报告人： James Wei Chen, PhD
时间： 2017-11-08 15:00 - 16:30
地点： Room 1113, Wang Kezhen Building
Abstract: Models of reinforcement learning (RL) are prevalent in the decision-making literature, but not all behavior appears to conform to the gradual convergence that is a central feature of RL. In some cases learning appears to happen all at once. Limited prior research on these ``epiphanies'' has shown evidence of sudden changes in behavior, but it remains unclear how such epiphanies occur. We propose a novel sequential-sampling model of epiphany learning (EL), and test it using an eye-tracking experiment. In the experiment, subjects repeatedly play a strategic game that has an optimal strategy. Subjects can learn over time from feedback, but are also allowed to commit to a strategy at any time, eliminating all other options and opportunities to learn. We find that the EL model is consistent with the choices, eye movements, and pupillary responses of subjects who commit to the optimal strategy (correct epiphany) but not always to those who commit to a suboptimal strategy or who do not commit at all. Our findings suggest that EL is driven by a latent evidence accumulation process which can be revealed with eye-tacking data.
Bio: James Wei Chen received his PhD in Economics from the Ohio State U and is currently a post-doc research fellow at the Chinese University of Hong Kong. His research interests focus on human decison-making, learning, and game theory. In this talk, he is going to discuss work related to his recent publication on epiphany learning on PNAS.
Host: Dr. Lusha Zhu