Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system

In our daily life we often make complex actions comprised of linked movements, such as reaching for a cup of coffee and bringing it to our mouth to drink. Recent work has highlighted the role of such linked movements in the formation of independent motor memories, affecting the learning rate and ability to learn opposing force fields. In these studies, distinct prior movements (lead-in movements) allow adaptation of opposing dynamics on the following movement. Purely visual or purely passive lead-in movements exhibit different angular generalization functions of this motor memory as the lead-in movements are modified, suggesting different neural representations. However, we currently have no understanding of how different movement kinematics (distance, speed or duration) affect this recall process and the formation of independent motor memories. Here we investigate such kinematic generalization for both passive and visual lead-in movements to probe their individual characteristics. After participants adapted to opposing force fields using training lead-in movements, the lead-in kinematics were modified on random trials to test generalization. For both visual and passive modalities, recalled compensation was sensitive to lead-in duration and peak speed, falling off away from the training condition. However, little reduction in force was found with increasing lead-in distance. Interestingly, asymmetric transfer between lead-in movement modalities was also observed, with partial transfer from passive to visual, but very little vice versa. Overall these tuning effects were stronger for passive compared to visual lead-ins demonstrating the difference in these sensory inputs in regulating motor memories. Our results suggest these effects are a consequence of state estimation, with differences across modalities reflecting their different levels of sensory uncertainty arising as a consequence of dissimilar feedback delays.

[1]  O. J. M. Smith,et al.  A controller to overcome dead time , 1959 .

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

[3]  David W. Franklin,et al.  Coordinate Representations for Interference Reduction in Motor Learning , 2015, PloS one.

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

[5]  E Bizzi,et al.  Motor learning by field approximation. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[6]  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.

[7]  Chethan Pandarinath,et al.  Neural population dynamics in human motor cortex during movements in people with ALS , 2015, eLife.

[8]  D. Wolpert,et al.  The Value of the Follow-Through Derives from Motor Learning Depending on Future Actions , 2015, Current Biology.

[9]  Michael I. Jordan,et al.  An internal model for sensorimotor integration. , 1995, Science.

[10]  Stephen H Scott,et al.  Limited transfer of learning between unimanual and bimanual skills within the same limb , 2006, Nature Neuroscience.

[11]  R. Shadmehr,et al.  Internal models and contextual cues: encoding serial order and direction of movement. , 2005, Journal of neurophysiology.

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

[13]  D. Wolpert,et al.  The effect of contextual cues on the encoding of motor memories , 2013, Journal of neurophysiology.

[14]  Reza Shadmehr,et al.  Quantifying Generalization from Trial-by-Trial Behavior of Adaptive Systems that Learn with Basis Functions: Theory and Experiments in Human Motor Control , 2003, The Journal of Neuroscience.

[15]  Daniel M. Wolpert,et al.  Forward Models for Physiological Motor Control , 1996, Neural Networks.

[16]  Jeremy D Wong,et al.  Visual cues signaling object grasp reduce interference in motor learning. , 2009, Journal of neurophysiology.

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

[18]  J. Krakauer,et al.  Explicit and Implicit Contributions to Learning in a Sensorimotor Adaptation Task , 2014, The Journal of Neuroscience.

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

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

[21]  Mathias Hegele,et al.  Adaptation to visuomotor rotations in younger and older adults. , 2008, Psychology and aging.

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

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

[24]  F. A. Mussa-Ivaldi,et al.  Does the motor control system use multiple models and context switching to cope with a variable environment? , 2002, Experimental Brain Research.

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

[26]  D F Stegeman,et al.  Generalization and transfer of contextual cues in motor learning. , 2015, Journal of neurophysiology.

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

[28]  Mathias Hegele,et al.  Implicit and explicit components of dual adaptation to visuomotor rotations , 2010, Consciousness and Cognition.

[29]  David W. Franklin,et al.  Neural Tuning Functions Underlie Both Generalization and Interference , 2015, PloS one.

[30]  R. Shadmehr,et al.  A Gain-Field Encoding of Limb Position and Velocity in the Internal Model of Arm Dynamics , 2003, PLoS biology.

[31]  Matthew T. Kaufman,et al.  Neural population dynamics during reaching , 2012, Nature.

[32]  Stephen H Scott,et al.  Dynamic Multisensory Integration: Somatosensory Speed Trumps Visual Accuracy during Feedback Control , 2016, The Journal of Neuroscience.

[33]  D. Wolpert,et al.  Motor Planning, Not Execution, Separates Motor Memories , 2016, Neuron.

[34]  Sae Franklin,et al.  Characterization of Neural Tuning: Visual Lead-in Movements Generalize in Speed and Distance , 2018, Converging Clinical and Engineering Research on Neurorehabilitation III.

[35]  Reza Shadmehr,et al.  Learning of action through adaptive combination of motor primitives , 2000, Nature.

[36]  David W. Franklin,et al.  Adaptive tuning functions arise from visual observation of past movement , 2016, Scientific Reports.

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

[38]  David J Ostry,et al.  Modifiability of generalization in dynamics learning. , 2007, Journal of neurophysiology.

[39]  Reza Shadmehr,et al.  Internal models of limb dynamics and the encoding of limb state , 2005, Journal of neural engineering.

[40]  D. Wolpert,et al.  Is the cerebellum a smith predictor? , 1993, Journal of motor behavior.

[41]  D. Nozaki,et al.  Distinct Motor Plans Form and Retrieve Distinct Motor Memories for Physically Identical Movements , 2012, Current Biology.

[42]  John W. Krakauer,et al.  Independent learning of internal models for kinematic and dynamic control of reaching , 1999, Nature Neuroscience.

[43]  Masaya Hirashima,et al.  Tagging motor memories with transcranial direct current stimulation allows later artificially-controlled retrieval , 2016, eLife.

[44]  A. Cangelosi,et al.  Active lead-in variability affects motor memory formation and slows motor learning , 2017, Scientific Reports.

[45]  J. Kalaska,et al.  Colored context cues can facilitate the ability to learn and to switch between multiple dynamical force fields. , 2011, Journal of neurophysiology.

[46]  Mathias Hegele,et al.  Explicit strategies in force field adaptation , 2019, bioRxiv.

[47]  Masaya Hirashima,et al.  Gain Field Encoding of the Kinematics of Both Arms in the Internal Model Enables Flexible Bimanual Action , 2011, The Journal of Neuroscience.