Simulating closed- and open-loop voluntary movement: a nonlinear control-systems approach

• Published in: IEEE transactions on biomedical engineering Vol. 49; no. 11; pp. 1242 - 1252
• Main Authors: Davidson, P.R., Jones, R.D., Andreae, J.H., Sirisena, H.R.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation, Biological - physiology; Adaptive control; Biological and medical sciences; Computational modeling; Computer Simulation; Error correction; Feedback; Feedback control; Female; Humans; Inverse problems; Learning - physiology; Male; Medical sciences; Models, Biological; Motor drives; Movement - physiology; Muscle, Skeletal - physiology; Nonlinear Dynamics; Open loop systems; Programmable control; Psychology; Psychomotor Performance - physiology; Reproducibility of Results; Sensitivity and Specificity; Stochastic Processes; Vision, Ocular - physiology; Adaptive inverse control; internal models; motor learning; motor control modeling; tracking task
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2002.804601

In many recent human motor control models, including feedback-error learning and adaptive model theory (AMT), feedback control is used to correct errors while an inverse model is simultaneously tuned to provide accurate feedforward control. This popular and appealing hypothesis, based on a combination of psychophysical observations and engineering considerations, predicts that once the tuning of the inverse model is complete the role of feedback control is limited to the correction of disturbances. This hypothesis was tested by looking at the open-loop behavior of the human motor system during adaptation. An experiment was carried out involving 20 normal adult subjects who learned a novel visuomotor relationship on a pursuit tracking task with a steering wheel for input. During learning, the response cursor was periodically blanked, removing all feedback about the external system (i.e., about the relationship between hand motion and response cursor motion). Open-loop behavior was not consistent with a progressive transfer from closed to open-loop control. Our recently developed computational model of the brain-a novel nonlinear implementation of AMT-was able to reproduce the observed closed- and open-loop results. In contrast, other control-systems models exhibited only minimal feedback control following adaptation, leading to incorrect open-loop behavior. This is because our model continues to use feedback to control slow movements after adaptation is complete. This behavior enhances the internal stability of the inverse model. In summary, our computational model is currently the only motor control model able to accurately simulate the closed- and open-loop characteristics of the experimental response trajectories.