设作用一致。我们进一步表明，这些低水平的调整偏差不是由对类别的选择性驱动的。总的来说，我们的结果与一个框架一致，即低水平特征选择性有助于大脑中高水平语义类别信息的计算。
