ConceptBuilder: an open-source software tool for measuring, depicting, and quantifying causal models

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.

The role of causal beliefs in political identity and voting

An emerging literature in psychology and political science has identified political identity as an important driver of political decisions. However, less is known about how a person's political identity is incorporated into their broader self-concept and why it influences some people more than others. We examined the role of political identity in representations of the self-concept as one determinant of people's political behaviors. We tested the predictions of a recent theoretical account of self-concept representation that, inspired by work on conceptual representation, emphasizes the role of causal beliefs. This account predicts that people who believe that their political identity is causally central (linked to many other features of the self-concept) will be more likely to engage in behaviors consistent with their political identity than those who believe that the same aspect is causally peripheral (linked to fewer other features). Consistent with these predictions, in a study run when political identity was particularly salient-during the 2016 U.S. Presidential election-we found that U.S. voters who believed their political party identity was more causally central (vs. those who believe it was causally peripheral) were more likely to vote for their political party's candidate. Further, in two studies, we found that U.K. residents who believed that their English or British national identity was more causally central were more likely to support the U.K. leaving the European Union (Brexit) than those who believed the same identities were more causally peripheral.

Visual causal models enhance clinical explanations of treatments for generalized anxiety disorder

A daily challenge in clinical practice is to adequately explain disorders and treatments to patients of varying levels of literacy in a time-limited situation. Drawing jointly upon research on causal reasoning and multimodal theory, the authors asked whether adding visual causal models to clinical explanations promotes patient learning. Participants were 86 people currently or formerly diagnosed with a mood disorder and 104 lay people in Boston, Massachusetts, USA, who were randomly assigned to receive either a visual causal model (dual-mode) presentation or auditory-only presentation of an explanation about generalized anxiety disorder and its treatment. Participants' knowledge was tested before, immediately after, and 4 weeks after the presentation. Patients and lay people learned significantly more from visual causal model presentations than from auditory-only presentations, and visual causal models were perceived to be helpful. Participants retained some information 4 weeks after the presentation, although the advantage of visual causal models did not persist in the long term. In conclusion, dual-mode presentations featuring visual causal models yield significant relative gains in patient comprehension immediately after the clinical session, at a time when the authors suggest that patients may be most willing to begin the recommended treatment plan.

The conceptual centrality of causal cycles

How do causal cycles affect judgments of conceptual centrality? Generally, a feature is central to a concept to the extent that other features in the concept depend on it, thereby rendering it immutable from the concept (Sloman, Love, & Ahn, 1998). Previous research on conceptual centrality has focused primarily on features involved in four major types of dependency structures: simple cause-effect relations, causal chains, common-cause structures, and common-effect structures. Causal cycles are a fifth type of dependency structure commonly reported in people's real-life concepts, yet to date, they have been relatively ignored in research on conceptual centrality. The results of six experiments suggest that previously held assumptions about the conceptual representation of cycles are incorrect. We discuss the implications of these findings for our understanding of theory-based concepts.