用语言模型整合认知科学碎片化研究，提升理论清晰度与跨任务预测能力。
Addressing Longstanding Challenges in Cognitive Science with Language Models
- 利用语言模型梳理分散文献，揭示概念与测量间的重叠关系。
- 实现跨任务预测，从自然语料中提取文化与生态结构信息。
- 适合希望提升研究整合能力的认知科学家，需警惕模型偏见与替代风险。
认知科学因多学科交叉特性，在研究整合、理论形式化和概念清晰性方面长期面临挑战。近年来人工智能发展，尤其是语言模型的兴起，为解决这些问题提供了新工具。语言模型可帮助梳理碎片化文献，形式化口头理论，识别不同概念与测量之间的重叠，实现跨任务预测，并从自然语料中提取文化或生态结构。然而，这些潜力伴随简化过度、黑箱性、技能退化及偏见等风险。总体而言，若谨慎使用以补充而非取代人类判断，语言模型有望推动认知科学向更整合、累积的方向发展。
原文摘要 · Abstract (English)
Cognitive science faces ongoing challenges in research integration, formalization, conceptual clarity, and other areas, in part due to its multifaceted and interdisciplinary nature. Recent advances in artificial intelligence, particularly the development of language models, offer tools that may help to address these longstanding issues. Specifically, they can help map fragmented literatures, formalize verbal theories, identify overlap among constructs and measures, generate predictions across tasks, and extract cultural or ecological structure from naturalistic data. However, these opportunities come with risks, including oversimplification, opacity, deskilling, and bias. Taken together, we conclude that language models could serve as tools for a more integrative and cumulative cognitive science when used judiciously to complement, rather than replace, human agency.
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