| Project/Area Number |
13J10032
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 国内 |
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| Research Field |
Cognitive science
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| Research Institution | Nagoya University |
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Principal Investigator |
アーノード ソービフルヒャ (2014) 名古屋大学, 情報科学研究科, 特別研究員(PD)
アーノード リービフルヒャ (2013) 名古屋大学, 情報科学研究科, 特別研究員(DC2)
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| Project Period (FY) |
2013-04-01 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 2014: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2013: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | mental representation / artificial intelligence / cognitive evolution / neural network / machine learning / second order learning / meta-learning / social cognition / 心的表象 / 知能の進化 / 機械学習 / ニューラル・ネットワーク / 二次学習 / 進化と学習の相互作用 |
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| Outline of Annual Research Achievements |
In this research we investigated a fundamental question about the origins of cognition: How did representational cognition evolve? The project consisted of theoretical work (formulation and refinement of the central hypothesis) and computational verification (w.r.t. spatial cognition and social cognition). At the heart of this research project is a hypothesis that representational cognition evolves under selection for 2nd order learning ability (i.e. the ability to "learn to learn"). Applied to the topic of social cognition, this implies that evolution social abilities that qualify as 2nd order learning will produce forms of social cognition that operate by forming representations of other individuals’ minds ("Theory of Mind"). We investigated this idea by letting a simple form of social cognition evolve in a population of AI systems (neural networks).
Results of computational work: 1) We showed that in simulation, evolution under selection for 2nd order learning ability leads to a representational form of social cognition, whereas evolution under selection for 1st order learning ability does not. This result supports the hypothesis that representational cognition is a product of evolutionary selection for second order learning ability. 2) In doing so, we showed how representational cognition can be evolved in neural networks. This technique could prove applicable in e.g. social robotics.
Results of theoretical work: A theoretical paper detailing the theory (illustrated with our computational work) was completed and published in Minds and Machines journal.
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| Research Progress Status |
26年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
26年度が最終年度であるため、記入しない。
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Report
(2 results)
Research Products
(6 results)
