Evaluating computational models of explanation using human judgments
By Michael D. Pacer, Joseph Jay Williams, X. Chen, Tania Lombrozo, T. Griffiths. 2013
30 citations recorded by the source corpus.
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). Across two versions of two causal structures and across both experiments, we find that the Causal Explanation Tree and Most Relevant Explanation models provide better fits to human data than either Most Probable Explanation or Explanation Tree models. We identify strengths and shortcomings of these models and what they can reveal about human explanation. We conclude by suggesting the value of pursuing computational and psychological investigations of explanation in parallel.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Trevor Darrell
- Zeynep Akata
- Ronghang Hu
- Lisa Anne Hendricks
- Bernt Schiele
- Mohit Bansal
- Kate Saenko
- Marcus Rohrbach
Citations in the corpus
- Grounding Visual Explanations (2018, background): “with human subjects on a specific aspect of explanations [18].”