Those same tools can look for patterns in everyday language and behavior that reflect culture or environment. They can propose experiments and generate predictions that span tasks and populations. Using language models thoughtfully could speed discovery by highlighting promising connections and making complex arguments easier to test, while preserving the careful reasoning that researchers provide.
Any technological aid brings trade-offs. Models can hide errors behind fluent outputs, reproduce biases present in their training data, and encourage overreliance on automated summaries. The most helpful role for these systems is to augment human judgment and creativity, not to replace them. Follow the link to see how these possibilities intersect with human potential, learning, and inclusion, and to consider what safeguards scientists should build as they explore this new terrain.
Cognitive science faces ongoing challenges in research integration, formalization, conceptual clarity, and other areas, partly due to its multifaceted and interdisciplinary nature. Recent advances in artificial intelligence, particularly the development of language models, offer tools that may help 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.
