Optimized behavior in a robot model of sequential action
Madison, WI : Cognitive Science Society
InKalish, C.; Rau, M.; Zhu, J. (ed.), Proceedings of the 40th Annual Meeting of the Cognitive Science Society (CogSci 2018), pp. 1615
The 40th Annual Meeting of the Cognitive Science Society (CogSci 2018) (Madison, Wisconsin, USA, 25-28 July 2018)
Article in monograph or in proceedings
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SW OZ DCC KI
Kalish, C.; Rau, M.; Zhu, J. (ed.), Proceedings of the 40th Annual Meeting of the Cognitive Science Society (CogSci 2018)
SubjectCognitive artificial intelligence; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication
People learn and use complex sequential actions on a daily basis, despite living in a high-dimensional environment and body. Sequential action learning is sometimes studied in cognitive psychology using button-pressing tasks such as Nissen and Bullemer's (1987) serial respone time (SRT) task. However, the SRT task only measures the speed of button presses, neglecting the rich - and difficult to control - trajectory of the arm, which can show predictive movements and other contextual effects. In this study, we evolve neural networks to carry out a mouse-based SRT task under conditions of differing prediction uncertainty. We replicate behaviors found in a recent human experiment, and explore ramifications for human sequence learning.
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