The valence of new information influences learning rates in humans: good news tends to receive more weight than bad news. We investigated this learning bias in four experiments, by systematically manipulating the source of required action (free versus forced choices), outcome contingencies (low versus high reward) and motor requirements (go versus no-go choices). Analysis of model-estimated learning rates showed that the confirmation bias in learning rates was specific to free choices, but was independent of outcome contingencies. The bias was also unaffected by the motor requirements, thus suggesting that it operates in the representational space of decisions, rather than motoric actions. Finally, model simulations revealed that learning rates estimated from the choice-confirmation model had the effect of maximizing performance across low- and high-reward environments. We therefore suggest that choice-confirmation bias may be adaptive for efficient learning of action–outcome contingencies, above and beyond fostering person-level dispositions such as self-esteem.

新信息的价格会影响人类的学习速度：好消息往往比坏消息受到更多的重视。我们通过系统地操纵所需动作的来源（自由选择与强迫选择），结果意外事件（低奖励与高奖励）和运动要求（执行选择与不执行选择），在四个实验中研究了这种学习偏差。对模型估计的学习率的分析表明，学习率的确认偏差特定于自由选择，但不依赖于结果的偶然性。偏见也不受运动要求的影响，因此表明它在决策的代表性空间而不是在运动中起作用。最后，模型仿真显示，根据选择确认模型估算的学习率具有在低薪和高薪环境中最大化性能的作用。因此，我们建议选择确认偏见可能适合于有效地学习行动-结果的意外情况，这超出了培养诸如自尊之类的人的水平倾向的能力。