Causal relations from kinematic simulations

Abstract

Reasoners distinguish between different types of causal relations, such as causes, enabling conditions, and preventions. Psychologists disagree about the representations that give rise to the different relations, but agree that mental simulations are fundamental in inferring them. We explore how causal relations are extracted from mental simulations. The theory of mental models posits that people use a kinematic simulation to infer possibilities. It predicts that causes should be easier to infer than enabling conditions, and that the latency to infer a causal relation should correlate with the number of moves in a simulation. To test these two predictions, we adapted a railway domain designed to elicit mental simulations, and we devised problems in which reasoners had to infer causal relations from simulations of movements of cars in this domain. Two studies corroborated the theory's predictions. We discuss the results in light of recent theories of causation and mental simulation.


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