Evaluating computational models of explanation using human judgments

26 Sep 2013Michael PacerJoseph WilliamsXi ChenTania LombrozoThomas Griffiths

We evaluate four computational models of explanation in Bayesian networks by comparing model predictions to human judgments. In two experiments, we present human participants with causal structures for which the models make divergent predictions and either solicit the best explanation for an observed event (Experiment 1) or have participants rate provided explanations for an observed event (Experiment 2)... (read more)

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