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人类视觉皮层中的目标表示是通过动态部分形状视图的时间整合形成的。

Object Representations in Human Visual Cortex Formed Through Temporal Integration of Dynamic Partial Shape Views.

机构信息

Department of Neurobiology, The Alexander Silberman Institute of Life Sciences, and The Edmond and Lily Safra Center for Brain Sciences, Hebrew University, 91904 Jerusalem, Israel

Department of Neurobiology, The Alexander Silberman Institute of Life Sciences, and The Edmond and Lily Safra Center for Brain Sciences, Hebrew University, 91904 Jerusalem, Israel.

出版信息

J Neurosci. 2018 Jan 17;38(3):659-678. doi: 10.1523/JNEUROSCI.1318-17.2017. Epub 2017 Dec 1.

Abstract

We typically recognize visual objects using the spatial layout of their parts, which are present simultaneously on the retina. Therefore, shape extraction is based on integration of the relevant retinal information over space. The lateral occipital complex (LOC) can represent shape faithfully in such conditions. However, integration over time is sometimes required to determine object shape. To study shape extraction through temporal integration of successive partial shape views, we presented human participants (both men and women) with artificial shapes that moved behind a narrow vertical or horizontal slit. Only a tiny fraction of the shape was visible at any instant at the same retinal location. However, observers perceived a coherent whole shape instead of a jumbled pattern. Using fMRI and multivoxel pattern analysis, we searched for brain regions that encode temporally integrated shape identity. We further required that the representation of shape should be invariant to changes in the slit orientation. We show that slit-invariant shape information is most accurate in the LOC. Importantly, the slit-invariant shape representations matched the conventional whole-shape representations assessed during full-image runs. Moreover, when the same slit-dependent shape slivers were shuffled, thereby preventing their spatiotemporal integration, slit-invariant shape information was reduced dramatically. The slit-invariant representation of the various shapes also mirrored the structure of shape perceptual space as assessed by perceptual similarity judgment tests. Therefore, the LOC is likely to mediate temporal integration of slit-dependent shape views, generating a slit-invariant whole-shape percept. These findings provide strong evidence for a global encoding of shape in the LOC regardless of integration processes required to generate the shape percept. Visual objects are recognized through spatial integration of features available simultaneously on the retina. The lateral occipital complex (LOC) represents shape faithfully in such conditions even if the object is partially occluded. However, shape must sometimes be reconstructed over both space and time. Such is the case in anorthoscopic perception, when an object is moving behind a narrow slit. In this scenario, spatial information is limited at any moment so the whole-shape percept can only be inferred by integration of successive shape views over time. We find that LOC carries shape-specific information recovered using such temporal integration processes. The shape representation is invariant to slit orientation and is similar to that evoked by a fully viewed image. Existing models of object recognition lack such capabilities.

摘要

我们通常通过视网膜上同时存在的部分的空间布局来识别视觉对象。因此,形状提取基于对空间相关视网膜信息的整合。外侧枕叶复合体(LOC)可以在这种条件下忠实地表示形状。然而,有时需要进行时间上的整合来确定物体的形状。为了通过对连续部分形状视图的时间整合来研究形状提取,我们向人类参与者(男性和女性)展示了在狭窄的垂直或水平狭缝后面移动的人工形状。在同一视网膜位置,任何瞬间只能看到形状的一小部分。然而,观察者感知到的是一个连贯的整体形状,而不是一个杂乱无章的图案。使用 fMRI 和多体素模式分析,我们搜索了编码时间整合形状身份的大脑区域。我们还要求形状的表示应该对狭缝方向的变化保持不变。我们发现,在 LOC 中,形状信息的狭缝不变性最准确。重要的是,狭缝不变的形状表示与在全图像运行期间评估的常规整体形状表示匹配。此外,当相同的依赖狭缝的形状薄片被打乱,从而阻止它们的时空整合时,狭缝不变的形状信息会急剧减少。各种形状的狭缝不变表示也反映了通过感知相似性判断测试评估的形状感知空间的结构。因此,LOC 可能介导依赖狭缝的形状视图的时间整合,产生狭缝不变的整体形状感知。这些发现为 LOC 中形状的全局编码提供了强有力的证据,无论生成形状感知所需的整合过程如何。视觉对象是通过同时在视网膜上提供的特征的空间整合来识别的。即使物体部分被遮挡,外侧枕叶复合体(LOC)也能忠实地表示形状。然而,有时需要在空间和时间上重建形状。在正像知觉中就是这种情况,当物体在一个狭窄的狭缝后面移动时。在这种情况下,任何时刻的空间信息都是有限的,因此只能通过随时间对连续形状视图进行整合来推断整个形状感知。我们发现,LOC 携带使用这种时间整合过程恢复的特定于形状的信息。该形状表示对狭缝方向不变,并且与完全查看的图像引起的形状表示相似。现有的物体识别模型缺乏这种能力。

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