Title: Leveraging Language Models to Resolve Persistent Issues in Cognitive Science

Abstract:

The multifaceted and interdisciplinary character of cognitive science contributes to enduring difficulties in areas such as conceptual precision, formalization, and research integration. However, recent breakthroughs in artificial intelligence, notably the emergence of language models, present potential solutions to these entrenched problems. These advanced tools offer several specific capabilities: they can synthesize fragmented bodies of literature, convert verbal theories into formal structures, detect redundancies among different constructs and measurement methods, produce predictions across various experimental tasks, and uncover cultural or ecological patterns within naturalistic datasets. Nevertheless, the adoption of these technologies introduces significant risks, including the potential for oversimplification, a lack of transparency, the erosion of specialized skills, and the perpetuation of bias. Consequently, we argue that language models can function as valuable instruments for fostering a more cohesive and progressive cognitive science, provided they are applied with care to augment, not supplant, human decision-making.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC