Proceedings of the Annual Meeting of the Cognitive Science Society

Acting effectively in the world requires learning and control- ling dynamic systems, that is, systems involving feedback re- lations among continuous variables that vary in real time. We introduce a novel class of dynamic control environments us- ing Ornstein-Uhlenbeck processes connected in causal Markov graphs that allow us to systematically test people’s ability to learn and control various dynamic systems. We find that per- formance varied across a range of test environments, roughly matching with complexity defined by a set of models trained on the task (an optimal model, a deep Reinforcement Learning agent, and a PID controller). The testbed of dynamic envi- ronments and class of models introduced in this paper lay the groundwork for the systematic study of people’s ability to con- trol complex dynamic systems.