Feedback Gains modulate with Motor Memory Uncertainty

A sudden change in dynamics produces large errors leading to increases in muscle co-contraction and feedback gains during early adaptation. We previously proposed that internal model uncertainty drives these changes, whereby the sensorimotor system reacts to the change in dynamics by upregulating stiffness and feedback gains to reduce the effect of model errors. However, these feedback gain increases have also been suggested to represent part of the adaptation mechanism. Here, we investigate this by examining changes in visuomotor feedback gains during gradual or abrupt force field adaptation. Participants grasped a robotic manipulandum and reached while a curl force field was introduced gradually or abruptly. Abrupt introduction of dynamics elicited large initial increases in kinematic error, muscle co-contraction and visuomotor feedback gains, while gradual introduction showed little initial change in these measures despite evidence of adaptation. After adaptation had plateaued, there was a change in the co-contraction and visuomotor feedback gains relative to null field movements, but no differences (apart from the final muscle activation pattern) between the abrupt and gradual introduction of dynamics. This suggests that the initial increase in feedback gains is not part of the adaptation process, but instead an automatic reactive response to internal model uncertainty. In contrast, the final level of feedback gains is a predictive tuning of the feedback gains to the external dynamics as part of the internal model adaptation. Together, the reactive and predictive feedback gains explain the wide variety of previous experimental results of feedback changes during adaptation.

[1]  Stephen H Scott,et al.  Visual Feedback Processing of the Limb Involves Two Distinct Phases , 2019, The Journal of Neuroscience.

[2]  Mark J Wagner,et al.  Shared Internal Models for Feedforward and Feedback Control , 2008, The Journal of Neuroscience.

[3]  Daichi Nozaki,et al.  Divisively Normalized Integration of Multisensory Error Information Develops Motor Memories Specific to Vision and Proprioception , 2019, The Journal of Neuroscience.

[4]  G. Torres-Oviedo,et al.  Natural error patterns enable transfer of motor learning to novel contexts. , 2012, Journal of neurophysiology.

[5]  M. Kawato,et al.  A hierarchical neural-network model for control and learning of voluntary movement , 2004, Biological Cybernetics.

[6]  Sarah E. Criscimagna-Hemminger,et al.  Contributions of the motor cortex to adaptive control of reaching depend on the perturbation schedule. , 2011, Cerebral cortex.

[7]  J. Vercher,et al.  Target and hand position information in the online control of goal-directed arm movements , 2003, Experimental Brain Research.

[8]  Ferdinando A. Mussa-Ivaldi,et al.  Robot-assisted adaptive training: custom force fields for teaching movement patterns , 2004, IEEE Transactions on Biomedical Engineering.

[9]  Helen J. Huang,et al.  Reductions in muscle coactivation and metabolic cost during visuomotor adaptation. , 2014, Journal of neurophysiology.

[10]  D. Wolpert,et al.  Gone in 0.6 Seconds: The Encoding of Motor Memories Depends on Recent Sensorimotor States , 2012, The Journal of Neuroscience.

[11]  F A Mussa-Ivaldi,et al.  Adaptive representation of dynamics during learning of a motor task , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[12]  Chao Gu,et al.  A Trial-by-Trial Window into Sensorimotor Transformations in the Human Motor Periphery , 2016, The Journal of Neuroscience.

[13]  Sarah E. Pekny,et al.  Protection and Expression of Human Motor Memories , 2011, The Journal of Neuroscience.

[14]  D. Wolpert,et al.  Specificity of Reflex Adaptation for Task-Relevant Variability , 2008, The Journal of Neuroscience.

[15]  D. Ostry,et al.  Muscle cocontraction following dynamics learning , 2008, Experimental Brain Research.

[16]  D. Wolpert,et al.  The Temporal Evolution of Feedback Gains Rapidly Update to Task Demands , 2013, The Journal of Neuroscience.

[17]  Wilsaan M. Joiner,et al.  Dissociating effects of error size, training duration, and amount of adaptation on the ability to retain motor memories. , 2019, Journal of neurophysiology.

[18]  Daniel M. Wolpert,et al.  A modular planar robotic manipulandum with end-point torque control , 2009, Journal of Neuroscience Methods.

[19]  Konrad Paul Kording,et al.  Estimating the sources of motor errors for adaptation and generalization , 2008, Nature Neuroscience.

[20]  Stephen H Scott,et al.  Rapid Feedback Responses Correlate with Reach Adaptation and Properties of Novel Upper Limb Loads , 2013, The Journal of Neuroscience.

[21]  Paul L. Gribble,et al.  Time course of changes in the long latency feedback response parallels the fast process of short term motor adaptation , 2020, bioRxiv.

[22]  Sae Franklin,et al.  Visuomotor feedback gains upregulate during the learning of novel dynamics , 2012, Journal of neurophysiology.

[23]  Reza Shadmehr,et al.  The Neural Feedback Response to Error As a Teaching Signal for the Motor Learning System , 2016, The Journal of Neuroscience.

[24]  Gary C. Sing,et al.  Linear hypergeneralization of learned dynamics across movement speeds reveals anisotropic, gain-encoding primitives for motor adaptation. , 2011, Journal of neurophysiology.

[25]  Sae Franklin,et al.  Fractionation of the visuomotor feedback response to directions of movement and perturbation , 2014, Journal of neurophysiology.

