自然语言语法符合自由能量原理
Elliot Murphy1,2, Emma Holmes3,4, Karl Friston4
1Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Science Center, Houston, TX 77030 USA.
概括
这项研究通过主动推理将自然语言语法与自由能量原理 (FEP) 联系起来. 它提出了图灵-乔姆斯基压缩 (TCC) 来解释高效的语法计算作为一种自我组织形式.
科学领域:
- 计算神经科学是一种计算神经科学.
- 理论语言学的语言学.
- 心理语言学 心理语言学
背景情况:
- 自然语言的语法产生复杂的,层次结构.
- 自由能量原理 (FEP) 解释了生物系统如何保持组织.
- 之前的工作将FEP与语音细分和语言交流联系起来.
研究的目的:
- 将FEP扩展到解释语法对象生成的基础计算.
- 辩称语言设计原则,如"最小搜索",与FEP保持一致.
- 介绍图灵-乔姆斯基压缩 (TCC) 作为高效语法计算的原则.
主要方法:
- 理论语法和FEP的概念整合.
- 在FEP框架内建模语法计算.
- 使用树形几何深度和科尔摩戈罗夫复杂度 (Lempel-Ziv) 进行初步分析.
主要成果:
- 语言设计中的经济原则遵循FEP.
- 树形几何深度和科尔摩戈罗夫复杂度预测了与变量自由能量最小化一致的语法运算.
- TCC提供了一个有效的语法计算原则.
结论:
- 在语言学中,FEP提供了一个强大的框架来理解高级语言函数和可计算性.
- TCC将语言问题与FEP关于自我组织的解释结合起来.
- 来自语言学和心理语言学的证据支持在主动推理中建立语法计算的基础.
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