Addressing Longstanding Challenges in Cognitive Science with Language Models
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914618567294976 |
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| author | Wulff, Dirk U. Mata, Rui |
| author_facet | Wulff, Dirk U. Mata, Rui |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_00206 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Addressing Longstanding Challenges in Cognitive Science with Language Models Wulff, Dirk U. Mata, Rui Artificial Intelligence Computation and Language 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. |
| title | Addressing Longstanding Challenges in Cognitive Science with Language Models |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2511.00206 |