Learning Causal Structure through Local Prediction-error Learning

Research on human causal learning has largely focused on strength learning, or on computational-level theories; there are few formal algorithmic models of how people learn causal structure from covariations. We introduce a model that learns causal structure in a local manner via prediction-error learning. This local learning is then integrated dynamically into a unified representation of causal structure. The model uses computationally plausible approximations of (locally) rational learning, and so represents a hybrid between the associationist and rational paradigms in causal learning research. We conclude by showing that the model provides a good fit to data from a previous experiment.