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arxiv: 2404.13258 · v1 · submitted 2024-04-20 · 📡 eess.SY · cs.SY· math.DS

Human Motor Learning Dynamics in High-dimensional Tasks

Ankur Kamboj , Rajiv Ranganathan , Xiaobo Tan , Vaibhav Srivastava This is my paper

Pith reviewed 2026-05-24 02:12 UTC · model grok-4.3

classification 📡 eess.SY cs.SYmath.DS
keywords motor learningmotor synergiesinternal model theoryhigh-dimensional taskscomputational modelconvergence propertiestarget capture gameperformance trade-offs
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The pith

A model using motor synergies for low-dimensional representations and internal model theory for fast and slow processes captures human motor learning in high-DoF tasks, converges, and matches human data while showing parameter tuning for 4D

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a computational model of human motor learning suited to tasks with many degrees of freedom. It reduces the high-dimensional space to lower dimensions via motor synergies and applies internal model theory to handle both rapid adaptations and gradual refinements. The model is shown to converge and is tested against movement data collected from people playing a target capture game. Parameter effects on trade-offs such as speed-accuracy, exploration-exploitation, satisficing, and flexibility-performance are analyzed, with evidence that the human system adjusts those parameters to improve learning and performance outcomes.

Core claim

The authors construct a computational motor learning model that leverages the concept of motor synergies to extract low-dimensional learning representations in the high-dimensional motor space and the internal model theory of motor control to capture both fast and slow motor learning processes. They establish the model's convergence properties and validate it using data from a target capture game played by human participants. They study the influence of model parameters on several motor learning trade-offs such as speed-accuracy, exploration-exploitation, satisficing, and flexibility-performance, and show that the human motor learning system tunes these parameters to optimize learning and 4D

What carries the argument

Motor synergy extraction of low-dimensional representations combined with internal model theory updates for fast and slow learning timescales.

If this is right

  • The model converges to stable low-dimensional representations under the stated conditions.
  • Parameter settings control the balance among speed-accuracy, exploration-exploitation, satisficing, and flexibility-performance.
  • Human participants appear to select parameter values that jointly optimize learning rate and output metrics.
  • The same framework can be used to predict how changes in task demands would alter observed learning behavior.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could be used to design feedback or training protocols that deliberately shift the trade-off parameters for rehabilitation of coordination deficits.
  • Similar dimensionality-reduction steps might apply to learning problems in other high-dimensional control domains such as prosthetics or multi-limb robotics.
  • The observed parameter tuning implies that motor systems solve a multi-objective optimization problem rather than a single performance goal.
  • Experiments that artificially constrain synergies could test whether learning slows or becomes less flexible in the manner the model predicts.

Load-bearing premise

Motor synergies supply a valid mechanism for pulling low-dimensional learning representations out of high-dimensional motor spaces, and internal model theory can be applied directly to both fast and slow processes in this setting.

What would settle it

If simulations of the model produce learning trajectories or final performance levels that systematically fail to match the movement patterns and improvement rates recorded from the human participants in the target capture game.

Figures

Figures reproduced from arXiv: 2404.13258 by Ankur Kamboj, Rajiv Ranganathan, Vaibhav Srivastava, Xiaobo Tan.

Figure 1
Figure 1. Figure 1: Performance measures across subjects: Temporal evolution of (a) reaching error, and (b) straightness of trajectory for subjects (red) and the respective fitted HML model (blue) across trials. where the minima associated with the first term determines a u that makes the human’s estimate of cursor velocity Cˆu equal to the error-driven proportional feedback kP ex, for some kP > 0, and the second term ensures… view at source ↗
Figure 2
Figure 2. Figure 2: Cursor Trajectories: Cursor trajectory data from human experiments (a), (c) and the fitted model (b), (d). As learning progresses through the 8 sessions, the trajectories become closer to a straight line between targets, which the proposed HML model also captures. (e) shows the evolution of forward model error for the fitted model as a function of trials. 5 [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparing HML model with Ref [35] model: Comparing the errors in RE curve fitting from the model in Ref [35] to the HML model shows that the model in Ref [35] is not as accurate as HML model in capturing the RE for this motor learning task. 4.3 Investigating Trade-offs in Motor Learning Behavior We showed that the HML model can capture the human motor learning behavior in novel learning tasks for high-dime… view at source ↗
Figure 4
Figure 4. Figure 4: Effort variation with η: Distribution of driving and exploratory effort (averaged across 128 Monte Carlo runs) with means and 95% confidence bounds across trials as η is varied around its fitted value 3.1742. While driving effort increases, exploratory effort decreases initially, and both plateau past the fitted η value. One-tailed paired t-tests over the effort values across trials reveal this plateauing … view at source ↗
Figure 5
Figure 5. Figure 5: Speed and accuracy variation with kP : Across trial distribution (averaged over 128 Monte Carlo runs) of speed and accuracy with means and 95% confidence bounds as kP is varied around its fitted value 1.3098. Accuracy is highest around the fitted value (p< 0.001) and past that speed increases while accuracy decreases. accuracy go up as kP increases to a certain value. Comparing the accuracy values across t… view at source ↗
Figure 6
Figure 6. Figure 6: Satisficing effect: Probabilities of entering the targets as a function of target size and learning threshold (FME) for different trial times. For smaller trial times, lower learning thresholds (high learning accuracy) are required to achieve high success probabilities for the same target sizes. Satisficing behavior is observed at high learning accuracy (low FME) levels, where learning with higher accuracy… view at source ↗
Figure 7
Figure 7. Figure 7: FME variation with σu and number of synergies: FME as a function of increasing σu and number of synergies used. For limited training time, using more synergies is not always the most optimal strategy. Minimum FME (blue cells) is achieved at synergies lower than 19. 5.1 Motor Synergies to Capture High-dimensional Motor Learning There has been strong evidence of the use of postural synergies by the human ner… view at source ↗
Figure 8
Figure 8. Figure 8: Effort variation with σu: Distribution of driving and exploratory effort across trials as σu is varied around its fit value 0.8764. Driving effort is highest around the fitted value of σu (p< 0.01), while exploratory effort increases monotonically with σu. C.2 Comparative Analysis of HML Model’s Efficacy in Explaining the Motor Learning [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparing HML model with Ref [35] model: (a) Comparing the FME curves from the model in Ref [35] to the HML model shows that the model in Ref [35] is not as accurate as HML model in capturing the human skill state (represented by the participant’s estimate of BoMI mapping matrix Cˆ) for this motor learning task. (b) and (c) show the RE and SoT evolution curves for Subject 6. References [1] MN Abdelghani, T… view at source ↗
read the original abstract

