Simplicity and Goodness-of-Fit in Explanation: The Case of Intuitive Curve-Fitting

Other things being equal, people prefer simpler explanations to more complex ones. However, complex explanations often provide better fits to the observed data, and goodness-of-fit must therefore be traded off against simplicity to arrive at the most likely explanation. In three experiments, we examine how people negotiate this tradeoff. As a case study, we investigate laypeople’s intuitions about curve-fitting in visually presented graphs, a domain with established quantitative criteria for trading off simplicity and goodness-of-fit. We examine whether people are well-calibrated to normative criteria, or whether they instead have an underfitting or overfitting bias (Experiment 1), we test people’s intuitions in cases where simplicity and goodness-of-fit are no longer inversely correlated (Experiment 2), and we directly measure judgments concerning the complexity and goodness-of-fit in a set of curves (Experiment 3). To explain these findings, we posit a new heuristic: That the complexity of an explanation is used to estimate its goodness-of-fit to the data.