This preprint investigates the predictability of trial-by-trial human rule-learning behavior using the Badham et al. (2017) dataset. We compare cognitive and data-driven models, including Win-Stay-Lose-Shift, Q-learning, LSTM, and XGBoost, under leave-one-participant-out cross-validation. Results show that episodic resetting of behavioral-history features provides the largest performance gain, and that model performance approaches a practical predictability plateau near AUC = 0.676. Behavioral clustering further decomposes population-level predictability into four learner profiles.
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Dr. Alaa Ba Hamid
Osamah H. Alghamdi
Sabeeh M. A. Rahman
King Fahd University of Petroleum and Minerals
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Hamid et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69fd8021bfa21ec5bbf0887e — DOI: https://doi.org/10.5281/zenodo.20048975