The decay of motor adaptation to novel movement dynamics reveals an asymmetry in the stability of motion state-dependent learning

Motor adaptation paradigms provide a quantitative method to study short-term modification of motor commands. Despite the growing understanding of the role motion states (e.g., velocity) play in this form of motor learning, there is little information on the relative stability of memories based on these movement characteristics, especially in comparison to the initial adaptation. Here, we trained subjects to make reaching movements perturbed by force patterns dependent upon either limb position or velocity. Following training, subjects were exposed to a series of error-clamp trials to measure the temporal characteristics of the feedforward motor output during the decay of learning. The compensatory force patterns were largely based on the perturbation kinematic (e.g., velocity), but also showed a small contribution from the other motion kinematic (e.g., position). However, the velocity contribution in response to the position-based perturbation decayed at a slower rate than the position contribution to velocity-based training, suggesting a difference in stability. Next, we modified a previous model of motor adaptation to reflect this difference and simulated the behavior for different learning goals. We were interested in the stability of learning when the perturbations were based on different combinations of limb position or velocity that subsequently resulted in biased amounts of motion-based learning. We trained additional subjects on these combined motion-state perturbations and confirmed the predictions of the model. Specifically, we show that (1) there is a significant separation between the observed gain-space trajectories for the learning and decay of adaptation and (2) for combined motion-state perturbations, the gain associated to changes in limb position decayed at a faster rate than the velocity-dependent gain, even when the position-dependent gain at the end of training was significantly greater. Collectively, these results suggest that the state-dependent adaptation associated with movement velocity is relatively more stable than that based on position.

[1]  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.

[2]  E. Bizzi,et al.  Neuronal correlates of movement dynamics in the dorsal and ventral premotor area in the monkey , 2005, Experimental Brain Research.

[3]  Sarah E. Criscimagna-Hemminger,et al.  Consolidation Patterns of Human Motor Memory , 2008, The Journal of Neuroscience.

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

[5]  E. Bizzi,et al.  Neuronal Correlates of Motor Performance and Motor Learning in the Primary Motor Cortex of Monkeys Adapting to an External Force Field , 2001, Neuron.

[6]  Sherwin S Chan,et al.  Motor cortical representation of position and velocity during reaching. , 2007, Journal of neurophysiology.

[7]  J. Krakauer,et al.  Adaptation to Visuomotor Transformations: Consolidation, Interference, and Forgetting , 2005, The Journal of Neuroscience.

[8]  A. Prochazka Chapter 11 Quantifying Proprioception , 1999 .

[9]  Jeremy D Wong,et al.  Somatosensory Plasticity and Motor Learning , 2010, The Journal of Neuroscience.

[10]  D. Wolpert,et al.  Context-Dependent Decay of Motor Memories during Skill Acquisition , 2013, Current Biology.

[11]  R. Shadmehr,et al.  Decay of Motor Memories in the Absence of Error , 2013, The Journal of Neuroscience.

[12]  Wilsaan M. Joiner,et al.  Adaptive Control of Saccades via Internal Feedback , 2008, The Journal of Neuroscience.

[13]  Juan Fernández-Ruiz,et al.  Decay of prism aftereffects under passive and active conditions. , 2004, Brain research. Cognitive brain research.

[14]  D. Wolpert,et al.  Failure to Consolidate the Consolidation Theory of Learning for Sensorimotor Adaptation Tasks , 2004, The Journal of Neuroscience.

[15]  L. Paninski,et al.  Spatiotemporal tuning of motor cortical neurons for hand position and velocity. , 2004, Journal of neurophysiology.

[16]  E. Bizzi,et al.  Consolidation in human motor memory , 1996, Nature.

[17]  Wilsaan M. Joiner,et al.  Long-term retention explained by a model of short-term learning in the adaptive control of reaching. , 2008, Journal of neurophysiology.

[18]  Reza Shadmehr,et al.  Contributions of the cerebellum and the motor cortex to acquisition and retention of motor memories , 2014, NeuroImage.

[19]  A. P. Georgopoulos,et al.  Movement parameters and neural activity in motor cortex and area 5. , 1994, Cerebral cortex.

[20]  D. Wolpert,et al.  Temporal and amplitude generalization in motor learning. , 1998, Journal of neurophysiology.

