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Optimality principles in sensorimotor control

Nature Neuroscience volume 7, pages 907–915 (2004)Cite this article

Abstract

The sensorimotor system is a product of evolution, development, learning and adaptation—which work on different time scales to improve behavioral performance. Consequently, many theories of motor function are based on 'optimal performance': they quantify task goals as cost functions, and apply the sophisticated tools of optimal control theory to obtain detailed behavioral predictions. The resulting models, although not without limitations, have explained more empirical phenomena than any other class. Traditional emphasis has been on optimizing desired movement trajectories while ignoring sensory feedback. Recent work has redefined optimality in terms of feedback control laws, and focused on the mechanisms that generate behavior online. This approach has allowed researchers to fit previously unrelated concepts and observations into what may become a unified theoretical framework for interpreting motor function. At the heart of the framework is the relationship between high-level goals, and the real-time sensorimotor control strategies most suitable for accomplishing those goals.

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Figure 1: Schematic illustration of open- and closed-loop optimization.
Figure 2: Minimal intervention principle.
Figure 3: Application of optimal feedback control to a redundant stochastic system.

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Acknowledgements

We thank G. Loeb and J. Triesch for their comments on the manuscript. This work was supported by US National Institutes of Health grant R01-NS045915.

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  1. Department of Cognitive Science, University of California San Diego, La Jolla, 92093-0515, California, USA

    Emanuel Todorov

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Todorov, E. Optimality principles in sensorimotor control. Nat Neurosci 7, 907–915 (2004). https://doi.org/10.1038/nn1309

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