Presentation 2007/5/17
Behavioral Data Analysis by Reinforcement Learning Models
Kenji Doya, Kazuyuki Samejima,
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
Date of Issue

Conference Information
Committee HIP
Conference Date 2007/5/17(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To Human Information Processing (HIP)
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) 60
Page pp.pp.-
#Pages 2
Date of Issue