人类如何给人带来信心? 对感知元认知过程模型的全面比较
Medha Shekhar1, Dobromir Rahnev1
1School of Psychology, Georgia Institute of Technology.
Journal of experimental psychology. General
|December 14, 2023
概括
这项研究比较了14个计算模型的决策信心. 一个新的组合模型,logWEV,在所有感知任务中表现出卓越的表现,为信任判断提供了更可概括的解释.
科学领域:
- 认知心理学 认知心理学
- 计算神经科学是一种神经科学.
- 决策科学 决策科学 决策科学
背景情况:
- 人类拥有超认知能力,通过信心判断来衡量决策准确性.
- 存在众多的信心计算模型,但缺乏直接比较,阻碍了模型选择.
- 了解信心生成机制对于认知和计算建模至关重要.
研究的目的:
- 为了比较14个知名的信心计算模型的经验充分性.
- 确定对感知任务的最佳性能模型,并探索新的组合.
- 提供当前信任模型及其基础机制的全面评估.
主要方法:
- 将14种不同的信心计算模型与来自三个大规模感知实验的数据相匹配.
- 采用参数和模型恢复分析来评估模型的区分和可靠性.
- 基于适合性和解释各种数据模式的能力来评估模型性能.
主要成果:
- 逻辑正常元噪声 (LogN) 模型最适合于实验1和2中的数据,而正证据 (PE) 模型最适合于实验3.
- 一个新的混合模型,logWEV,结合了LogN和加权证据和可见性 (WEV) 原则,在所有数据集中都超过了所有单个模型.
- 模型回收分析表明,回收能力一般很好,但特殊的例外影响了模型歧视.
结论:
- 与现有的模型相比,logWEV模型提供了更强大和更容易概括的基本感知任务的信心判断.
- 这项研究促进了我们对超认知信心背后的计算机制的理解.
- 这些发现强调了模型比较的重要性以及混合模型在认知科学中的潜力.
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