Abstract
A fundamental challenge in the cognitive sciences is discovering the dynamics that govern behaviour. Take the example of spoken language, which is characterised by a highly variable and complex set of physical movements that map onto the small set of cognitive units that comprise language. What are the fundamental dynamical principles behind the movements that structure speech production? In this study, we discover models in the form of symbolic equations that govern articulatory gestures during speech. A sparse symbolic regression algorithm is used to discover models from kinematic data on the tongue and lips. We explore these candidate models using analytical techniques and numerical simulations, and find that a second-order linear model achieves high levels of accuracy, but a nonlinear force is required to properly model articulatory dynamics in approximately one third of cases. This supports the proposal that an autonomous, nonlinear, second-order differential equation is a viable dynamical law for articulatory gestures in speech. We conclude by identifying future opportunities and obstacles in data-driven model discovery and outline prospects for discovering the dynamical principles that govern language, brain and behaviour.
Abstract (translated)
认知科学中的一个基本挑战是发现支配行为的动力学规律。以口语为例，它由一系列高度多变且复杂的物理动作组成，这些动作映射到语言中有限的认知单元上。那么，在构成言语产生的运动背后的基本动力学原理是什么呢？在这项研究中，我们发现了描述发音过程中口部和舌头活动的符号方程模型。使用了一种稀疏的符号回归算法从口唇及舌的运动数据中发现这些模型。通过分析技术和数值模拟探索了候选模型，并发现在大约三分之一的情况下，需要非线性力来准确地建模发声动态过程，而二次线性模型则在大多数情况下表现出很高的准确性。这支持了这样一个提议：一个自治的、非线性的二阶微分方程可以作为言语发音运动的动力学法则。 我们最后指出了数据驱动型模型发现中的未来机会和障碍，并概述了发现支配语言、大脑及行为的动力学原理的可能性。
URL
https://arxiv.org/abs/2504.04849
