一个数学理论的关系概括在过渡性推理的数学理论
Samuel Lippl1,2,3, Kenneth Kay1,2,4, Greg Jensen1,3,5
1Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY.
bioRxiv : the preprint server for biology
|September 4, 2023
概括
学习系统可以使用简单的统计原理进行过渡性推理 (TI). 然而,复杂的神经网络表现出很差的概括性,突出了理解关系抽象的差距.
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.
- 机器学习 机器学习
背景情况:
- 人类和动物对新情况进行学习关系的概括,这是高级认知的一个关键方面.
- 过渡性推理 (TI) 涉及学习关系 (A > B,B > C) 和它们的概括 (A > C).
- 在学习系统中实现这种概括的潜在诱导偏见仍然不清楚.
结论:
- 最小的统计学学习原理可以解释过渡性的诱导偏差.
- 在TI任务中经验观察到的行为是由这些原则解释的.
- 为了解关系抽象的神经基础,建立了一个正式的框架.
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