[21]  A. Prochazka Quantifying proprioception. , 1999, Progress in brain research.

[22]  Yohsuke R. Miyamoto,et al.  Temporal structure of motor variability is dynamically regulated and predicts motor learning ability , 2014, Nature Neuroscience.

[23]  E. W. Block,et al.  Motor learning in the “podokinetic” system and its role in spatial orientation during locomotion , 1998, Experimental Brain Research.

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

[25]  T. Brashers-Krug,et al.  Functional Stages in the Formation of Human Long-Term Motor Memory , 1997, The Journal of Neuroscience.

[26]  E. Bizzi,et al.  Neuronal activity in the supplementary motor area of monkeys adapting to a new dynamic environment. , 2004, Journal of neurophysiology.

[27]  Maurice A. Smith,et al.  The Decay of Motor Memories Is Independent of Context Change Detection , 2015, PLoS Comput. Biol..

[28]  Gary C. Sing,et al.  Primitives for Motor Adaptation Reflect Correlated Neural Tuning to Position and Velocity , 2009, Neuron.

[29]  Emilio Bizzi,et al.  Intermittent Practice Facilitates Stable Motor Memories , 2006, The Journal of Neuroscience.

[30]  Gary C. Sing,et al.  Limb motion dictates how motor learning arises from arbitrary environmental dynamics. , 2013, Journal of neurophysiology.

[31]  W. T. Thach,et al.  Throwing while looking through prisms. II. Specificity and storage of multiple gaze-throw calibrations. , 1996, Brain : a journal of neurology.

[32]  E. Vaadia,et al.  Expressions of Multiple Neuronal Dynamics during Sensorimotor Learning in the Motor Cortex of Behaving Monkeys , 2011, PloS one.

[33]  Reza Shadmehr,et al.  Optimizing effort: increased efficiency of motor memory with time away from practice. , 2015, Journal of neurophysiology.

[34]  Jörn Diedrichsen,et al.  Structural learning in feedforward and feedback control. , 2012, Journal of neurophysiology.

[35]  E. Vaadia,et al.  Neuronal Correlates of Memory Formation in Motor Cortex after Adaptation to Force Field , 2010, The Journal of Neuroscience.

[36]  Wilsaan M Joiner,et al.  The training schedule affects the stability, not the magnitude, of the interlimb transfer of learned dynamics. , 2013, Journal of neurophysiology.

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

[38]  Aaron L. Wong,et al.  Saccade adaptation improves in response to a gradually introduced stimulus perturbation , 2011, Neuroscience Letters.

[39]  A. Haith,et al.  Unlearning versus savings in visuomotor adaptation: comparing effects of washout, passage of time, and removal of errors on motor memory , 2013, Front. Hum. Neurosci..

[40]  J. Krakauer,et al.  Sensory prediction errors drive cerebellum-dependent adaptation of reaching. , 2007, Journal of neurophysiology.

[41]  Maurice A. Smith,et al.  Environmental Consistency Determines the Rate of Motor Adaptation , 2014, Current Biology.

[42]  E. Bizzi,et al.  Cortical correlates of learning in monkeys adapting to a new dynamical environment. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[43]  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.

[44]  Hannah J. Block,et al.  Interlimb coordination during locomotion: what can be adapted and stored? , 2005, Journal of neurophysiology.

[45]  S. M. Morton,et al.  A locomotor adaptation including explicit knowledge and removal of postadaptation errors induces complete 24-hour retention. , 2013, Journal of neurophysiology.

[46]  M. Kawato,et al.  Inverse-dynamics model eye movement control by Purkinje cells in the cerebellum , 1993, Nature.

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

[48]  B. Edin,et al.  Muscle afferent responses to isometric contractions and relaxations in humans. , 1990, Journal of neurophysiology.

[49]  J. Krakauer,et al.  Error correction, sensory prediction, and adaptation in motor control. , 2010, Annual review of neuroscience.

[50]  Y. Rossetti,et al.  Three timescales in prism adaptation. , 2015, Journal of neurophysiology.

[51]  Ilana Nisky,et al.  Learning and generalization in an isometric visuomotor task. , 2015, Journal of neurophysiology.

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