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Different Measures of Structural Similarity Tap Different Aspects of Visual Object Processing

机译:结构相似性的不同度量可利用视觉对象处理的不同方面

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摘要

The structural similarity of objects has been an important variable in explaining why some objects are easier to categorize at a superordinate level than to individuate, and also why some patients with brain injury have more difficulties in recognizing natural (structurally similar) objects than artifacts (structurally distinct objects). In spite of its merits as an explanatory variable, structural similarity is not a unitary construct, and it has been operationalized in different ways. Furthermore, even though measures of structural similarity have been successful in explaining task and category-effects, this has been based more on implication than on direct empirical demonstrations. Here, the direct influence of two different measures of structural similarity, contour overlap and within-item structural diversity, on object individuation (object decision) and superordinate categorization performance is examined. Both measures can account for performance differences across objects, but in different conditions. It is argued that this reflects differences between the measures in whether they tap: (i) global or local shape characteristics, and (ii) between- or within-category structural similarity.
机译:对象的结构相似性是一个重要变量,它解释了为什么某些对象比上级更容易分类而不是个体化,以及为什么某些脑损伤患者在识别自然(结构相似)对象方面比人工制品(结构上更多)困难。不同的对象)。尽管结构相似性有其优点,但它并不是一个统一的结构,并且已经以不同的方式进行了操作。此外,即使结构相似性的度量已经成功地解释了任务和类别效果,但这更多地是基于含义而不是直接的经验证明。在此,研究了两种不同的结构相似性度量标准(轮廓重叠和项内结构多样性)对对象个体化(对象决策)和上级分类性能的直接影响。两种方法都可以说明对象之间的性能差异,但条件不同。有人认为,这反映了这些措施在利用方面的差异:(i)整体或局部形状特征,以及(ii)类别间或类别内结构相似性。

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