Optimal Control Predicts Human Performance on Objects with Internal Degrees of Freedom

• Published in: PLoS computational biology Vol. 5; no. 6; p. e1000419
• Main Authors: Nagengast, Arne J., Braun, Daniel A., Wolpert, Daniel M.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.06.2009; Public Library of Science (PLoS)
• Subjects: Accuracy; Adult; Algorithms; Arm - physiology; Behavior; Computational biology; Computational Biology/Computational Neuroscience; Computer Science/Systems and Control Theory; Computer Simulation; Control theory; Experiments; Feedback - physiology; Female; Humans; Linear Models; Male; Mathematical models; Models, Biological; Motor ability; Motor Skills - physiology; Neuroscience/Motor Systems; Neuroscience/Theoretical Neuroscience; Nonlinear Dynamics; Operations research; Physiological aspects; Robotics; Sensitivity analysis; Statistical models; Studies; Task Performance and Analysis; User-Computer Interface; Germany; User-Computer Interface; Humans; Linear Models; Male; Robotics; Algorithms; Feedback; Models, Biological; Computer Simulation; Adult; Female; Nonlinear Dynamics; Arm; Motor Skills; Task Performance & Analysis
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1000419

On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.