Shape Similarity, Better than Semantic Membership, Accounts for the Structure of Visual Object Representations in a Population of Monkey Inferotemporal Neurons

The anterior inferotemporal cortex (IT) is the highest stage along the hierarchy of visual areas that, in primates, processes visual objects. Although several lines of evidence suggest that IT primarily represents visual shape information, some recent studies have argued that neuronal ensembles in IT code the semantic membership of visual objects (i.e., represent conceptual classes such as animate and inanimate objects). In this study, we investigated to what extent semantic, rather than purely visual information, is represented in IT by performing a multivariate analysis of IT responses to a set of visual objects. By relying on a variety of machine-learning approaches (including a cutting-edge clustering algorithm that has been recently developed in the domain of statistical physics), we found that, in most instances, IT representation of visual objects is accounted for by their similarity at the level of shape or, more surprisingly, low-level visual properties. Only in a few cases we observed IT representations of semantic classes that were not explainable by the visual similarity of their members. Overall, these findings reassert the primary function of IT as a conveyor of explicit visual shape information, and reveal that low-level visual properties are represented in IT to a greater extent than previously appreciated. In addition, our work demonstrates how combining a variety of state-of-the-art multivariate approaches, and carefully estimating the contribution of shape similarity to the representation of object categories, can substantially advance our understanding of neuronal coding of visual objects in cortex.颞下回前部（anterior inferotemporal cortex, IT）是灵长类视觉皮层区域层级中处理视觉物体的最高阶段。尽管多项证据表明IT主要表征视觉形状信息，但近期部分研究提出，IT内的神经元集群可编码视觉物体的语义归属，即表征有生命与无生命物体等概念类别。本研究通过对一组视觉物体的IT响应开展多变量分析，探究了IT中语义信息而非纯视觉信息的表征程度。本研究依托多种机器学习方法，包括近期在统计物理领域开发的前沿聚类算法，发现多数情况下，IT对视觉物体的表征可通过物体在形状层面的相似性，或是更出人意料的低层次视觉属性相似性来解释。仅在少数案例中，我们观察到IT对语义类别的表征无法通过其成员的视觉相似性加以说明。总体而言，这些发现再次确认了IT作为显性视觉形状信息传递载体的核心功能，并揭示出IT对低层次视觉属性的表征程度远超此前认知。此外，本研究证明，结合多种当前最优的多变量分析方法，并精准估算形状相似性对物体类别表征的贡献，可大幅提升我们对皮层内视觉物体神经元编码机制的理解。