Spontaneous Causal Learning While Controlling A Dynamic System

Abstract

When dealing with a dynamic causal system people may employ a variety of different strategies. One of these strategies is causal learning, that is, learning about the causal structure and parameters of the system acted upon. In two experiments we examined whether people spontaneously induce a causal model when learning to control the state of an outcome value in a dynamic causal system. After the control task, we modified the causal structure of the environment and assessed decision makers’ sensitivity to this manipulation. While purely instrumental knowledge does not support inferences given the new modified structure, causal knowledge does. The results showed that most participants learned the structure of the underlying causal system. However, participants acquired surprisingly little knowledge of the system’s parameters when the causal processes that governed the system were not perceptually separated (Experiment 1). Knowledge improved considerably once processes were separated and feedback was made more transparent (Experiment 2). These findings indicate that even without instruction, causal learning is a favored strategy for interacting with and controlling a dynamic causal system.