In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to…

Dealing with alternative causes is necessary to avoid making inaccurate causal inferences from covariation data. However, information about alternative causes is frequently unavailable, rendering them unobserved. The current article reviews the way in which current learning models deal, or could deal, with unobserved causes. A new model of causal…

Studied a way of constraining feature centrality, the causal status of features in a category through 6 experiments involving 204 undergraduate students. Results show that the causal status effect is deeply rooted in many aspects of the categorization process. Causal knowledge is shown to affect categorization by determining feature centrality.…

D. Hume (1739/1987) argued that causality is not observable. P. W. Cheng claimed to present "a theoretical solution to the problem of causal induction first posed by Hume more than two and a half centuries ago" (p. 398) in the form of the power PC theory (L. R. Novick & P. W. Cheng). This theory claims that people's goal in causal induction is to…