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A pleasant surprise: perplexity from large language models assesses divergent thinking

Poster Session D - Monday, March 9, 2026, 8:00 – 10:00 am PDT, Fairview/Kitsilano Ballroom

Yuhua Yu¹ (), Quentin Raffaelli², Simone Luchini³, Roger E. Beaty³, Jessica R. Andrews-Hanna¹; ¹Department of Psychology, University of Arizona, ²Department of Psychology, University of Calgary, ³Department of Psychology, Pennsylvania State University

Divergent thinking (DT) is central to human innovation and creativity, yet its assessment remains largely constrained by lab-based tasks and a focus on final products, overlooking the rich information embedded in the thought process itself. We introduce perplexity—a computational measure of surprise derived from large language models – as a theoretically grounded DT measure that can be flexibly applied to verbal data in both process- and product-oriented contexts. Study 1 links the perplexity in participants’ verbalized thought processes —during both goal-directed divergent thinking and unprompted stream of consciousness—with individuals’ DT performance. We show that a higher level of surprising content (higher perplexity) predicts greater DT capacity. Study 2 focuses on creative products, validating perplexity as an automated measure of originality in solutions to open-ended, real-world problems. Together, these findings establish perplexity as a unified metric that bridges theories of creative cognition with naturalistic, verbal data. By providing a scalable measurement of DT via both participants’ thought processes and creative products, our approach opens new avenues for scalable research on DT across diverse contexts.

Topic Area: THINKING: Other