[26]  David J Ostry,et al.  Transfer of Motor Learning across Arm Configurations , 2002, The Journal of Neuroscience.

[27]  J. Randall Flanagan,et al.  Motor learning of novel dynamics is not represented in a single global coordinate system: evaluation of mixed coordinate representations and local learning , 2013, Journal of neurophysiology.

[28]  J. Saunders,et al.  Humans use continuous visual feedback from the hand to control fast reaching movements , 2003, Experimental Brain Research.

[29]  Maurice A. Smith,et al.  The Binding of Learning to Action in Motor Adaptation , 2011, PLoS Comput. Biol..

[30]  J. Lackner,et al.  Rapid adaptation to Coriolis force perturbations of arm trajectory. , 1994, Journal of neurophysiology.

[31]  Sae Franklin,et al.  Rapid visuomotor feedback gains are tuned to the task dynamics , 2017, Journal of neurophysiology.

[32]  R. Shadmehr,et al.  Interacting Adaptive Processes with Different Timescales Underlie Short-Term Motor Learning , 2006, PLoS biology.

[33]  F. Mussa-Ivaldi,et al.  The motor system does not learn the dynamics of the arm by rote memorization of past experience. , 1997, Journal of neurophysiology.

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

[35]  Mollie K. Marko,et al.  Sensitivity to prediction error in reach adaptation. , 2012, Journal of neurophysiology.

[36]  Rodrigo S. Maeda,et al.  Feedforward and Feedback Control Share an Internal Model of the Arm's Dynamics , 2018, The Journal of Neuroscience.

[37]  Konrad Paul Kording,et al.  Relevance of error: what drives motor adaptation? , 2009, Journal of neurophysiology.

[38]  E. Brenner,et al.  Fast corrections of movements with a computer mouse. , 2003, Spatial vision.

[39]  F Crevecoeur,et al.  Movement stability under uncertain internal models of dynamics. , 2010, Journal of neurophysiology.

[40]  J. Krakauer,et al.  Computational neurorehabilitation: modeling plasticity and learning to predict recovery , 2016, Journal of NeuroEngineering and Rehabilitation.

[41]  M. Kawato,et al.  Adaptation to Stable and Unstable Dynamics Achieved By Combined Impedance Control and Inverse Dynamics Model , 2003 .

[42]  Nicolas Schweighofer,et al.  Minimizing Precision-Weighted Sensory Prediction Errors via Memory Formation and Switching in Motor Adaptation , 2019, The Journal of Neuroscience.

[43]  J. A. Pruszynski,et al.  Temporal evolution of "automatic gain-scaling". , 2009, Journal of neurophysiology.

[44]  Rieko Osu,et al.  CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm , 2008, The Journal of Neuroscience.

[45]  R Shadmehr,et al.  Electromyographic Correlates of Learning an Internal Model of Reaching Movements , 1999, The Journal of Neuroscience.

[46]  T. Milner,et al.  Adaptive control of stiffness to stabilize hand position with large loads , 2003, Experimental Brain Research.

[47]  Rieko Osu,et al.  Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG. , 2002, Journal of neurophysiology.

[48]  David J. Ostry,et al.  Different adaptation rates to abrupt and gradual changes in environmental dynamics , 2018, Experimental Brain Research.

[49]  E. Bizzi,et al.  The control of stable postures in the multijoint arm , 1996, Experimental Brain Research.

[50]  Kurt A. Thoroughman,et al.  Motor adaptation to single force pulses: sensitive to direction but insensitive to within-movement pulse placement and magnitude. , 2006, Journal of neurophysiology.

[51]  J. Diedrichsen,et al.  A Dedicated Binding Mechanism for the Visual Control of Movement , 2014, Current Biology.

[52]  David W Franklin,et al.  Impedance control and internal model use during the initial stage of adaptation to novel dynamics in humans , 2005, The Journal of physiology.

[53]  David W Franklin,et al.  Increasing muscle co-contraction speeds up internal model acquisition during dynamic motor learning , 2018, Scientific Reports.

[54]  J. Flanagan,et al.  Learning and recall of incremental kinematic and dynamic sensorimotor transformations , 2005, Experimental Brain Research.

[55]  R A Scheidt,et al.  Persistence of motor adaptation during constrained, multi-joint, arm movements. , 2000, Journal of neurophysiology.

[56]  Helen J. Huang,et al.  Reduction of Metabolic Cost during Motor Learning of Arm Reaching Dynamics , 2012, The Journal of Neuroscience.

[57]  Rieko Osu,et al.  Endpoint Stiffness of the Arm Is Directionally Tuned to Instability in the Environment , 2007, The Journal of Neuroscience.

[58]  James L. Patton,et al.  Augmented Dynamics and Motor Exploration as Training for Stroke , 2013, IEEE Transactions on Biomedical Engineering.

[59]  Manu Chhabra,et al.  Flexible, Task-Dependent Use of Sensory Feedback to Control Hand Movements , 2011, The Journal of Neuroscience.

[60]  Jörn Diedrichsen,et al.  Reach adaptation: what determines whether we learn an internal model of the tool or adapt the model of our arm? , 2008, Journal of neurophysiology.

[61]  R. Shadmehr,et al.  Preparing to Reach: Selecting an Adaptive Long-Latency Feedback Controller , 2012, The Journal of Neuroscience.

[62]  Vincent S. Huang,et al.  Persistence of motor memories reflects statistics of the learning event. , 2009, Journal of neurophysiology.