Year: 2014
Type: article
Abstract: People judge the strength of cause-and-effect relationships as a matter of routine, and often do so in the absence of evidence about the covariation between cause and effect.In the present study, we examine the possibility that explanatory power is used in making these judgments.To intervene on explanatory power without changing the target causal relation, we manipulated explanatory scope-the number of effects predicted by an explanation-in two different ways, finding downstream consequences of these manipulations on causal strength judgments (Experiment 1).Directly measuring perceived explanatory power for the same items also revealed item-by-item correlations between causal strength and explanatory power (Experiment 2).These results suggest that explanatory power may be a useful heuristic for estimating causal strength in the absence of statistical evidence.
Institutions: Yale University, Wesleyan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2403951411