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Title: Heterogeneity in generalized reinforcement learning and its relation to cognitive ability
Authors: Chen, Shu-Heng;Du, Y.-R.
Contributors: 經濟學系
Keywords: Artificial intelligence;Cognitive systems;Cognitive ability;Different sizes;Experience-weighted attraction learning;Granularity;Individual Differences;Learning behavior;Learning models;Likelihood ratio tests;nocv1;Reinforcement learning
Date: 2017-05
Issue Date: 2017-05-08 14:38:52 (UTC+8)
Abstract: In this paper, we study the connections between working memory capacity (WMC) and learning in the context of economic guessing games. We apply a generalized version of reinforcement learning, popularly known as the experience-weighted attraction (EWA) learning model, which has a connection to specific cognitive constructs, such as memory decay, the depreciation of past experience, counterfactual thinking, and choice intensity. Through the estimates of the model, we examine behavioral differences among individuals due to different levels of WMC. In accordance with ‘Miller's magic number’, which is the constraint of working memory capacity, we consider two different sizes (granularities) of strategy space: one is larger (finer) and one is smaller (coarser). We find that constraining the EWA models by using levels (granules) within the limits of working memory allows for a better characterization of the data based on individual differences in WMC. Using this level-reinforcement version of EWA learning, also referred to as the EWA rule learning model, we find that working memory capacity can significantly affect learning behavior. Our likelihood ratio test rejects the null that subjects with high WMC and subjects with low WMC follow the same EWA learning model. In addition, the parameter corresponding to ‘counterfactual thinking ability’ is found to be reduced when working memory capacity is low. © 2016 Elsevier B.V.
Relation: Cognitive Systems Research, 42, 1-22
Data Type: article
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