Searching for Category-Consistent Features
提出一个生成模型，用计算机视觉方法从物体图像中提取类别一致特征，并在68个类别上训练，发现下属级线索引导注视更快，但基本级线索验证更快，解释了类别层次对视觉搜索的影响。
This article introduces a generative model of category representation that uses computer vision methods to extract category-consistent features (CCFs) directly from images of category exemplars. The model was trained on 4,800 images of common objects, and CCFs were obtained for 68 categories spanning subordinate, basic, and superordinate levels in a category hierarchy. When participants searched for these same categories, targets cued at the subordinate level were preferentially fixated, but fixated targets were verified faster when they followed a basic-level cue. The subordinate-level advantage in guidance is explained by the number of target-category CCFs, a measure of category specificity that decreases with movement up the category hierarchy. The basic-level advantage in verification is explained by multiplying the number of CCFs by sibling distance, a measure of category distinctiveness. With this model, the visual representations of real-world object categories, each learned from the vast numbers of image exemplars accumulated throughout everyday experience, can finally be studied.