Why do some explanations strike people as highly satisfying while others, seemingly equally accurate, strike them as less appealing? We analyze thousands of open-ended explanations generated by lay-people in response to 'Why?' questions spanning multiple domains, to discover (1) what kinds of features are associated with ratings of explanation quality; (2) whether people can tell how good their explanations are; and (3) which cognitive traits predict the ability to generate good explanations. Our results support a pluralistic view of explanation, where satisfaction is best predicted by either functional or mechanistic content. Respondents were better able to judge how accurate their explanations were than how satisfying they were. Insight problem solving ability was the cognitive ability most strongly associated with the generation of satisfying explanations.
According to continuum models of psychosis, cognitive biases contribute to delusional ideation in the general population. In a large (N = 1002) pre-registered general population study, we examine key specific predictions of such models; in particular, the hypotheses that delusional ideation in the general population is predicted by the Jumping to Conclusions bias (JTC), Over-adjustment, the Bias Against Disconfirm-ing Evidence (BADE), and the Liberal Acceptance bias (LA). Crucially, we include explicit indices of data quality, and incorporate a new, animated Beads Task which overcomes known problems with this instrument. Our results initially appear to replicate several classic findings concerning the relationships between delusional ideation and the aforementioned cognitive biases: Delusional ideation predicted JTC, overadjustment, and BADE. Importantly, however, we demonstrate that many of these classic findings are either severely diminished — or disappear entirely — when inattentive participants are removed from the analyses. These findings highlight crucial issues that need to be addressed to rigorously test continuum models of psychosis.
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