| Project/Area Number |
63450017
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| Research Category |
Grant-in-Aid for General Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Psychology
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| Research Institution | RITSUMEIKAN UNIVERSITY (1989)
Kyoto University (1988) |
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Principal Investigator |
MUROFUSHI Kiyoko RITSUMEIKAN UNIVERSITY, FACULTY OF LETTERS, PROFESSOR, 文学部, 教授 (80027482)
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| Co-Investigator(Kenkyū-buntansha) |
FUJITA Kazuo KYOTO UNIVERSITY, PRIMATE RES. INST., ASSISTANT, 霊長類研究所, 助手 (80183101)
MATSUZAWA Tetsurou KYOTO UNIVERSITY, PRIMATE RES. INST., ASSOCIATE PROFESSOR, 霊長類研究所, 助教授 (60111986)
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| Project Period (FY) |
1988 – 1989
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| Project Status |
Completed (Fiscal Year 1989)
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| Budget Amount *help |
¥5,800,000 (Direct Cost: ¥5,800,000)
Fiscal Year 1989: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1988: ¥5,000,000 (Direct Cost: ¥5,000,000)
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| Keywords | Chimpanzee / Conditional symbolic matching-to-sample / Counting-rule-learning / Number Concept / Addition & Subtraction of Objects / チンパンジー / 数 / 条件性シンボリック・マッチング |
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| Research Abstract |
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.
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