人们如何预测随机步行? 对于人类认知模型的教训
Jake Spicer1, Jian-Qiao Zhu1, Nick Chater2
1Department of Psychology, University of Warwick.
Psychological review
|September 19, 2024
概括
人类预测者在随机步行任务中偏离理性预测,表现出过度的波动性. 采样模型最能解释这些认知偏差,表明计算噪音影响决策.
科学领域:
- 认知科学是一种认知科学.
- 行为经济学是一种行为经济学.
- 计算神经科学是一种计算神经科学.
背景情况:
- 预测波动的值,如随机走路,是常见的.
- 人类偏离合理预测 (重复最后一个值),显示过度波动.
- 这些偏差可能会揭示认知的统计特征.
研究的目的:
- 探索预测的认知模型,使用随机行走的人类偏差作为标准.
- 为了确定哪些模型最好地解释观察到的人类预测行为.
主要方法:
- 对比了来自两个实验的人类数据与贝叶斯式,基于错误的学习,自回归和采样模型.
- 分析了随机步行属性的系统偏差 (例如,波动性).
主要成果:
- 采样模型为总和个人人类预测数据提供了最适合的模型.
- 人类预测的变化与随机预测系统有关.
结论:
- 以计算噪声为特征的认知采样机制解释了人类预测偏差.
- 预测的变化主要来自内部决策噪音,而不是输出阶段的噪音.
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