title: Representing causation.
abstract: The dynamics model, which is based on L. Talmy's (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and explains the induction of causal relationships from single observations. Support for the model is provided in experiments in which participants categorized 3-D animations of realistically rendered objects with trajectories that were wholly determined by the force vectors entered into a physics simulator. Experiments 1-3 showed that causal judgments are based on several forces, not just one. Experiment 4 demonstrated that people compute the resultant of forces using a qualitative decision rule. Experiments 5 and 6 showed that a dynamics approach extends to the representation of social causation. Implications for the relationship between causation and time are discussed.
authors.affiliation: Department of Psychology, Emory University, Atlanta, GA 30322, USA. pwolff@emory.edu
authors.forename: Phillip
authors.initials: P
authors.lastname: Wolff
mesh_terms: D001499:Bayes Theorem; D003071:Cognition; D006801:Humans; D008960:Models, Psychological; D011582:Psychological Theory; D014796:Visual Perception