心理学
强化学习
规范性
认知心理学
价值(数学)
钢筋
经验证据
社会心理学
人工智能
机器学习
认识论
计算机科学
哲学
作者
Stefano Palminteri,Maël Lebreton
标识
DOI:10.1016/j.tics.2022.04.005
摘要
Humans do not integrate new information objectively: outcomes carrying a positive affective value and evidence confirming one's own prior belief are overweighed. Until recently, theoretical and empirical accounts of the positivity and confirmation biases assumed them to be specific to 'high-level' belief updates. We present evidence against this account. Learning rates in reinforcement learning (RL) tasks, estimated across different contexts and species, generally present the same characteristic asymmetry, suggesting that belief and value updating processes share key computational principles and distortions. This bias generates over-optimistic expectations about the probability of making the right choices and, consequently, generates over-optimistic reward expectations. We discuss the normative and neurobiological roots of these RL biases and their position within the greater picture of behavioral decision-making theories.
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