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Marchant, Nicolás; Quillien, Tadeg; Chaigneau, Sergio E. – Cognitive Science, 2023
The causal view of categories assumes that categories are represented by features and their causal relations. To study the effect of causal knowledge on categorization, researchers have used Bayesian causal models. Within that framework, categorization may be viewed as dependent on a likelihood computation (i.e., the likelihood of an exemplar with…
Descriptors: Classification, Bayesian Statistics, Causal Models, Evaluation Methods
Mayrhofer, Ralf; Waldmann, Michael R. – Cognitive Science, 2016
Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when…
Descriptors: Causal Models, Bayesian Statistics, Inferences, Probability
Rehder, Bob – Cognitive Science, 2017
This article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models (CGMs) have enjoyed success modeling category-based judgments as well as a number of other cognitive phenomena, CGMs are only able to represent causal structures that are acyclic. A number of new…
Descriptors: Abstract Reasoning, Logical Thinking, Causal Models, Graphs
Lu, Hongjing; Rojas, Randall R.; Beckers, Tom; Yuille, Alan L. – Cognitive Science, 2016
Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about…
Descriptors: Learning Processes, Causal Models, Sequential Learning, Abstract Reasoning
Weisberg, Deena S.; Gopnik, Alison – Cognitive Science, 2013
Young children spend a large portion of their time pretending about non-real situations. Why? We answer this question by using the framework of Bayesian causal models to argue that pretending and counterfactual reasoning engage the same component cognitive abilities: disengaging with current reality, making inferences about an alternative…
Descriptors: Causal Models, Bayesian Statistics, Young Children, Imagination
Bes, Benedicte; Sloman, Steven; Lucas, Christopher G.; Raufaste, Eric – Cognitive Science, 2012
The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more…
Descriptors: Statistical Inference, Probability, Correlation, Causal Models