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Subordinate-level categorization relies on high spatial frequencies to a greater degree than basic-level categorization

机译:下属级别的分类比基本级别的分类在更大程度上依赖于高空间频率

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摘要

In two experiments, category verification of images of common objects at subordinate, basic, and superordinate levels was performed after low-pass spatial filtering, high-pass spatial filtering, 50% phase randomization, or no image manipulation. Both experiments demonstrated the same pattern of results: Low-pass filtering selectively impaired subordinate-level category verification, while having little to no effect on basic-level category verification. Subordinate categorization consequently relies to a greater degree on high spatial frequencies of images. This vulnerability of subordinate-level processing was specific to a lack of high spatial frequency information, as opposed to other visual information, since neither high-pass filtering nor the addition of phase noise produced a comparable reduction in performance. These results are consistent with the notion that object recognition at basic levels relies on the general shapes of objects, whereas recognition at subordinate levels relies on finer visual details.
机译:在两个实验中,在低通空间滤波,高通空间滤波,50%相位随机化或未进行图像处理之后,对从属,基本和上级的常见对象的图像进行了类别验证。这两个实验都证明了相同的结果模式:低通滤波选择性地损害了下级类别的验证,而对基本级类别的验证几乎没有影响。因此,从属分类在很大程度上取决于图像的高空间频率。与其他视觉信息相反,下级处理的这种弱点特定于缺少高空间频率信息,因为高通滤波和相位噪声的添加均不会导致性能的可比降低。这些结果与以下概念一致:基本级别的对象识别依赖于对象的一般形状,而下级级别的识别则依赖于更精细的视觉细节。

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