使用人工神经网络来揭示人类信心计算计算
Medha Shekhar1,2, Herrick Fung1, Krish Saxena1
1School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, United States of America.
PLoS computational biology
|December 29, 2025
概括
人类的信心判断,特别是在复杂的任务中,更好地通过一个专注于前两个选择之间的差异 (Top2Diff) 而不是更广泛的证据的模型来解释. 这一发现提升了对认知决策过程的理解.
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.
- 人工智能的人工智能
背景情况:
- 人类的信任判断对于决策准确性至关重要,但对于自然主义的,多选项任务来说,人们对它的理解很差.
- 现有的认知模型是有限的,主要解决简单的,两种选择的场景.
研究的目的:
- 探讨对多选项,自然主义决策的信任机制.
- 用一种新的卷积神经网络 (CNN) 模型来比较各种信任策略.
主要方法:
- 利用CNN模型 (RTNet) 来分析八位替代数字歧视任务中的信心策略 (N=60).
- 基于证据分布 (完整与子集) 和证据类型 (后面与原始) 的比较策略.
主要成果:
- 使用前两种选择之间的原始证据差异的Top2Diff模型显示出优越的定量和质量匹配.
- 这种模型还提供了对人类信心评级的最佳预测.
结论:
- 人类的信心似乎依赖于特定的证据子集,挑战像贝叶斯信心假设这样的现有理论.
- 在复杂的决策环境中,CNN提供了一个强大的框架来建模人类的信心.
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