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Confidence biases and learning among intuitive Bayesians

机译：贝叶斯直觉中的自信心偏差和学习

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摘要

We design a double-or-quits game to compare the speed of learning one’s specific ability with the speed of rising confidence as the task gets increasingly difficult. We find that people on average learn to be overconfident faster than they learn their true ability and we present an intuitive-Bayesian model of confidence which integrates confidence biases and learning. Uncertainty about one’s true ability to perform a task in isolation can be responsible for large and stable confidence biases, namely limited discrimination, the hard–easy effect, the Dunning–Kruger effect, conservative learning from experience and the overprecision phenomenon (without underprecision) if subjects act as Bayesian learners who rely only on sequentially perceived performance cues and contrarian illusory signals induced by doubt. Moreover, these biases are likely to persist since the Bayesian aggregation of past information consolidates the accumulation of errors and the perception of contrarian illusory signals generates conservatism and under-reaction to events. Taken together, these two features may explain why intuitive Bayesians make systematically wrong predictions of their own performance.
机译：我们设计了一个双打或双打游戏，以比较学习特定技能的速度和随着任务的增加而增加的自信心的速度。我们发现平均而言，人们学会自信的速度要比学习真正能力的速度快，并且我们提出了一种直观的贝叶斯信心模型，该模型融合了信心偏差和学习能力。如果不确定某人独立执行某项任务的真实能力，可能会导致较大且稳定的置信偏差，即歧视有限，难易效应，邓宁-克鲁格效应，保守的经验学习和过度精确的现象（无过度精确），如果受试者充当贝叶斯学习者，他们仅依赖于因怀疑而产生的顺序感知的表演线索和逆势幻觉信号。此外，由于过去信息的贝叶斯聚合巩固了错误的积累，并且逆势幻觉信号的感知产生了保守性和对事件的反应不足，这些偏见可能会继续存在。综上所述，这两个特征可以解释为什么直观的贝叶斯主义者会系统地错误地预测自己的表现。

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