融合计算与认知，探索语言符号表征的深层机制
Looking forward: Linguistic theory and methods
- 从效率、局部性等角度检验语言符号表征假设
- 神经网络推动语言理论与分析方法革新
- 关注语言交互中的主体间性，拓展演化语言学视野
本章探讨语言学理论与方法的最新进展，聚焦计算、认知与演化视角的深度融合。主要呈现四大趋势：(1) 明确检验符号表征的假设，如效率、局部性及概念语义基础；(2) 人工神经网络对理论争议与语言分析的影响；(3) 主体间性在语言理论中的重要性提升；(4) 演化语言学持续发展。通过连接计算机科学、心理学、神经科学与生物学，本文为语言学研究的未来图景提供前瞻性视角。
原文摘要 · Abstract (English)
This chapter examines current developments in linguistic theory and methods, focusing on the increasing integration of computational, cognitive, and evolutionary perspectives. We highlight four major themes shaping contemporary linguistics: (1) the explicit testing of hypotheses about symbolic representation, such as efficiency, locality, and conceptual semantic grounding; (2) the impact of artificial neural networks on theoretical debates and linguistic analysis; (3) the importance of intersubjectivity in linguistic theory; and (4) the growth of evolutionary linguistics. By connecting linguistics with computer science, psychology, neuroscience, and biology, we provide a forward-looking perspective on the changing landscape of linguistic research.
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