Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA.
Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Computational and Biological Learning Laboratory, Department of Engineering, Cambridge University, Cambridge CB2 1PZ, UK.
Neuron. 2018 Sep 5;99(5):1083-1097.e6. doi: 10.1016/j.neuron.2018.07.035. Epub 2018 Aug 16.
Accurate decisions require knowledge of prior probabilities (e.g., prevalence or base rate), but it is unclear how prior probabilities are learned in the absence of a teacher. We hypothesized that humans could learn base rates from experience making decisions, even without feedback. Participants made difficult decisions about the direction of dynamic random dot motion. Across blocks of 15-42 trials, the base rate favoring left or right varied. Participants were not informed of the base rate or choice accuracy, yet they gradually biased their choices and thereby increased accuracy and confidence in their decisions. They achieved this by updating knowledge of base rate after each decision, using a counterfactual representation of confidence that simulates a neutral prior. The strategy is consistent with Bayesian updating of belief and suggests that humans represent both true confidence, which incorporates the evolving belief of the prior, and counterfactual confidence, which discounts the prior.
准确的决策需要先验概率（例如，患病率或基础率）的知识，但在没有教师的情况下，先验概率是如何被学习的还不清楚。我们假设，即使没有反馈，人类也可以从经验中学习基础比率，通过做出决策。参与者对动态随机点运动的方向做出了困难的决策。在 15-42 次试验的块中，支持左或右的基础比率发生了变化。参与者没有被告知基础比率或选择准确性，但他们逐渐改变了选择，从而提高了决策的准确性和信心。他们通过在每次决策后更新对基础比率的知识来实现这一点，使用对信心的反事实表示，该表示模拟了中立的先验。该策略与信念的贝叶斯更新一致，表明人类既代表真实的信心，其中包括对先验的不断发展的信念，也代表反事实的信心，其中扣除了先验。
