Evidence for the Flexible Sensorimotor Strategies Predicted by Optimal Feedback Control

By Daniel Hsi-wen Liu, E. Todorov. Journal of Neuroscience, 2007

473 citations recorded by the source corpus.

Everyday movements pursue diverse and often conflicting mixtures of task goals, requiring sensorimotor strategies customized for the task at hand. Such customization is mostly ignored by traditional theories emphasizing movement geometry and servo control. In contrast, the relationship between the task and the strategy most suitable for accomplishing it lies at the core of our optimal feedback control theory of coordination. Here, we show that the predicted sensitivity to task goals affords natural explanations to a number of novel psychophysical findings. Our point of departure is the little-known fact that corrections for target perturbations introduced late in a reaching movement are incomplete. We show that this is not simply attributable to lack of time, in contradiction with alternative models and, somewhat paradoxically, in agreement with our model. Analysis of optimal feedback gains reveals that the effect is partly attributable to a previously unknown trade-off between stability and accuracy. This yields a testable prediction: if stability requirements are decreased, then accuracy should increase. We confirm the prediction experimentally in three-dimensional obstacle avoidance and interception tasks in which subjects hit a robotic target with programmable impedance. In additional agreement with the theory, we find that subjects do not rely on rigid control strategies but instead exploit every opportunity for increased performance. The modeling methodology needed to capture this extra flexibility is more general than the linear-quadratic methods we used previously. The results suggest that the remarkable flexibility of motor behavior arises from sensorimotor control laws optimized for composite cost functions.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/6cac2a218904da04a650907dc9fc3a797776a337

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Citations in the corpus

Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2012, background): “This matches with a theory of optimal motor control, stating that task-irrelevant errors or variability coming from sensory – motor noise should be left untreated to maximize performance (Liu & Todorov, 2007; Todorov, 2004; Todorov & Jordan, 2002; Wei & Körding, 2009; Wolpert et al., 2011).”
Noise in the nervous system (2008, background): “This framework has been able to explain quantitative data from human and primate movement”
Structure learning in action (2010, background): “Such parametric control laws can be used to study movement generation within the framework of optimal feedback control, and have been successful in explaining a wide range of human motor behaviour [55–61].”
Action and behavior: a free-energy formulation (2010, background): “Thisisasimplemetaphor for empirical studies of movement trajectories under perturbation (e.g., Paulignan et al. 1991; Fourneret and Jeannerod 1998; Liu and Todorov 2007 ).”
Motor Control is Decision-Making (2012, background): “For example, the theory is able to account for detailed aspects of trajectory generation and the response to perturbations [26] as well as how co-variation of effectors is tuned so as to push variability into task-irrelevant dimensions while preserving accuracy in task-relevant dimensions [27].”
If MaxEnt RL is the Answer, What is the Question? (2019, background): “[29] Dan Liu and Emanuel Todorov.”
Computational mechanisms of sensorimotor control. (2011, background): “Liu and Todorov (2007) investigated another predicted feature of optimal control.”
Skill and motor control: intelligence all the way down (2016, background): “First off, Liu and Todorov (2007) demonstrate ‘‘that corrections for target perturbations introduced late in a reaching movement are incomplete’’ (p.”
Coding and use of tactile signals from the fingertips in object manipulation tasks (2009, result): “Furthermore, the idea that control is centred on distinct movement goals is consistent with a recent computational model of sensorimotor control referred to as optimal feedback contro”
A computational neuroanatomy for motor control (2008, background): “In David Marr’s (1982) terminology, the scientists were working on a computational problem: inquiring about the driving purpose and logic of actions.”