学习自信:代理商如何根据预测错误来学习自信
Pierre Le Denmat1, Kobe Desender2, Tom Verguts3
1Brain and Cognition, KU Leuven, Leuven, Belgium; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Cognition
|September 25, 2025
概括
人类根据反的预测错误动态调整决策信心. 这种学习过程类似于训练神经网络,有效地校准信心,即使客观表现保持不变.
科学领域:
- 认知心理学 认知心理学
- 神经科学是一个神经科学.
- 机器学习 机器学习
背景情况:
- 决策信心理想反映了正确选择的概率.
- 衡量信心的学习机制仍然不清楚.
研究的目的:
- 调查个人如何学会校准他们的决策信心.
- 为了确定人类是否使用预测错误来更新信心计算.
主要方法:
- 一个感知决策实验,使用操纵反制度 (正面与负面).
- 在反条件下对信任评级和客观绩效的分析.
- 使用基于预测错误的单层神经网络建模信心.
主要成果:
- 信心评级动态跟踪反,随着积极的反增加,随着消极的反减少.
- 反操纵没有影响目标绩效.
- 结合预测错误的神经网络模型比基于价值的模型更适合行为数据.
结论:
- 人类信心计算是一个动态的过程,根据试验级预测错误进行更新.
- 这种更新机制在统计学上是高效的,并且特定于信任,而不是客观业绩.
- 结果支持一个类似于训练函数近似器使用预测错误的学习原理.
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