Naselaris Thomas, Stansbury Dustin E, Gallant Jack L
Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA.
J Physiol Paris. 2012 Sep-Dec;106(5-6):239-49. doi: 10.1016/j.jphysparis.2012.02.001. Epub 2012 Mar 28.
The representations of animate and inanimate objects appear to be anatomically and functionally dissociated in the primate brain. How much of the variation in object-category tuning across cortical locations can be explained in terms of the animate/inanimate distinction? How is the distinction between animate and inanimate reflected in the arrangement of object representations along the cortical surface? To investigate these issues we recorded BOLD activity in visual cortex while subjects viewed streams of natural scenes. We then constructed an explicit model of object-category tuning for each voxel along the cortical surface. We verified that these models accurately predict responses to novel scenes for voxels located in anterior visual areas, and that they can be used to accurately decode multiple objects simultaneously from novel scenes. Finally, we used principal components analysis to characterize the variation in object-category tuning across voxels. Remarkably, we found that the first principal component reflects the distinction between animate and inanimate objects. This dimension accounts for between 50 and 60% of the total variation in object-category tuning across voxels in anterior visual areas. The importance of the animate-inanimate distinction is further reflected in the arrangement of voxels on the cortical surface: voxels that prefer animate objects tend to be located anterior to retinotopic visual areas and are flanked by voxels that prefer inanimate objects. Our explicit model of object-category tuning thus explains the anatomical and functional dissociation of animate and inanimate objects.
在灵长类动物大脑中,有生命物体和无生命物体的表征在解剖学和功能上似乎是分离的。在不同皮质位置上,物体类别调谐的变化中有多少可以用有生命/无生命的区分来解释?有生命和无生命的区分如何在沿着皮质表面的物体表征排列中得到体现?为了研究这些问题,我们在受试者观看自然场景流时记录了视觉皮质中的血氧水平依赖(BOLD)活动。然后,我们为沿着皮质表面的每个体素构建了一个明确的物体类别调谐模型。我们验证了这些模型能够准确预测位于前视觉区域的体素对新场景的反应,并且它们可用于同时从新场景中准确解码多个物体。最后,我们使用主成分分析来表征跨体素的物体类别调谐变化。值得注意的是,我们发现第一主成分反映了有生命物体和无生命物体之间的区分。这个维度在前视觉区域的体素中占物体类别调谐总变化的50%至60%。有生命与无生命区分的重要性还进一步体现在皮质表面体素的排列上:偏好有生命物体的体素往往位于视网膜拓扑视觉区域之前,并被偏好无生命物体的体素包围。因此,我们明确的物体类别调谐模型解释了有生命物体和无生命物体在解剖学和功能上的分离。