Incorporating think-aloud traces with behavioral data in LLM-driven model discovery for risky choice yields higher held-out predictive accuracy and shifts most participants' best models from explicit-comparator to integrated-utility structures.
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Speech analysis of think-aloud protocols shows that transferable insights in insight problems are accompanied by spontaneous verbal labeling of problem types and faster performance gains.
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Think-Aloud Reshapes Automated Cognitive Model Discovery Beyond Behavior
Incorporating think-aloud traces with behavioral data in LLM-driven model discovery for risky choice yields higher held-out predictive accuracy and shifts most participants' best models from explicit-comparator to integrated-utility structures.
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Leveraging Speech to Identify Signatures of Insight and Transfer in Problem Solving
Speech analysis of think-aloud protocols shows that transferable insights in insight problems are accompanied by spontaneous verbal labeling of problem types and faster performance gains.