Near-optimal human adaptive control across different noise environments.

A person learning to control a complex system needs to learn about both the dynamics and the noise of the system. We evaluated human subjects' abilities to learn to control a stochastic dynamic system under different noise conditions. These conditions were created by corrupting the forces applied to the system with noise whose magnitudes were either proportional or inversely proportional to the sizes of subjects' control signals. We also used dynamic programming to calculate the mathematically optimal control laws of an "ideal actor" for each noise condition. The results suggest that people learned control strategies tailored to the specific noise characteristics of their training conditions. In particular, as predicted by the ideal actors, they learned to use smaller control signals when forces were corrupted by proportional noise and to use larger signals when forces were corrupted by inversely proportional noise, thereby achieving levels of performance near the information-theoretic upper bounds. We conclude that subjects learned to behave in a near-optimal manner, meaning that they learned to efficiently use all available information to plan and execute control policies that maximized performances on their tasks.