Drawing conclusions: Representing and evaluating competing explanations

Despite the increase in studies investigating people's explanatory preferences in the domains of psychology and philosophy, little is known about their preferences in more applied domains, such as the criminal justice system. We show that when people evaluate competing legal accounts of the same evidence, their explanatory preferences are affected by whether they are required to draw causal models of the evidence. In addition, we identify ‘mechanism’ as an explanatory feature that people value when evaluating explanations. Although previous research has shown that people can reason correctly about causality, ours is one of the first studies to show that generating and drawing causal models directly affects people's evaluations of explanations. Our findings have implications for the development of normative models of legal arguments, which have so far adopted a singularly ‘unified’ approach, as well as the development of modelling tools to support people's reasoning and decision-making in applied domains. Finally, they add to the literature on the cognitive basis of evaluating competing explanations in new domains. •We assess how people structurally represent competing explanations in a legal context.•We assess how competing explanations are evaluated and compared.•We find that drawing causal models shifts people's explanatory preferences (towards simplicity).•This effect holds regardless of which adversarial side puts forth the simple explanation.•We hypothesise that drawing causal models facilitates probabilistic reasoning.