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A comparative study on the formation of prototype categories

原型范畴形成的比较研究

基本信息

批准号：
16530465
负责人：
JITSUMORI Masako
金额：
$ 2.3万
依托单位：
Chiba University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2004
资助国家：
日本
起止时间：
2004 至 2006
项目状态：
已结题

项目摘要

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.

我们模仿自然范畴的性质，用有色矩形作为构成范畴的特征，建立了人工范畴。一种是家族相似性关系；高度可变的样本由一个相似性网络构成，每个类别中的特征相互关联。另一种是多态规则；没有单一的特征是区分类别的关键。人类在学习类别方面表现出困难，然后根据他们获得的规则，以要么全有要么全不的方式对新的刺激做出反应。鸽子很容易地学会了类别，然后根据家族相似程度显示出原型效应。由于被定义为类别原型的刺激包含了在给定类别中出现频率最高的特征，所以原型上的最佳区分可以通过特征学习和样本学习来解释。在接下来的实验中，我们训练鸽子区分变形后的人脸图像。每个类别中的训练样本是通过将单个唯一人脸M与每个原始人脸(A、B、C和D)及其50%变形混合而创建的。为了检验样本学习的效果，我们对鸽子进行了额外的中间变形测试，这些中间变形在面孔变形维度上介于M和每个原始面孔之间。鸽子的概括梯度随正向类别M的形态比例增加而减小，与类别特征的漫画效应相似。样本学习和特征学习模型都未能预测到这一发现。本研究中使用的分类任务可能使鸽子能够提取类别成员共有的面部特征，并根据共同成分的程度对新面孔做出反应。