Functions of Learning Rate in Adaptive Reward Learning

• Published in: Frontiers in human neuroscience Vol. 11; p. 592
• Main Authors: Wu, Xi, Wang, Ting, Liu, Chang, Wu, Tao, Jiang, Jiefeng, Zhou, Dong, Zhou, Jiliu
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 06-12-2017; Frontiers Media S.A
• Subjects: adaptive learning; Bayesian modeling; Brain; Brain mapping; Cognitive ability; Computational neuroscience; Cortex (cingulate); Cortex (parietal); Feedback; Flexibility; fMRI; Functional magnetic resonance imaging; Hypotheses; Information technology; Learning; Learning curves; learning rate; Neuroimaging; Neuroscience; Prefrontal cortex; Reinforcement; reward; China; reward; fMRI; adaptive learning; Bayesian modeling; learning rate
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2017.00592

As a crucial cognitive function, learning applies prediction error (the discrepancy between the prediction from learning and the world state) to adjust predictions of the future. How much prediction error affects this adjustment also depends on the learning rate. Our understanding to the learning rate is still limited, in terms of (1) how it is modulated by other factors, and (2) the specific mechanisms of how learning rate interacts with prediction error to update learning. We applied computational modeling and functional magnetic resonance imaging to investigate these issues. We found that, when human participants performed a reward learning task, reward magnitude modulated learning rate. Modulation strength further predicted the difference in behavior following high vs. low reward across subjects. Imaging results further showed that this modulation was reflected in brain regions where the reward feedback is also encoded, such as the medial prefrontal cortex (MFC), precuneus, and posterior cingulate cortex. Furthermore, for the first time, we observed that the integration of the learning rate and the reward prediction error was represented in MFC activity. These findings extend our understanding of adaptive learning by demonstrating how it functions in a chain reaction of prediction updating.