人类决策中的认知偏差可能源于学习速率下降，而非真实偏见。
Bias or Optimality? Disentangling Bayesian Inference and Learning Biases in Human Decision-Making
- 用贝叶斯推理建模行为，发现对称但递减的学习率可模拟出认知偏差
- 无论是否真实存在偏见，递减学习率都能产生与确认偏误相同的实验表现
- 提出实验设计区分真实偏见与学习速率下降带来的伪像
近期研究认为人类在双臂伯努利老虎机任务（TABB）中的行为受正向偏见和确认偏误影响，暗示其不客观整合新信息。然而我们发现，即使代理通过客观贝叶斯推理更新信念，使用不对称学习率的标准Q-learning模型仍能拟合出这两种偏误。将贝叶斯推理视为一种有效Q-learning算法时，其具有对称但递减的学习率。我们通过主方程分析这些学习系统的随机动态，发现确认偏误与无偏但递减的学习率会产生产生相同的可观察行为特征。最后，我们提出实验方案以区分真实的认知偏见与递减学习率造成的伪像。
原文摘要 · Abstract (English)
Recent studies claim that human behavior in a two-armed Bernoulli bandit (TABB) task is described by positivity and confirmation biases, implying that humans do not integrate new information objectively. However, we find that even if the agent updates its belief via objective Bayesian inference, fitting the standard Q-learning model with asymmetric learning rates still recovers both biases. Bayesian inference cast as an effective Q-learning algorithm has symmetric, though decreasing, learning rates. We explain this by analyzing the stochastic dynamics of these learning systems using master equations. We find that both confirmation bias and unbiased but decreasing learning rates yield the same behavioral signatures. Finally, we propose experimental protocols to disentangle true cognitive biases from artifacts of decreasing learning rates.
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