Dissociating Variability and Effort as Determinants of Coordination

When coordinating movements, the nervous system often has to decide how to distribute work across a number of redundant effectors. Here, we show that humans solve this problem by trying to minimize both the variability of motor output and the effort involved. In previous studies that investigated the temporal shape of movements, these two selective pressures, despite having very different theoretical implications, could not be distinguished; because noise in the motor system increases with the motor commands, minimization of effort or variability leads to very similar predictions. When multiple effectors with different noise and effort characteristics have to be combined, however, these two cost terms can be dissociated. Here, we measure the importance of variability and effort in coordination by studying how humans share force production between two fingers. To capture variability, we identified the coefficient of variation of the index and little fingers. For effort, we used the sum of squared forces and the sum of squared forces normalized by the maximum strength of each effector. These terms were then used to predict the optimal force distribution for a task in which participants had to produce a target total force of 4–16 N, by pressing onto two isometric transducers using different combinations of fingers. By comparing the predicted distribution across fingers to the actual distribution chosen by participants, we were able to estimate the relative importance of variability and effort of 1∶7, with the unnormalized effort being most important. Our results indicate that the nervous system uses multi-effector redundancy to minimize both the variability of the produced output and effort, although effort costs clearly outweighed variability costs.

[1]  Etienne Burdet,et al.  Quantization of human motions and learning of accurate movements , 1998, Biological Cybernetics.

[2]  D. A. Shreeve,et al.  An evaluation of optimization techniques for the prediction of muscle activation patterns during isometric tasks. , 1996, Journal of biomechanical engineering.

[3]  Ashvin Shah,et al.  A computational model of muscle recruitment for wrist movements. , 2002, Journal of neurophysiology.

[4]  D. Hoffman,et al.  Step-tracking movements of the wrist. IV. Muscle activity associated with movements in different directions. , 1999, Journal of neurophysiology.

[5]  Rieko Osu,et al.  The central nervous system stabilizes unstable dynamics by learning optimal impedance , 2001, Nature.

[6]  Konrad Paul Kording,et al.  Decision Theory: What "Should" the Nervous System Do? , 2007, Science.

[7]  K M Newell,et al.  Variability and Noise in Continuous Force Production , 2000, Journal of motor behavior.

[8]  J. Krakauer,et al.  Why Don't We Move Faster? Parkinson's Disease, Movement Vigor, and Implicit Motivation , 2007, The Journal of Neuroscience.

[9]  D. Domkin,et al.  Structure of joint variability in bimanual pointing tasks , 2002, Experimental Brain Research.

[10]  Rajesh P. N. Rao,et al.  Bayesian brain : probabilistic approaches to neural coding , 2006 .

[11]  Emmanuel Guigon,et al.  Computational Motor Control : Redundancy and Invariance , 2007 .

[12]  R. Zaremba,et al.  ATP utilization for calcium uptake and force production in different types of human skeletal muscle fibres , 2001, The Journal of physiology.

[13]  J. Diedrichsen Optimal Task-Dependent Changes of Bimanual Feedback Control and Adaptation , 2007, Current Biology.

[14]  B. Ripley,et al.  Robust Statistics , 2018, Wiley Series in Probability and Statistics.

[15]  Emanuel Todorov,et al.  Optimal Control Theory , 2006 .

[16]  Daniel M. Wolpert,et al.  Signal-dependent noise determines motor planning , 1998, Nature.

[17]  Emanuel Todorov,et al.  Stochastic Optimal Control and Estimation Methods Adapted to the Noise Characteristics of the Sensorimotor System , 2005, Neural Computation.

[18]  Michael I. Jordan,et al.  Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.

[19]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[20]  N. A. Bernshteĭn The co-ordination and regulation of movements , 1967 .

[21]  R. C. Oldfield The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[22]  Peter J. Huber,et al.  Robust Statistics , 2005, Wiley Series in Probability and Statistics.

[23]  P. Morasso Spatial control of arm movements , 2004, Experimental Brain Research.