显而易见的学习偏差来自最佳推理:从主方程分析的洞察力
1Department of Psychology, New York University, New York, NY 10003.
概括
人类在决策任务中的行为可能因模型匹配而出现偏见,不一定是客观偏见. 客观贝叶斯推理可以模仿像确认偏差这样的偏差,当用简化学习模型分析时.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 强化学习是一种强化学习.
背景情况:
- 以前的研究表明,在伯努利强盗任务中,人类的决策表现出积极性和确认偏见.
- 这些偏见通常通过将具有恒定学习率的Q学习模型与人类数据相匹配来推断.
- 这意味着人类可能无法客观地整合新信息.
研究的目的:
- 调查人类决策中的明显偏见是否来自客观的贝叶斯推理或实际的认知偏见.
- 分析学习系统的动态,使用大师方程来理解行为特征.
- 为了确定是否需要暂时变化的学习速率来准确地建模人类行为.
主要方法:
- 应用贝叶斯推理来模型信念更新在一个双臂的伯努利强盗任务.
- 使用大师方程分析了贝叶斯推理和Q学习模型的随机动态.
- 在不同学习模型中比较行为特征,特别是动作切换概率.
主要成果:
- 即使是客观的贝叶斯推理,当通过Q学习模型近似时,也可能表现出积极性和确认偏差.
- 贝叶斯推理,当被制定为有效的Q学习算法时,具有无偏见的,暂时下降的学习率.
- 确认偏差和暂时下降的学习率都具有减少行动切换概率的行为特征.
结论:
- 人类决策中的明显偏见可能是简化模型拟合的工件,不一定是固有的认知偏见.
- 与客观贝叶斯推理相一致的暂时下降的学习率,可以模仿确认偏差.
- 准确地建模人类行为需要考虑暂时变化的学习速率,才能得出有偏见的决策.
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