The current progress of language research has resulted in extensive interdisciplinary interactions and in-depth integration with multiple fields. Dialogue and collaboration with related studies in cognitive neuroscience, computational linguistics, and artificial intelligence (AI) have substantially driven its interdisciplinary development.

Cognitive neuroscience

Language is a high-level cognitive function of the human brain, and the development and related research findings of cognitive neuroscience play a crucial role in advancing language research. Neuroscience examines the neural mechanisms and foundations of language in the brain, while cognitive science focuses on revealing the cognitive principles behind perception, learning, attention, memory, reasoning, and language comprehension and production, often using behaviorist research methods.

Although language studies and cognitive neuroscience are closely related, a fully compatible platform between the two has not yet been established. Generally, language research progresses from theory to verification, while cognitive neuroscience takes the opposite approach, proposing theoretical hypotheses based on empirical research and data support. Therefore, the empirical data from the cognitive neuroscience does not always match perfectly with the theoretical assumptions of language studies. The main difference between the two lies in that language research focuses on general language knowledge, while cognitive neuroscience focuses on the neural mechanisms of language.

Computational linguistics

Computational linguistics is a new discipline that utilizes computers to process and analyze natural language, focusing on its comprehension and automatic generation. Traditional language research seeks to discover universal rules of natural language from limited samples, but natural language is full of exceptions. In this context, corpora, characterized by the provision of extensive, objective, and reliable language resources, effectively address this problem. The development of language databases and the rise of corpus linguistics are significant markers of the advancement of computational linguistics. “Large-scale model” and “authenticity” are the core features of corpus linguistic data.

The research methods of computational linguistics for language studies mainly involve formal methods, automatic parsing algorithms, and statistical techniques. Various formal methods are often used in the automated processing of phonetics, vocabulary, morphology, syntax, semantics, and pragmatics.

AI

Language intelligence is an important aspect of AI. Language intelligence research is a branch of AI research based on language data, mainly using various digital technologies to process and parse human language, with the aim of achieving human-machine collaboration. By using technologies such as big data and deep learning to parse, annotate, and process speech information, based on physiological mechanisms of the human brain and its semantic processing rules, machines may attain more advanced language capabilities.

The rapid development of AI has also had a profound impact on language research. As a key area of AI, natural language processing enables rapid processing of large amounts of textual data and the extraction of meaningful insights, laying the foundations for human-machine dialogue, machine translation, and language teaching. However, due to its limited interpretability and accuracy in natural language processing, it cannot yet fully replace traditional language research methods.

Cognition, computation, and AI are closely interconnected. Language research based on cognition focuses on clarifying the cognitive mechanisms and neural foundations underlying language comprehension, while language research based on computation and AI aims to simulate intelligence. The research findings of cognitive neuroscience can provide theoretical hypotheses and empirical evidence for language studies, while the development of computational and artificial intelligence continuously generates new application demands, which in turn can promote the development of cognitive research. Based on the dialectical unity of these three aspects, language studies will transit from a single-discipline model to an interdisciplinary model that integrates related disciplines, revealing the mysteries of language, mind, and brain. Research methods will shift from a single-dimensional to multi-dimensional system, transitioning from external research based on theoretical thinking and the observation of results from behavioral experiments to an interactive system where results from cognitive neuroscience experiments and external research findings corroborate each other.

Language studies should strengthen interdisciplinary research, actively leveraging research achievements from various disciplines and fully drawing on diverse research concepts and methodologies. This approach will promote the integrated development of language research from a multidisciplinary perspective, thereby advancing the synergistic development of theoretical and applied language research in the new era.

Wang Yun is an associate professor from the School of Foreign Languages and Literature at Suzhou University of Science and Technology.