A comparative study on the formation of prototype categories

我们通过模仿自然类别的属性，使用彩色矩形作为组成类别的特征来创建人工类别。一种是家族相似性关系，高度可变的样本由相似性网络构成，每个类别中的特征相互关联。另一个是多态性规则;没有单一的特征是区分类别所必需的。人类在学习这些类别时表现出困难，然后根据他们获得的规则以全有或全无的方式对新的刺激做出反应。皮格尔顿较容易地掌握了这些类别，并根据家族相似性的程度表现出了雅阁效应。由于被定义为类别原型的刺激包含了给定类别中最频繁出现的特征，因此原型的最佳区分很容易通过特征学习和样例学习来解释。每个类别中的训练样本是通过将单个独特的面部M与每个原始面部（A、B、C和D）及其50%变形混合来创建的。为了检验样本学习的效果，我们测试了鸽子与额外的中间变形，范围在M和每个原始面孔之间的面孔变形维度。鸽子的泛化梯度随着正类M的形态比例的增加而增加，随着负类M的形态比例的增加而减少，这一现象类似于类别特征的漫画效应。范例学习和特征学习模型都未能预测这一发现。在这项研究中使用的分类任务可能使鸽子抽象的类别成员共享的面部特征，并根据程度的共同组成部分的新面孔作出反应。

We created artificial categories by mimicking properties of natural category, using colored rectangles as the features comprising the categories. One is family resemblance relationship ; the highly variable exemplars were structured by a similarity network with the features correlating to one another in each category. Another is polymorphous rule ; no single feature was essential for distinguishing the categories. Humans showed difficulty in learning the categories and then responded to novel stimuli in an all-or-none fashion based on the rules they acquired. Pigeons learned the categories with ease and then showed a prototype effect in accord with the degrees of family resemblance. Because the stimuli defined as the prototypes of the categories contained the features that most frequently occurred in the given category, the best discrimination at the prototypes was readily explained by feature learning and exemplar learning.In the next experiment, we trained pigeons to discriminate morphed images of human faces. The training exemplars in each category were created by blending a single unique face M with each of the original faces (A, B, C, and D) and their 50% morphs. To examine the effects of exemplar learning, we tested the pigeons with additional intermediate morphs that ranged between M and each of the original faces on a face-morph dimension. The pigeons' generalization gradient increased as a function of morph proportion of M of the positive category and decreased as a function of morph proportion of M of the negative category, a phenomena similar to the caricature effect of category features. Both the exemplar learning and feature learning models failed to predict the finding. The classification task used in this study might have enabled the pigeons to abstract the facial features shared by the category members and to respond to novel faces based on the degrees of common component.