A Constraint Satisfaction Model of Causal Learning and Reasoning

Following up on previous work by Thagard (1989, 2000) we have developed a connectionist constraint satisfaction model which aims at capturing a wide variety of tasks involving causal cognitions, including causal reasoning, learning, hypothesis testing, and prediction. We will show that this model predicts a number of recent findings, including asymmetries of blocking, and asymmetries of sensitivity to structural implications of causal models in explicit versus implicit tasks.