Persuasive Features of Scientific Explanations: Explanatory Schemata of Physical and Psychosocial Phenomena

Explanations are central to understanding the causal relationships between entities within the environment. Instead of examining basic heuristics and schemata that inform the acceptance or rejection of scientific explanations, recent studies have predominantly examined complex explanatory models. In the present study, we examined which essential features of explanatory schemata can account for phenomena that are attributed to domain-specific knowledge. In two experiments, participants judged the validity of logical syllogisms and reported confidence in their response. In addition to validity of the explanations, we manipulated whether scientists or people explained an animate or inanimate phenomenon using mechanistic (e.g., force, cause) or intentional explanatory terms (e.g., believes, wants). Results indicate that intentional explanations were generally considered to be less valid than mechanistic explanations and that ‘scientists’ were relatively more reliable sources of information of inanimate phenomena whereas ‘people’ were relatively more reliable sources of information of animate phenomena. Moreover, after controlling for participants’ performance, we found that they expressed greater overconfidence for valid intentional and invalid mechanistic explanations suggesting that the effect of belief-bias is greater in these conditions.

Biased belief updating in causal reasoning about COVID-19

In three experiments based on 977 participants, we investigated whether people would show belief bias by letting their prior beliefs on politically charged topics unduly influence their reasoning when updating beliefs based on evidence. Participants saw data from fictional studies and made judgments of how strongly COVID-19 mitigation measures influenced the number of COVID-19 cases (political problems) or a medicine influenced number of headaches (neutral problems). We predicted that liberals would overestimate and conservatives would underestimate causal strength on political problems relative to neutral problems. In Experiments 1 and 2, liberals showed this overestimation bias. Surprisingly, college-student conservatives in Experiment 2 showed the same overestimation as liberals. These findings made sense because all three groups who overestimated the strength of mitigation measures held prior beliefs that strongly favored use of these measures. In Experiment 3, conservatives’ judgments of the strength of mitigation measures after seeing evidence increased as their degree of prior support for these measures increased. Furthermore, conservatives who strongly opposed the use of mitigation measures underestimated causal strength in the political problems. These results suggest that belief bias is driven more by specific beliefs relevant to the reasoning context than to general attitudinal factors like political ideology.

Attitude to ambiguity as a predictor of analytic thinking

Thinking dispositions are considered important predictors of analytic thinking. While several thinking dispositions have been found to predict responses on a range of analytic thinking tasks, this field is arguably underdeveloped. There are likely many relevant dispositional variables associated with analytic thinking that remains to be explored. This study examines one such dispositional variable, namely, attitude to ambiguity. The disposition is implied in the literature given that internal conflict – likely with associated ambiguity – is typically experienced in cognitive tasks used to study thinking and reasoning. In this article, the association between attitude to ambiguity and analytic thinking is empirically examined using Bayesian methods. A total of 313 adults (mean age = 29.31, SD = 12.19) completed the Multidimensional Attitude Toward Ambiguity (MAAS) scale, along with the Cognitive Reflection Test and a syllogism-based measure of belief bias. Results found one component of the MAAS scale, Moral Absolutism, to be a robust predictor of scores on both the Cognitive Reflection Test and the measure of belief bias.