Deconstructing Visual Scenes in Cortex: Gradients of Object and Spatial Layout Information
Deconstructing Visual Scenes in Cortex: Gradients of Object and Spatial Layout Information
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DOI：
10.1093/cercor/bhs091
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发表时间：
2013-04-01
期刊：
影响因子：
3.7
通讯作者：
Baker, Chris I.
中科院分区：
文献类型：
--
作者：
Harel, Assaf;Kravitz, Dwight J.;Baker, Chris I.
真实世界的视觉场景是复杂的、杂乱的、不同种类的刺激，涉及场景和对象的选择性皮质区域，包括海马旁位置区(PPA)、脾后复合体(RSC)和枕侧复合体(LOC)。为了了解每个区域对分布式场景表示的独特贡献，我们基于改编自Money的神经解剖学框架生成预测，并使用最小场景进行测试，在这些场景中，我们独立地处理空间布局(开放、闭合和渐变)和对象内容(家具，例如床、梳妆台)。由于RSC与后顶叶皮质有很强的连通性，RSC有很强的空间布局信息，但没有物体信息，其反应甚至不受物体存在的调制。相比之下，位于腹侧视觉通路的LOC含有强烈的物体信息，但没有背景信息。最后，与背侧和腹侧视觉通路相连的PPA显示了物体和空间背景的信息，对两者中的任何一个的存在或不存在都很敏感。这些结果表明，1)LOC、PPA和RSC有不同的表征，强调场景的不同方面；2)每个区域的具体表征可以从它们的连通性模式中预测；3)PPA结合了通过连通性预测的空间布局和对象信息。
Real-world visual scenes are complex cluttered, and heterogeneous stimuli engaging scene- and object-selective cortical regions including parahippocampal place area (PPA), retrosplenial complex (RSC), and lateral occipital complex (LOC). To understand the unique contribution of each region to distributed scene representations, we generated predictions based on a neuroanatomical framework adapted from monkey and tested them using minimal scenes in which we independently manipulated both spatial layout (open, closed, and gradient) and object content (furniture, e.g., bed, dresser). Commensurate with its strong connectivity with posterior parietal cortex, RSC evidenced strong spatial layout information but no object information, and its response was not even modulated by object presence. In contrast, LOC, which lies within the ventral visual pathway, contained strong object information but no background information. Finally, PPA, which is connected with both the dorsal and the ventral visual pathway, showed information about both objects and spatial backgrounds and was sensitive to the presence or absence of either. These results suggest that 1) LOC, PPA, and RSC have distinct representations, emphasizing different aspects of scenes, 2) the specific representations in each region are predictable from their patterns of connectivity, and 3) PPA combines both spatial layout and object information as predicted by connectivity.