Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge CB2 3EB, United Kingdom.
J Neurosci. 2014 Apr 2;34(14):4766-75. doi: 10.1523/JNEUROSCI.2828-13.2014.
Category-specificity has been demonstrated in the human posterior ventral temporal cortex for a variety of object categories. Although object representations within the ventral visual pathway must be sufficiently rich and complex to support the recognition of individual objects, little is known about how specific objects are represented. Here, we used representational similarity analysis to determine what different kinds of object information are reflected in fMRI activation patterns and uncover the relationship between categorical and object-specific semantic representations. Our results show a gradient of informational specificity along the ventral stream from representations of image-based visual properties in early visual cortex, to categorical representations in the posterior ventral stream. A key finding showed that object-specific semantic information is uniquely represented in the perirhinal cortex, which was also increasingly engaged for objects that are more semantically confusable. These findings suggest a key role for the perirhinal cortex in representing and processing object-specific semantic information that is more critical for highly confusable objects. Our findings extend current distributed models by showing coarse dissociations between objects in posterior ventral cortex, and fine-grained distinctions between objects supported by the anterior medial temporal lobes, including the perirhinal cortex, which serve to integrate complex object information.
在人类后腹侧颞叶皮层中,已经证明了各种物体类别具有类别特异性。尽管腹侧视觉通路中的物体表示必须足够丰富和复杂,以支持对单个物体的识别,但对于特定物体是如何被表示的,人们知之甚少。在这里,我们使用表示相似性分析来确定不同种类的物体信息是如何反映在 fMRI 激活模式中的,并揭示类别和物体特定语义表示之间的关系。我们的结果表明,沿着腹侧流,从早期视觉皮层的基于图像的视觉属性表示,到后腹侧流的类别表示,信息特异性呈现出梯度变化。一个关键的发现表明,物体特定的语义信息在旁海马回中被独特地表示,对于语义上更容易混淆的物体,旁海马回的参与程度也越来越高。这些发现表明,旁海马回在表示和处理物体特定的语义信息方面起着关键作用,这些信息对于高度混淆的物体更为关键。我们的发现通过显示后腹侧皮层中物体的粗略分离,以及前内侧颞叶(包括旁海马回)支持的物体的细粒度区别,扩展了当前的分布式模型,这些区别有助于整合复杂的物体信息。