@inproceedings{varadarajan-etal-2025-capturing,
    title = "Capturing Human Cognitive Styles with Language: Towards an Experimental Evaluation Paradigm",
    author = "Varadarajan, Vasudha  and
      Mahwish, Syeda  and
      Liu, Xiaoran  and
      Buffolino, Julia  and
      Luhmann, Christian  and
      Boyd, Ryan L.  and
      Schwartz, H.",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2025.naacl-short.81/",
    doi = "10.18653/v1/2025.naacl-short.81",
    pages = "966--979",
    ISBN = "979-8-89176-190-2",
    abstract = "While NLP models often seek to capture cognitive states via language, the validity of predicted states is determined by comparing them to annotations created without access the cognitive states of the authors. In behavioral sciences, cognitive states are instead measured via experiments. Here, we introduce an experiment-based framework for evaluating language-based cognitive style models against human behavior. We explore the phenomenon of decision making, and its relationship to the linguistic style of an individual talking about a recent decision they made. The participants then follow a classical decision-making experiment that captures their cognitive style, determined by how preferences change during a decision exercise. We find that language features, intended to capture cognitive style, can predict participants' decision style with moderate-to-high accuracy (AUC 0.8), demonstrating that cognitive style can be partly captured and revealed by discourse patterns."
}