Typicality in categorization, recognition and identification: Evidence from face recognition
Typicality in categorization, recognition and identification: Evidence from face recognition
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分类、识别和识别的典型性：来自人脸识别的证据
DOI：
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发表时间：
1991
期刊：
影响因子：
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通讯作者：
A. Ferrara
中科院分区：
文献类型：
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作者：
T. Valentine;A. Ferrara
分类和记忆样例模型的支持者声称，识别判断是基于熟悉度计算的，计算方法是将探测器与记忆中所有样例之间的相似性求和。与许多以前看到的样本高度相似的探针应该比更不相似的探针更准确或更快地被识别。在识别相对陌生的和人为的刺激的一些实验中，已经支持了“总和-相似性规则”。然而，来自人脸识别的证据显然与这一规则相矛盾。与典型人脸相比，独特或不寻常的人脸识别更准确。有人提出，如果将使用面孔照片作为刺激的任务被称为识别任务，那么这一矛盾就可以得到解决。然而，如果做出这种解释，样本模型并不是唯一一类可以解释面孔“分类”和“识别”任务中的区分性影响的模型。三个模拟结果表明，并行分布式处理模型也可以解释人脸处理任务的数据。两个模拟基于单层自关联网络。最后的仿真是基于使用后向误差传播的多层网络。
The proponents of exemplar models of categorization and memory have claimed that recognition judgements are based on familiarity computed by summing the similarity between a probe and all exemplars in memory. A probe which is highly similar to many previously seen exemplars should be recognized more accurately or faster than a more dissimilar probe. The ‘summed-similarity rule’ has been supported in a number of experiments on recognition of relatively unfamiliar and artificial stimuli. However, evidence from face recognition clearly contradicts the rule. Distinctive or unusual faces are recognized more accurately than typical faces. It is proposed that this contradiction can be resolved if tasks using photographs of faces as stimuli which have been termed ‘recognition’ tasks are interpreted as ‘identification’ tasks. However, if this interpretation is made, an exemplar model is not the only class of models which can account for the effects of distinctiveness in face ‘classification’ and ‘identification’ tasks. Three simulations are reported which show that parallel distributed processing models can also account for the data from face-processing tasks. Two simulations are based on a single-layer auto-associative network. The final simulation is based on a multi-layer network using backward error propagation.