The prototype effect revisited: Evidence for an abstract feature model of face recognition.
The prototype effect revisited: Evidence for an abstract feature model of face recognition.
复制标题
重新审视原型效应：人脸识别抽象特征模型的证据。
作者：
G. Wallis;U. Siebeck;K. Swann;V. Blanz;H. Bülthoff
人类通常对面孔有非凡的记忆力。在某些情况下，他们可能会被愚弄。本文中描述的实验为观察者错误地“识别”他们从未见过的面孔的效果提供了新的证据。脸是一个嵌合体（原型），从以前看到的脸提取的部分建立。众所周知，这类面孔可能会与真正熟悉的面孔混淆，这一结果被称为原型效应。然而，最近的研究未能找到充分的证据，其中原型不仅被认为是熟悉的，而且比以前见过的面孔更熟悉。这项研究试图重新调查这种影响。在一对实验中，证据报告的全面效果的基础上，一个老/新的歧视任务和熟悉度排名任务。结果表明是一致的识别模型，其中的面孔表示为可重用的，抽象的功能的组合。在最后的实验中，新的预测模型进行了验证，通过比较直立和颠倒的脸的原型效果的大小。尽管从根本上分段性质的模型，提供了一个解释，它如何也可以占观察员的敏感性，以结构和整体的线索。这一讨论是支持使用无监督网络模型。总体而言，该论文描述了基于特征的抽象模型如何协调人脸识别文献中的一系列结果，从而减少当前感知到的人脸和其他物体表示之间的差异。
Humans typically have a remarkable memory for faces. Nonetheless, in some cases they can be fooled. Experiments described in this paper provide new evidence for an effect in which observers falsely "recognize" a face that they have never seen before. The face is a chimera (prototype) built from parts extracted from previously viewed faces. It is known that faces of this kind can be confused with truly familiar faces, a result referred to as the prototype effect. However, recent studies have failed to find evidence for a full effect, one in which the prototype is regarded not only as familiar, but as more familiar than faces which have been seen before. This study sought to reinvestigate the effect. In a pair of experiments, evidence is reported for the full effect based on both an old/new discrimination task and a familiarity ranking task. The results are shown to be consistent with a recognition model in which faces are represented as combinations of reusable, abstract features. In a final experiment, novel predictions of the model are verified by comparing the size of the prototype effect for upright and upside-down faces. Despite the fundamentally piecewise nature of the model, an explanation is provided as to how it can also account for the sensitivity of observers to configural and holistic cues. This discussion is backed up with the use of an unsupervised network model. Overall, the paper describes how an abstract feature-based model can reconcile a range of results in the face recognition literature and, in turn, lessen currently perceived differences between the representation of faces and other objects.
影响因子：
5.4
作者：
Farah, MJ;Wilson, KD;Tanaka, JN
通讯作者：
Tanaka, JN