自然语言语义符合自由能原理
Natural Language Syntax Complies with the Free-Energy Principle
摘要：
自然语言语法产生一个无限的层次结构表达式数组。我们声称，这些都是用于主动推理的服务，符合自由能原理（FEP）。虽然概念上的进步以及建模和仿真工作已经试图连接语音分割和语言通信的FEP，我们扩展这个程序的基础计算负责生成语法对象。我们认为，最近提出的原则，经济的语言设计-如“最小搜索”的标准，从理论语法-坚持FEP。这为FEP提供了更大程度的解释力-关于更高的语言功能-并为语言学提供了关于可计算性的第一原则的基础。我们展示了如何树的几何深度和柯尔莫哥洛夫复杂性估计（招募Lempel-Ziv压缩算法）可以用来准确地预测法律操作的语法语法，直接在变分自由能最小化的配方。这是用来激发语言设计的一般原则，我们称之为图灵-乔姆斯基压缩（TCC）。我们使用TCC对齐关注的语言学家与FEP提供的自组织的规范性帐户，通过编组证据从理论语言学和心理语言学地面的核心原则，有效的句法计算内主动推理。
英文摘要：
Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating syntactic objects. We argue that recently proposed principles of economy in language design - such as "minimal search" criteria from theoretical syntax - adhere to the FEP. This affords a greater degree of explanatory power to the FEP - with respect to higher language functions - and offers linguistics a grounding in first principles with respect to computability. We show how both tree-geometric depth and a Kolmogorov complexity estimate (recruiting a Lempel-Ziv compression algorithm) can be used to accurately predict legal operations on syntactic workspaces, directly in line with formulations of variational free energy minimization. This is used to motivate a general principle of language design that we term Turing-Chomsky Compression (TCC). We use TCC to align concerns of linguists with the normative account of self-organization furnished by the FEP, by marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference.
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