Presentation | 2007/5/17 Behavioral Data Analysis by Reinforcement Learning Models Kenji Doya, Kazuyuki Samejima, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Reinforcement learning is a theoretical framework for adaptive agents, including animals, humans, and robots, to acquire novel behaviors based on scalar reward signals. We developed a Bayesian framework for estimating the hidden variables and parameters of a reinforcement learning agent from the sequence of perception, action, and reward it experienced. Here we report how we applied the framework to data analyses of neuronal recording and functional brain imaging experiments, and discuss the potential use of the paradigm for understanding and assessment of human brain functions. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | reinforcement learning / Bayesian inference / meta-parameters / basal ganglia |
Paper # | HCS2007-11,HIP2007-11 |
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Committee | HCS |
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Conference Date | 2007/5/17(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Human Communication Science (HCS) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Behavioral Data Analysis by Reinforcement Learning Models |
Sub Title (in English) | |
Keyword(1) | reinforcement learning |
Keyword(2) | Bayesian inference |
Keyword(3) | meta-parameters |
Keyword(4) | basal ganglia |
1st Author's Name | Kenji Doya |
1st Author's Affiliation | Okinawa Institute of Science and Technology:ATR Computational Neuroscience Laboratories() |
2nd Author's Name | Kazuyuki Samejima |
2nd Author's Affiliation | Tamagawa University |
Date | 2007/5/17 |
Paper # | HCS2007-11,HIP2007-11 |
Volume (vol) | vol.107 |
Number (no) | 59 |
Page | pp.pp.- |
#Pages | 2 |
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