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Poster B34 in Poster Session B - Thursday, August 8, 2024, 1:30 – 3:30 pm, Johnson Ice Rink
Deciphering the meta-cognitive experiences in creative problem-solving
Yang-Fan Lu1 (), Qianli Yang2, Hang Zhang1; 1Peking University, 2Chinese Academy of Sciences
Previous studies on human meta-cognition, represented by confidence in perceptual decisions, often focus on over-simplified environments that yield experiences with limited semantic dimensions. However, in real-life situations such as solving a new problem, people need to make sequential decisions in a complex environment, exploring vast combinations of actions that unfold over time. How do people make meta-cognitive evaluations out of the rich, high-dimensional cognitive experiences in such situations? Here we develop a computational method that models each individual’s meta-cognitive ratings (e.g., difficulty) of problem-solving experience in a visual puzzle game based on information-theoretic metrics derived from their own action sequences. Individuals are assumed to Bayesian update their “thought-space distributions” with their own behavioral distributions on different semantic categories. Our preliminary results show that information discrepancies between beliefs at different moments can predict the individual differences in self-reported difficulty.
Keywords: problem-solving Bayesian inference cognitive effort information theory