Computational modeling of epiphany learning

Models of reinforcement learning (RL) are prevalent in the decision-making literature, but not all behavior seems to conform to the gradual convergence that is a central feature of RL. In some cases learning seems to happen all at once. Limited prior research on these "epiphanies" has show...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS Vol. 114; no. 18; p. 4637
Main Authors: Chen, Wei James, Krajbich, Ian
Format: Journal Article
Language:English
Published: United States 02.05.2017
Subjects:
beauty contest
epiphany learning
pupil dilation
eye tracking
decision making
ISSN:1091-6490, 1091-6490
Online Access:Get more information
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Summary:Models of reinforcement learning (RL) are prevalent in the decision-making literature, but not all behavior seems to conform to the gradual convergence that is a central feature of RL. In some cases learning seems 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 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 of 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 that can be revealed with eye-tracking data.
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ISSN:1091-6490
1091-6490
DOI:10.1073/pnas.1618161114