不信任的机制:贝叶斯对错误信息学习的解释
Lion Schulz1, Yannick Streicher1, Eric Schulz1,2
1Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
PLoS computational biology
|May 14, 2025
概括
人类可以学会识别可靠的信息来源,即使是那些撒谎或有偏见的信息来源. 我们的研究表明,人们通常使用贝叶斯原则来学习来源的可信性,尽管噪音反可能会阻碍偏见检测.
科学领域:
- 认知科学 认知科学
- 信息科学 信息科学 信息科学
- 行为经济学是一种行为经济学.
背景情况:
- 在个人和政治背景下,区分可信信息至关重要.
- 现有的研究缺乏可控的方法来研究关于信息来源可信度的学习.
研究的目的:
- 介绍一种新的行为任务和贝叶斯模型来研究人们如何学习信息来源的可信性.
- 调查人类对各种 (错误) 信息来源的学习背后的计算机制.
主要方法:
- 开发了一个新的行为任务,参与者学习各种 (错误) 信息来源的属性.
- 将学习过程正式化为一个双倍贝叶斯推理问题.
- 在不同的反条件下分析了参与者的学习动态和信任报告.
主要成果:
- 参与者在评估信息来源时展示了贝叶斯学习动态.
- 信任报告与学识来源的可信度相关.
- 参与者表现出事先的假设来源是有帮助的.
- 更杂的反影响了源偏差的检测.
结论:
- 人类具有学习信息来源可靠性的基本能力.
- 贝叶斯式学习模型有效地捕捉了这种能力.
- 在杂的环境中,学习失败,特别是对于有偏见的新闻来源.
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