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首页> 外文期刊>Journal of vision >Typicality Sharpens Object Representations in Object-Selective Cortex
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Typicality Sharpens Object Representations in Object-Selective Cortex

机译:典型性提高了对象选择皮质中的对象表示

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The purpose of categorization is to identify generalizable classes of objects whose members can be treated equivalently. Within a category, however, some exemplars are more representative of that concept than other members of the same category (Rosch 1973, Rosch & Mervis 1975). This typicality effect usually manifests as increased speed of recognition, as well as lower error rates for verifying category membership of the more typical item. Despite these behavioral effects, little is known about how typicality influences the neural representation of objects from the same category. To address this question, we performed an fMRI experiment in which participants were shown color photographs from 128 subordinate-level object categories grouped into 16 basic-level categories (4 species of animals, 4 types of plants, 4 transportation modalities, and 4 classes of musical instruments). Typicality for each subordinate within its basic category was assessed behaviorally. We analyzed neural responses in early visual areas and object-, scene-, and face-selective areas: V1, V2, VP, hV4, LOC, TOS, PPA, RSC, FFA. For each brain area, we computed separate similarity matrices (Kriegeskorte et al. 2008) for the most and least prototypical halves of the category set. In V1, V2, and LOC, the most typical exemplars from a basic category had a more similar representation and were more distinct from prototypes of other basic categories (using a category boundary effect measure) than the least typical exemplars. Furthermore, a subsequent analysis showed that in LOC, but not in early visual areas, the most typical exemplars also correlated better than the least typical with the average response elicited by other exemplars. Our results suggest that neural representation differs for typical and less typical object exemplars. More specifically, typicality may be correlated to neural distance between categories in LOC, with highly typical members maximizing dissimilarity to instances of other categories.
机译:分类的目的是确定可以等同对待其成员的对象的可概括类。但是,在一个类别中,某些示例比该类别的其他成员更能代表该概念(Rosch 1973,Rosch&Mervis 1975)。这种典型性效应通常表现为识别速度的提高,以及用于验证较典型项的类别成员资格的错误率较低。尽管有这些行为影响,但关于典型性如何影响同一类别对象的神经表示知之甚少。为了解决这个问题,我们进行了功能磁共振成像实验,向参与者显示了128个下级对象类别的彩色照片,这些类别分为16个基本级别类别(4种动物,4种植物,4种运输方式和4种类别)乐器)。在行为上评估了每个下属在其基本类别中的典型性。我们分析了早期视觉区域以及对象,场景和面部选择区域中的神经反应:V1,V2,VP,hV4,LOC,TOS,PPA,RSC,FFA。对于每个大脑区域,我们针对类别集的最大和最小原型半部分分别计算了相似性矩阵(Kriegeskorte等,2008)。在V1,V2和LOC中,与最不典型的示例相比,基本类别中最典型的示例具有更相似的表示形式,并且与其他基本类别的原型(使用类别边界效应度量)之间的区别更大。此外,随后的分析表明,在LOC中,但不是在早期的视觉区域中,最典型的示例与其他示例所引起的平均响应之间的相关性也比最不典型的示例更好。我们的结果表明,对于典型的和不太典型的对象示例,神经表示形式有所不同。更具体地说,典型性可能与LOC中类别之间的神经距离相关,高度典型的成员将与其他类别实例的相似性最大化。

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