& Explanations of psychological phenomena seem to generate more public interest when they contain neuroscientific information. Even irrelevant neuroscience information in an explanation of a psychological phenomenon may interfere with people’s abilities to critically consider the underlying logic of this explanation. We tested this hypothesis by giving naïve adults, students in a neuroscience course, and neuroscience experts brief descriptions of psychological phenomena followed by one of four types of explanation, according to a 2 (good explanation vs. bad explanation) 2 (without neuroscience vs. with neuroscience) design. Crucially, the neuroscience information was irrelevant to the logic of the explanation, as confirmed by the expert subjects. Subjects in all three groups judged good explanations as more satisfying than bad ones. But subjects in the two nonexpert groups additionally judged that explanations with logically irrelevant neuroscience information were more satisfying than explanations without. The neuroscience information had a particularly striking effect on nonexperts ’ judgments of bad explanations, masking otherwise salient problems in these explanations. &

This paper explores the role of causal explanations in evaluating counterfactual conditionals. In reasoning about what would have been the case if A had been true, the localist injunction to hold constant all the variables that causally influence whether A is true or not, is sometimes unreasonably constraining. We hypothesize that speakers may resolve this tension by including in their deliberations the question of what would explain the hypothesized truth of A. To account for our recent psychological findings about counterfactuals, an alternative approach based on Causal Bayesian networks is proposed in which the intervention operator utilizes the agent’s beliefs about the explanatory power of the antecedent of the counterfactual. The results of three psychological experiments are reported in which the new method succeeds in predicting subjects ’ responses while the traditional method for evaluating counterfactuals in Bayesian networks fails.

studies of concept learning in adults address the learning of novel concepts, but much of learning involves the updating and restructuring of familiar concepts. Research on conceptual change explores this issue directly but differs greatly from the formal approach of the adult learning studies. This paper bridges these two areas to advance our knowledge of the mechanisms underlying concept restructuring. The main idea behind this approach is that concepts are built on causal-explanatory knowledge, and hence, models of causal induction may help to clarify the mechanisms of the restructuring process. A new paradigm is presented to study the learning and revising of causal networks. Experiments 1 and 2 showed that learners’ prior beliefs about the causal relations in a domain affected their hypotheses as they began to infer the correct causes. First, when the prior learning suggested evidence against some of the incorrect causes, this helped learners to focus on the correct causes later in learning. Second, the prior causal beliefs were difficult to give up, and they biased learners away from the correct causes that competed to explain the same effects. Experiment 3 showed that learning by intervention, as opposed to observation, affected the concept restructuring process in different ways, depending on what interventions were chosen and by whom. People choosing their own