Explanatory completeness

Explaining contentious political issues promotes open-minded thinking

Cognitive scientists suggest that inviting people to explain contentious political issues might reduce intergroup toxicity because it exposes people to how poorly they understand the issue. However, whether providing explanations can result in more open-minded political thinking remains unclear. On one hand, inviting people to explain a political issue might make them more impartial and open-minded in their thinking. On the other hand, an invitation to explain a contentious political issue might lead to myside bias-rationalization of one's default position. Here, we address these contrasting predictions in five experiments (N = 1884; three pre-registered), conducted across a variety of contexts: with graduate students interacting with an actor in a laboratory setting, with US residents at the peak of the 2012 and 2016 U.S. presidential elections, with UK residents before the highly polarized 2019 Brexit vote, and with gun-control partisans. Across studies, we found that explaining politically contentious topics resulted in more open-minded thinking, an effect that generalized across coded (Studies 1-4) and self-report (Studies 3-4) measures. We also observed that participants who were made to feel like their explanations were welcomed felt closer to their discussion partner (Studies 3-4), an effect that generalized to all outgroup members with whom they disagreed with about the politically contentious issue (Study 4). We discuss the theoretical implications of these findings, and the potential for explanations to foster open-minded political engagement.

Less is more: information needs, information wants, and what makes causal models useful

Each day people make decisions about complex topics such as health and personal finances. Causal models of these domains have been created to aid decisions, but the resulting models are often complex and it is not known whether people can use them successfully. We investigate the trade-off between simplicity and complexity in decision making, testing diagrams tailored to target choices (Experiments 1 and 2), and with relevant causal paths highlighted (Experiment 3), finding that simplicity or directing attention to simple causal paths leads to better decisions. We test the boundaries of this effect (Experiment 4), finding that including a small amount of information beyond that related to the target answer has a detrimental effect. Finally, we examine whether people know what information they need (Experiment 5). We find that simple, targeted, information still leads to the best decisions, while participants who believe they do not need information or seek out the most complex information performed worse.

Motivated to learn: An account of explanatory satisfaction

Many explanations have a distinctive, positive phenomenology: receiving or generating these explanations feels satisfying. Accordingly, we might expect this feeling of explanatory satisfaction to reinforce and motivate inquiry. Across five studies, we investigate how explanatory satisfaction plays this role: by motivating and reinforcing inquiry quite generally ("brute motivation" account), or by selectively guiding inquiry to support useful learning about the target of explanation ("aligned motivation" account). In Studies 1-2, we find that satisfaction with an explanation is related to several measures of perceived useful learning, and that greater satisfaction in turn predicts stronger curiosity about questions related to the explanation. However, in Studies 2-4, we find only tenuous evidence that satisfaction is related to actual learning, measured objectively through multiple-choice or free recall tests. In Study 4, we additionally show that perceptions of learning fully explain one seemingly specious feature of explanatory preferences studied in prior research: the preference for uninformative "reductive" explanations. Finally, in Study 5, we find that perceived learning is (at least in part) causally responsible for feelings of satisfaction. Together, these results point to what we call the "imperfectly aligned motivation" account: explanatory satisfaction selectively motivates inquiry towards learning explanatory information, but primarily through fallible perceptions of learning. Thus, satisfaction is likely to guide individuals towards lines of inquiry that support perceptions of learning, whether or not individuals actually are learning.

Can conditionals explain explanations? A modus ponens model of B because A

We suggest a normative model for the evaluation of explanations B because A based on probabilistic conditional reasoning and compare it with empirical data. According to the modus ponens model of explanations, the probability of B because A should equal the joint probability of the conditional if A then B and the explanans A. We argue that B because A expresses the conjunction of A and B as well as positive relevance of A for B. In Study 1, participants (N = 80) judged the subjective probabilities of 20 sets of statements with a focus on belief-based reasoning under uncertainty. In Study 2, participants (N = 376) were assigned to one of six item sets for which we varied the inferential relevance of A for B to explore boundary conditions of our model. We assessed the performance of our model across a range of analyses and report results on the Equation, a fundamental model in research on probabilistic reasoning concerning the evaluation of conditionals. In both studies, results indicate that participants' belief in statements B because A followed model predictions systematically. However, a sizeable proportion of sets of beliefs contained at least one incoherence, indicating deviations from the norms of rationality suggested by our model. In addition, results of Study 2 lend support to the idea that inferential relevance may be relevant for the evaluation of both conditionals and explanations.