Murphy Elliot, Holmes Emma, Friston Karl
Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Science Center, Houston, TX 77030 USA.
Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center, Houston, TX 77030 USA.
Synthese. 2024;203(5):154. doi: 10.1007/s11229-024-04566-3. Epub 2024 May 3.
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. While we mostly focus on building new principled conceptual relations between syntax and the FEP, we also show through a sample of preliminary examples 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.
自然语言句法产生了一系列具有层次结构的、数量无限的表达式。我们认为,这些表达式是根据自由能原理(FEP)用于主动推理的。虽然概念上的进展以及建模和模拟工作试图将语音分割和语言交流与自由能原理联系起来,但我们将这个项目扩展到负责生成句法对象的底层计算。我们认为,最近在语言设计中提出的经济性原则——比如理论句法中的“最小搜索”标准——符合自由能原理。这为自由能原理在更高层次语言功能方面提供了更大程度的解释力,并为语言学在可计算性的第一原理方面提供了基础。虽然我们主要关注建立句法和自由能原理之间新的有原则的概念关系,但我们也通过一些初步示例表明,树几何深度和柯尔莫哥洛夫复杂度估计(采用莱姆佩尔 - 齐夫压缩算法)如何能够直接根据变分自由能最小化的公式,准确预测句法工作空间上的合法操作。这被用于激发一种我们称为图灵 - 乔姆斯基压缩(TCC)的语言设计通用原则。我们通过整理理论语言学和心理语言学的证据,利用图灵 - 乔姆斯基压缩将语言学家的关注点与自由能原理提供的自组织规范解释联系起来,从而为主动推理中高效句法计算的核心原则奠定基础。