Evaluating computational models of explanation using human judgments

TL;DR

This study compares four computational models of explanation in Bayesian networks by assessing their alignment with human judgments through two experiments, highlighting the superior performance of the Causal Explanation Tree and Most Relevant Explanation models.

Contribution

It provides an empirical evaluation of explanation models against human judgments, identifying which models better capture human explanation preferences.

Findings

Causal Explanation Tree and Most Relevant Explanation models fit human data better.

Models show strengths and limitations in explaining human reasoning.

Parallel computational and psychological investigations are valuable.

Abstract

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.