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Scene-Space Encoding within the Functional Scene-Selective Network

机译:功能场景选择网络中的场景空间编码

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摘要

High-level visual neuroscience has often focused on how different visual categories are encoded in the brain. For example, we know how the brain responds when viewing scenes as compared to faces or other objects a?? three regions are consistently engaged: the parahippocampal/lingual region (PPA), the retrosplenial complex (RSC), and the occipital place area/transverse occipital sulcus (TOS). Here we explore the fine-grained responses of these three regions when viewing 100 different scenes. We asked: 1) Can neural signals differentiate the 100 exemplars? 2) Are the PPA, RSC, and TOS strongly activated by the same exemplars and, more generally, are the a??scene-spacesa?? representing how scenes are encoded in these regions similar? In an fMRI study of 100 scenes we found that the scenes eliciting the greatest BOLD signal were largely the same across the PPA, RSC, and TOS. Remarkably, the orderings, from strongest to weakest, of scenes were highly correlated across all three regions (r = .82), but were only moderately correlated with non-scene selective brain regions (r = .30). The high similarity across scene-selective regions suggests that a reliable and distinguishable feature space encodes visual scenes. To better understand the potential feature space, we compared the neural scene-space to scene-spaces defined by either several different computer vision models or behavioral measures of scene similarity. Computer vision models that rely on more complex, mid- to high-level visual features best accounted for the pattern of BOLD signal in scene-selective regions and, interestingly, the better-performing models exceeded the performance of our behavioral measures. These results suggest a division of labor where the representations within the PPA and TOS focus on visual statistical regularities within scenes, whereas the representations within the RSC focus on a more high-level representation of scene category. Moreover, the data suggest the PPA mediates between the processing of the TOS and RSC.
机译:高级视觉神经科学通常关注于大脑中不同视觉类别的编码方式。例如,我们知道与面部或其他物体相比,大脑在观看场景时的反应如何?三个区域始终保持一致:海马旁/舌侧区域(PPA),脾后复合体(RSC)和枕后区域/枕后沟(TOS)。在这里,我们在查看100个不同场景时探索了这三个区域的细粒度响应。我们问:1)神经信号可以区分100个样本吗? 2)PPA,RSC和TOS是否被相同的示例强烈激活,更普遍的是,“场景空间”是?代表这些区域中的场景编码方式相似吗?在一项对100个场景的fMRI研究中,我们发现引发最大BOLD信号的场景在PPA,RSC和TOS上基本相同。值得注意的是,场景的顺序(从最强到最弱)在所有三个区域中高度相关(r = .82),但与非场景选择性脑区域仅中等相关(r = .30)。场景选择区域之间的高度相似性表明,可靠且可区分的特征空间可对视觉场景进行编码。为了更好地理解潜在的特征空间,我们将神经场景空间与由几种不同的计算机视觉模型或场景相似性的行为度量所定义的场景空间进行了比较。依赖更复杂的中高级视觉功能的计算机视觉模型最能说明场景选择区域中BOLD信号的模式,有趣的是,性能更好的模型超出了我们的行为指标。这些结果表明分工,其中PPA和TOS中的表示集中于场景内的视觉统计规律,而RSC中的表示集中于场景类别的更高级表示。而且,数据表明PPA在TOS和RSC的处理之间进行调节。

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