People often face the challenge of evaluating competing explanations. One approach is to assess the explanations’ relative probabilities – e.g., applying Bayesian inference to compute their posterior probabilities. Another approach is to consider an explanation’s qualities or ‘virtues’, such as its relative simplicity (i.e., the number of unexplained causes it invokes). The current work investigates how these two approaches are related. Study 1 found that simplicity is used to infer the inputs to Bayesian inference (explanations’ priors and likelihoods). Studies 1 and 2 found that simplicity is also used as a direct cue to the outputs of Bayesian inference (the posterior probability of an explanation), such that simplicity affects estimates of posterior probability even after controlling for elicited (Study 1) or provided (Study 2) priors and likelihoods, with simplicity having a larger effect in Study 1, where posteriors are more uncertain and difficult to compute. Comparing Studies 1 and 2 also suggested that simplicity plays additional roles unrelated to approximating probabilities, as reflected in simplicity’s effect on how ‘satisfying’ (vs. probable) an explanation is, which remained largely unaffected by the difficulty of computing posteriors. Together, these results suggest that the virtue of simplicity is used in multiple ways to approximate probabilities (i.e., serving as a cue to priors, likelihoods, and posteriors) when these probabilities are otherwise uncertain or difficult to compute, but that the influence of simplicity also goes beyond these roles.
Identifying abstract relations is essential for commonsense reasoning. Research suggests that even young children can infer relations such as “same” and “different,” but often fail to apply these concepts. Might the process of explaining facilitate the recognition and application of relational concepts? Based on prior work suggesting that explanation can be a powerful tool to promote abstract reasoning, we predicted that children would be more likely to discover and use an abstract relational rule when they were prompted to explain observations instantiating that rule, compared to when they received demonstration alone. Five- and 6-year-olds were given a modified Relational Match to Sample (RMTS) task, with repeated demonstrations of relational (same) matches by an adult. Half of the children were prompted to explain these matches; the other half reported the match they observed. Children who were prompted to explain showed immediate, stable success, while those only asked to report the outcome of the pedagogical demonstration did not. Findings provide evidence that explanation facilitates early abstraction over and above demonstration alone.
When faced with a dilemma between believing what is supported by an impartial assessment of the evidence (e.g., that one's friend is guilty of a crime) and believing what would better fulfill a moral obligation (e.g., that the friend is innocent), people often believe in line with the latter. But is this how people think beliefs ought to be formed? We addressed this question across three studies and found that, across a diverse set of everyday situations, people treat moral considerations as legitimate grounds for believing propositions that are unsupported by objective, evidence-based reasoning. We further document two ways in which moral considerations affect how people evaluate others' beliefs. First, the moral value of a belief affects the evidential threshold required to believe, such that morally beneficial beliefs demand less evidence than morally risky beliefs. Second, people sometimes treat the moral value of a belief as an independent justification for belief, and on that basis, sometimes prescribe evidentially poor beliefs to others. Together these results show that, in the folk ethics of belief, morality can justify and demand motivated reasoning.