Categorization influences detection: A perceptual advantage for representative exemplars of natural scene categories.
Categorization influences detection: A perceptual advantage for representative exemplars of natural scene categories.
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DOI：
10.1167/17.1.21
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发表时间：
2017-01-01
影响因子：
1.8
通讯作者：
Beck, Diane M
中科院分区：
文献类型：
--
作者：
Caddigan, Eamon;Choo, Heeyoung;Beck, Diane M
传统的识别和分类模型是从注册低级特征开始的，感知地组织输入，并将其与存储的表示相关联。然而，最近的证据表明，这一系列模型可能并不准确，物体和类别知识影响而不是跟随早期的视觉加工。在这里，我们展示了图像在多大程度上体现了它的类别，影响它被检测的容易程度。参与者进行了两种选择的强迫选择任务，在这项任务中，他们指出一张短暂呈现的图像是完整的还是被相位扰乱的场景照片。关键的是，场景的类别与检测任务无关。尽管如此，我们还是发现，与糟糕的场景相比，参与者能更好地看到好的图像，更准确地区分它们与相位混乱的图像，而且无论参与者是否被要求在实验期间考虑类别，这一优势都是显而易见的。然后，我们证明了好的样本比坏的样本更类似于同一类别的图像，这在两个方面影响了行为：第一，原型图像更容易检测，第二，完整的好场景更有可能是由先前的试验启动的。
Traditional models of recognition and categorization proceed from registering low-level features, perceptually organizing that input, and linking it with stored representations. Recent evidence, however, suggests that this serial model may not be accurate, with object and category knowledge affecting rather than following early visual processing. Here, we show that the degree to which an image exemplifies its category influences how easily it is detected. Participants performed a two-alternative forced-choice task in which they indicated whether a briefly presented image was an intact or phase-scrambled scene photograph. Critically, the category of the scene is irrelevant to the detection task. We nonetheless found that participants "see" good images better, more accurately discriminating them from phase-scrambled images than bad scenes, and this advantage is apparent regardless of whether participants are asked to consider category during the experiment or not. We then demonstrate that good exemplars are more similar to same-category images than bad exemplars, influencing behavior in two ways: First, prototypical images are easier to detect, and second, intact good scenes are more likely than bad to have been primed by a previous trial.