Presentation 2014-11-22
A Bayesian Network Model of theory-of-mind
Naoya NISHIO, Nobuhiko ASAKURA, Toshio INUI,
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Abstract(in English) Theory of mind (ToM) is a knowledge and cognitive framework for understanding the minds of others and predicting their actions. There are two models for ToM: theory theory and simulation theory. In theory theory, people understand the minds of others based on the knowledge of others' minds. In simulation theory, people understand the minds of others by projecting themselves into others' situations. Although most models in preceding studies were based on simulation theory, some of them were based on theory theory. These preceding models use either theory. However, in this paper we argue that people use both for understanding others' minds. Therefore, the purpose of this study is to construct a Bayesian network model based on both theories, and to discuss a developmental process of the human understanding and the interrelationships of various mental states such as beliefs, knowledge and false beliefs. The results suggest that the simultaneous development of mental functions of understanding the beliefs and knowledge of others is necessary for people to acquire more highly advanced mental functions of false beliefs.
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Keyword(in English) theory of mind / simulation theory / Bayesian network
Paper # NC2014-39
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Committee NC
Conference Date 2014/11/14(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Bayesian Network Model of theory-of-mind
Sub Title (in English)
Keyword(1) theory of mind
Keyword(2) simulation theory
Keyword(3) Bayesian network
1st Author's Name Naoya NISHIO
1st Author's Affiliation Graduate School of Informatics, Kyoto University()
2nd Author's Name Nobuhiko ASAKURA
2nd Author's Affiliation Graduate School of Informatics, Kyoto University
3rd Author's Name Toshio INUI
3rd Author's Affiliation Graduate School of Informatics, Kyoto University
Date 2014-11-22
Paper # NC2014-39
Volume (vol) vol.114
Number (no) 326
Page pp.pp.-
#Pages 6
Date of Issue