Small and manipulable objects (tools) preferentially evoke a network of brain regions relative to other objects, including the lateral occipitotemporal cortex (LOTC), which is assumed to process tool shape information. Given the correlation between various object properties, the exact type of information being represented in the LOTC remains debated. In three fMRI experiments, we examined the effects of multiple levels of shape (whole vs. object parts) and motor-related (grasping; manipulation) information. Combining representational similarity analysis and commonality analysis allowed us to partition the unique and shared effects of correlated dimensions. We found that grasping manner (for pickup), not the overall object shape or manner of manipulation, uniquely explained the LOTC neural activity pattern (Experiments 1 and 2). Experiment 3 tested tools composed of two parts to understand better how grasping manner was computed from object visual inputs. Support vector machine analysis revealed that the LOTC activity could decode different shapes of the tools' handle parts but not the tools' head parts. Together, these results suggest that the LOTC parses tool shapes by how it maps onto grasping programs; such parsing is not fully based on the whole-object shape but rather an interaction between the whole (where to grasp) and its parts (distinguishing the shape for the grasping part for specific grasping manners).

中文翻译：

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左侧枕颞叶皮层中的对象解析：整体形状，部分形状和可抓握性。

小型且可操作的对象（工具）相对于其他对象（包括横向枕颞皮层（LOTC））优先唤起大脑区域的网络，假定该对象处理工具形状信息。考虑到各种对象属性之间的相关性，在LOTC中表示的信息的确切类型仍有争议。在三个功能磁共振成像实验中，我们检查了多个级别的形状（整体与对象部分）和运动相关（抓取；操纵）信息的影响。结合代表性相似性分析和共性分析，使我们能够划分相关维的唯一和共享效果。我们发现，抓握方式（用于拾取）不是整体物体的形状或操纵方式，唯一地解释了LOTC神经活动模式（实验1和2）。实验3测试了由两部分组成的工具，以更好地了解如何根据对象的视觉输入来计算抓握方式。支持向量机分析显示，LOTC活动可以解码工具手柄部分的不同形状，但不能解码工具头部的形状。总之，这些结果表明，LOTC通过映射到抓紧程序的方式来分析工具形状。这样的解析不是完全基于整个对象的形状，而是整个（要在哪里抓取）及其各部分之间的相互作用（根据特定的抓握方式区分抓握部分的形状）。处理零件，但不处理工具的头部。总之，这些结果表明，LOTC通过映射到抓紧程序的方式来分析工具形状。这样的解析不是完全基于整个对象的形状，而是整个（要在哪里抓取）及其各部分之间的相互作用（根据特定的抓握方式区分抓握部分的形状）。处理零件，但不处理工具的头部。总之，这些结果表明，LOTC通过映射到抓紧程序的方式来分析工具形状。这样的解析不是完全基于整个对象的形状，而是整个（要在哪里抓取）及其各部分之间的相互作用（根据特定的抓握方式区分抓握部分的形状）。