Think-Aloud Reshapes Automated Cognitive Model Discovery Beyond Behavior
Computational cognitive models discovered using large language models have so far relied solely on behavioral data. However, it is well-known that models produced from the behavioral trajectory alone are typically under-determined. In this work, we explore the use of Think Aloud traces as an additional form of data constraint during automated model discovery. When applied to the domain of risky decision-making, we find that the models discovered with think-aloud achieve significantly improved predictive performance on held-out data. Additionally, we find that the discovered models belong to different structural classes than those discovered from behavior alone for the majority of particip...
| Authors | Hanbo Xie, Akshay K. Jagadish, Lan Pan et al. |
|---|---|
| Primary category | q-bio.NC |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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