Tracking Divergent Thinking: Perplexity as a Unified Metric of Surprise

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.