偏差还是最优性？解耦人类决策中的贝叶斯推断与学习偏差
Bias or Optimality? Disentangling Bayesian Inference and Learning Biases in Human Decision-Making
AI总结：
针对TABB任务中所谓正向与证实学习偏差，研究用主方程分析贝叶斯Q-learning动力学，证明客观贝叶斯推断的递减学习率会产生相同行为特征，并提出区分真实认知偏差与模型伪影的实验方案。
摘要：
近期研究声称，人类在双臂伯努利老虎机（TABB）任务中的行为可由正向偏差和证实偏差描述，这意味着人类不会客观地整合新信息。然而，我们发现，即使智能体通过客观的贝叶斯推断更新信念，用具有非对称学习率的标准Q-learning模型拟合仍会恢复这两种偏差。被表述为一种有效Q-learning算法的贝叶斯推断具有对称但递减的学习率。我们通过使用主方程分析这些学习系统的随机动力学来解释这一点。我们发现，证实偏差和无偏但递减的学习率会产生相同的行为特征。最后，我们提出实验方案，以将真正的认知偏差与递减学习率导致的伪影解耦。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
