类似性判断的顺序表征
Jonathan D Victor1, Guillermo Aguilar1, Suniyya A Waraich1
1Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, 1300 York Avenue, New York, NY 10065.
ArXiv
|October 24, 2023
概括
这项研究引入了一个新的框架,用于分析认知神经科学中的相似性判断. 它有助于从个体如何感知刺激之间的相似性中推断域结构.
科学领域:
- 认知神经科学 认知神经科学
- 系统神经科学 系统神经科学
- 心理物理学的精神物理.
背景情况:
- 了解感知和语义相似性判断在神经科学中至关重要.
- 现有的方法经常对将感知距离转换为相似性做出强有力的假设.
- 需要一个强大的框架来分析这些判断,并推断潜在的域结构.
研究的目的:
- 为分析相似性判断提供一个新的计算框架.
- 关于感知距离和相似性之间的关系做出有限的假设.
- 从经验相似性数据推断感知或语义领域的基本结构.
主要方法:
- 使用对相对相似性的经验判断 (一个刺激被认为与参考相似的几率).
- 使用贝叶斯估计来从相似性判断中推导选择概率.
- 根据选择概率的等级顺序关系,推导出与对称不相似性,超对称空间和附加树的兼容性指数.
主要成果:
- 开发了一个框架,需要对相似感知进行最小的假设.
- 根据它们与不同空间结构的兼容性来表征相似性判断的新型索引.
- 通过模拟和分析视觉领域的心理物理数据集来证明框架的实用性.
结论:
- 拟议的框架提供了一种灵活而强大的方法来分析相似性判断.
- 它可以从经验数据中对潜在的域结构进行强有力的推断.
- 为认知和系统神经科学研究人员提供了一个计算工具.
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