概率比率的相似性:将来自机器学习 (和其他来源) 的表示与认知模型相结合
1Department of Psychology, University at Albany, State University of New York, 1400 Washington Ave., Albany, NY, 12222, USA. gregcox7@gmail.com.
Psychonomic bulletin & review
|December 9, 2025
概括
本研究介绍了相似性作为概率比率 (SALR),这是一种新的方法,可以将机器学习矢量相似性与心理相似性联系起来. 通过更好地解释人类在复杂任务上的表现,SALR增强了认知模型.
科学领域:
- 认知科学 认知科学
- 机器学习 机器学习
- 心理学 心理学 心理学
背景情况:
- 类似性是记忆和感知理论的基础.
- 机器学习为文本和图像等复杂项目生成高维向量表示.
- 桥梁矢量相似性和心理相似性对于认知建模至关重要.
研究的目的:
- 介绍相似性作为概率比 (SALR),一个数学转换.
- 将矢量相似性 (共弦值) 运算化为一个概率比.
- 将机器学习表示集成到认知模型中.
主要方法:
- 开发了SALR,将等号相似性转换为概率比.
- 将SALR应用于扩散决策模型.
- 使用SALR分析人类反应速度和准确性.
主要成果:
- 通过机器学习相似性,SALR成功计算了扩散模型漂移率.
- 证明了SALR能够考虑人类响应时间和准确性的能力.
- 启用了对单个项目表示和信息编码的推断.
结论:
- SALR提供了一种实用的方法,用于将机器学习表示纳入认知模型.
- 萨尔提供了对人类处理复杂,自然的物品的理论见解.
- 这种方法通过计算建模来增强对感知和记忆的理解.
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