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An Experimental Study on the Counting-Rule-Learning in a Chimpanzee.

黑猩猩计数规则学习的实验研究。

基本信息

批准号：
63450017
负责人：
MUROFUSHI Kiyoko
金额：
$ 3.71万
依托单位：
RITSUMEIKAN UNIVERSITY (1989)Kyoto University (1988)
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (B)
财政年份：
1988
资助国家：
日本
起止时间：
1988 至 1989
项目状态：
已结题

项目摘要

A female chimpanzee, estimated 12 years old, AI finally learned to count objects presented by the video monitor, after she had experienced lots of matching Arabic numerals to the objects or random patterns of dots. The followings were found.1. The curves of response times as a function of the numbers of dots were very similar in the shape to those obtained in human experiments. It implied that both functions of subitizing and counting could be reflected. It is very interesting, however, that the response time to the largest number was shorter than the second largest number at the first stabe, then became the longest if the large number was added. This change of the response time to the largest number would be correspond to AI's response change from relative numerical judgement to counting.2. These cardinal numbers acquires by AI were correctly used to the heterogeneous pattern consisted of two different size of dots, two different colored objects and two different kinds of objects.3. Long training was necessary, however, to count the number of objects in a subset of the heterogeneous pattern. These results means that AI learned to use Arabian numerals as a label for naming a set of objects at first, the cardinal numbers for counting the number of objects in a set. Such Ai's performance is still limited within some range of stimulus and easily disturbed if the number of objects in a subset of the heterogeneous pattern is required. It may be possible that AI can use the numerals as the abstract symbols to operate logically, after great deal of training.

一只大约12岁的雌性黑猩猩，在经历了大量阿拉伯数字与物体或随机图案的点的匹配后，AI终于学会了计算视频监视器展示的物体的数量。主要研究结果如下：1.响应时间随点数变化的曲线在形状上与人体实验中得到的曲线非常相似。这表明，亚片化功能和计数功能都能得到体现。然而，非常有趣的是，在第一个STABE，对最大数字的响应时间比第二大数字短，如果加上大数字，则变得最长。这种反应时间到最大值的变化与人工智能从相对数字判断到计数的反应变化相对应。人工智能获取的这些基数被正确地用于由两个不同大小的点、两个不同颜色的物体和两种不同类型的物体组成的异质图案。然而，为了计算异质模式的子集中的对象数量，需要进行长时间的训练。这些结果意味着人工智能一开始学会了使用阿拉伯数字作为命名一组对象的标签，即计算集合中对象数量的基数。这种人工智能的性能仍然被限制在某个刺激范围内，并且如果需要异质模式的子集中的对象的数量，则容易受到干扰。经过大量训练，人工智能可能会使用数字作为抽象符号进行逻辑运算。