Kurt Thoroughman, Ph.D., Washington University assistant professor of biomedical engineering, and Jordan Taylor, Washington University doctoral student in biomedical engineering, tested a dozen volunteers who played a video game that involved a robotic arm. Thoroughman and Taylor found that the subjects learned different levels of the game in just 20 minutes of training over different environmental difficulties. Human subjects made reaching movements while holding a robotic arm whose perturbing forces changed directions at the same rate, twice as fast, or four times as fast as the direction of movement, therefore exposing subjects to environments of increasing complexity across movement space. Subjects learned all three environments and learned the low and medium complexity environments equally well. They learned the high complexity environment, too, though not as well as the other two.
Thoroughman and Taylor also could detect how individual movements trained people to make the next movement better. Surprisingly, people could very quickly change the way errors in one movement induced a learned response in the next movement. Specifically, subjects lessened their movement-by-movement adaptation and narrowed the spatial extent of generalization to match the environmental complexity, showing that people can rapidly reshape the transformation of sense into motor prediction to best learn a new movement task.
"We've demonstrated that the richness of motor training determines not only what we learn but how we learn," Thoroughman said. "What we cared about most was not only what people learned but how they learned from trial to trial, movement to movement.