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Linear Discrimination Analysis of Monkey Behavior in an Alternative Free Choice Task
Author(s) -
Kazuhito Takenaka,
Yasuo Nagasaka,
Sayaka Hihara,
Hiroyuki Nakahara,
Atsushi Iriki,
Yasuo Kuniyoshi,
Naotaka Fujii
Publication year - 2007
Publication title -
journal of robotics and mechatronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 19
eISSN - 1883-8049
pISSN - 0915-3942
DOI - 10.20965/jrm.2007.p0416
Subject(s) - linear discriminant analysis , nonverbal communication , context (archaeology) , interpersonal communication , task (project management) , discriminant function analysis , discriminant , cognitive psychology , computer science , function (biology) , psychology , point (geometry) , artificial intelligence , process (computing) , social psychology , machine learning , communication , mathematics , biology , paleontology , geometry , management , evolutionary biology , economics , operating system
When we observe people, we can often comprehend their intention from their behaviors. The intentions expressed by individuals can be considered as existing in interpersonal space and from a current social context. In our daily activity, choosing socially correct behavior through the observation of such social context is essential. However, it is not known how we can decode intention from another’s behavior. Here, we show how we can retrieve the intention of monkeys through external observation of their behavior patterns while performing alternative free choice tasks. We found that linear discriminant analysis on a monkey’s motion parameters could provide a discriminant score that appears to reflect the internal decision making process. The score showed a clear flexion point that we defined as a moment of outward expression of intention (OEI). This suggests that an alternative decision is made just before an OEI and that intention is expressed in the environment after this OEI in behavior, which in turn suggests that discriminant analysis may be useful in indicating how the brain implements nonverbal social communication. If we could embed the function in a human-machine interfaces, it could enable intuitive, smooth communication between machines and humans.

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