Advancing Cognitive Science with LLMs
This addresses the problem of interdisciplinary synthesis and clarity for cognitive scientists, but it is incremental as it reviews existing capabilities without presenting new results.
The paper tackles the challenges of knowledge synthesis and conceptual clarity in cognitive science by exploring how large language models (LLMs) can support areas like cross-disciplinary connections and theory formalization, concluding that LLMs can serve as tools for a more integrative and cumulative field when used to complement human expertise.
Cognitive science faces ongoing challenges in knowledge synthesis and conceptual clarity, in part due to its multifaceted and interdisciplinary nature. Recent advances in artificial intelligence, particularly the development of large language models (LLMs), offer tools that may help to address these issues. This review examines how LLMs can support areas where the field has historically struggled, including establishing cross-disciplinary connections, formalizing theories, developing clear measurement taxonomies, achieving generalizability through integrated modeling frameworks, and capturing contextual and individual variation. We outline the current capabilities and limitations of LLMs in these domains, including potential pitfalls. Taken together, we conclude that LLMs can serve as tools for a more integrative and cumulative cognitive science when used judiciously to complement, rather than replace, human expertise.