Can model-free reinforcement learning explain deontological moral judgments?

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• 作者：Alisabeth Ayars ^{alisabetha@email.arizona.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Moral judgment ; Model-free ; Model-based ; Dual-system ; Reinforcement learning
• 刊名：Cognition
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：150
• 期：Complete
• 页码：232-242
• 全文大小：433 K

文摘

Dual-systems frameworks propose that moral judgments are derived from both an immediate emotional response, and controlled/rational cognition. Recently Cushman (2013) proposed a new dual-system theory based on model-free and model-based reinforcement learning. Model-free learning attaches values to actions based on their history of reward and punishment, and explains some deontological, non-utilitarian judgments. Model-based learning involves the construction of a causal model of the world and allows for far-sighted planning; this form of learning fits well with utilitarian considerations that seek to maximize certain kinds of outcomes. I present three concerns regarding the use of model-free reinforcement learning to explain deontological moral judgment. First, many actions that humans find aversive from model-free learning are not judged to be morally wrong. Moral judgment must require something in addition to model-free learning. Second, there is a dearth of evidence for central predictions of the reinforcement account—e.g., that people with different reinforcement histories will, all else equal, make different moral judgments. Finally, to account for the effect of intention within the framework requires certain assumptions which lack support. These challenges are reasonable foci for future empirical/theoretical work on the model-free/model-based framework.