TY - GEN
T1 - Reinforcement Learning, not Supervised Learning, Can Lead to Insight
AU - Nonami, Arata
AU - Fukuda, Haruaki
AU - Sato, Yoshiyuki
AU - Samejima, Kazuyuki
AU - Ueda, Kazuhiro
N1 - Funding Information:
This study was supported by JSPS KAKENHI Grant Number JP16H01725.
Publisher Copyright:
© 2018 Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018. All rights reserved.
PY - 2018
Y1 - 2018
N2 - This study examined the differences among individuals in the performance of insight problem solving. The problem-solving characteristics of an individual seemed to be dependent on what and how they had learned. Thus, we compared the performances of insight problem solving between reinforcement and supervised learners. The results showed that the performances of reinforcement learners were better than those of supervised learners, although the non-insight problem solving performance of both learner types was comparable. This result suggests that insight might be supported by the cognitive mechanisms underlying reinforcement learning. In particular, we speculate that the degree of exploration, by which reinforcement learning is characterized, might have an impact on the performance of insight problem solving.
AB - This study examined the differences among individuals in the performance of insight problem solving. The problem-solving characteristics of an individual seemed to be dependent on what and how they had learned. Thus, we compared the performances of insight problem solving between reinforcement and supervised learners. The results showed that the performances of reinforcement learners were better than those of supervised learners, although the non-insight problem solving performance of both learner types was comparable. This result suggests that insight might be supported by the cognitive mechanisms underlying reinforcement learning. In particular, we speculate that the degree of exploration, by which reinforcement learning is characterized, might have an impact on the performance of insight problem solving.
KW - exploration
KW - insight problem solving
KW - reinforcement learning
KW - supervised learning
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M3 - Conference contribution
AN - SCOPUS:85139546924
T3 - Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018
SP - 2161
EP - 2166
BT - Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018
PB - The Cognitive Science Society
T2 - 40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018
Y2 - 25 July 2018 through 28 July 2018
ER -