| _version_ | 1867010653807443968 |
|---|---|
| author | Sam Kirkham |
| author_facet | Sam Kirkham Sam Kirkham |
| collection | Wiley Open Access |
| contents | Discovering Dynamical Laws for Speech Gestures Sam Kirkham Cognitive Science AbstractA fundamental challenge in the cognitive sciences is discovering the dynamics that govern behavior. Take the example of spoken language, which is characterized 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 behavior. 10.1111/cogs.70064 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1111/cogs.70064 |
| format | Artículo Open Access |
| id | wiley_oa_10_1111_cogs_70064 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Discovering Dynamical Laws for Speech Gestures Sam Kirkham Cognitive Science Discovering Dynamical Laws for Speech Gestures Sam Kirkham Cognitive Science AbstractA fundamental challenge in the cognitive sciences is discovering the dynamics that govern behavior. Take the example of spoken language, which is characterized 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 behavior. 10.1111/cogs.70064 http://creativecommons.org/licenses/by/4.0/ |
| title | Discovering Dynamical Laws for Speech Gestures |
| topic | Cognitive Science |
| url | https://onlinelibrary.wiley.com/doi/10.1111/cogs.70064 |