Conventional approaches to enhancing movement coordination, such as providing instructions and visual feedback, are often inadequate in complex motor tasks with multiple degrees of freedom (DoFs). To effectively address coordination deficits in such complex motor systems, it becomes imperative to develop interventions grounded in a model of human motor learning; however, modeling such learning processes is challenging due to the large DoFs. In this paper, we present a computational motor learning model that leverages the concept of motor synergies to extract low-dimensional learning representations in the high-dimensional motor space and the internal model theory of motor control to capture both fast and slow motor learning processes. We establish the model's convergence properties and validate it using data from a target capture game played by human participants. We study the influence of model parameters on several motor learning trade-offs such as speed-accuracy, exploration-exploitation, satisficing, and flexibility-performance, and show that the human motor learning system tunes these parameters to optimize learning and various output performance metrics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The paper presents a computational motor learning model that combines motor synergies to extract low-dimensional representations from high-DoF motor spaces with internal model theory to capture fast and slow learning processes. It establishes convergence properties of the model, validates it on data from a human target-capture game, analyzes the influence of model parameters on trade-offs including speed-accuracy, exploration-exploitation, satisficing, and flexibility-performance, and concludes that the human motor system tunes these parameters to optimize learning and performance metrics.

Significance. If the convergence analysis holds and the validation provides quantitative fits to human data, the work offers a framework for modeling and intervening in complex motor coordination tasks. The explicit linkage of fitted parameters to multiple performance trade-offs and the use of empirical human data (rather than purely simulated) are strengths that could inform both theory and applications in motor control.

minor comments (3)
  1. [Abstract] Abstract: the claim that convergence properties are established would benefit from a one-sentence indication of the proof technique (e.g., Lyapunov function or contraction mapping) to orient readers before the full derivation.
  2. The description of the target-capture game and data collection (participant numbers, trial structure, preprocessing) should include explicit criteria for data exclusion or outlier handling to allow replication.
  3. Notation for the synergy matrix and the fast/slow internal-model updates should be introduced with a short table or equation reference in the model section to improve readability for readers outside the immediate subfield.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their summary of the work, positive assessment of its significance, and recommendation for minor revision. No major comments appear in the provided report, so we have no specific points requiring point-by-point rebuttal or revision at this stage. We remain available to address any additional feedback that may arise.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper constructs its motor learning model from established external domain theories (motor synergies for low-dimensional representations and internal model theory for fast/slow processes), proves convergence properties independently, validates against separate human target-capture data, and then analyzes fitted parameter effects on trade-offs. No load-bearing step reduces by construction to a self-definition, a fitted input renamed as prediction, or a self-citation chain; the derivation chain is self-contained against external benchmarks and falsifiable data.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

Central claim rests on domain assumptions from motor control literature plus free parameters whose effects on trade-offs are analyzed; no new entities postulated.

free parameters (1)
  • model parameters
    Parameters whose influence on speed-accuracy, exploration-exploitation, satisficing, and flexibility-performance trade-offs is studied and tuned to match human data.
axioms (2)
  • domain assumption Motor synergies exist and can be leveraged to extract low-dimensional learning representations from high-dimensional motor space
    Invoked as the basis for simplifying high-DoF learning in the model construction.
  • domain assumption Internal model theory of motor control captures both fast and slow motor learning processes
    Used to structure the dual-timescale learning dynamics in the computational model.

pith-pipeline@v0.9.0 · 5705 in / 1533 out tokens · 32844 ms · 2026-05-24T02:12:00.098253+00:00 · methodology

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Reference graph

Works this paper leans on

53 extracted references · 53 canonical work pages

  1. [1]

    Sensitivity d erivatives for flexible senso- rimotor learning

    MN Abdelghani, Timothy P Lillicrap, and Douglas B Tweed. Sensitivity d erivatives for flexible senso- rimotor learning. Neural Computation, 20(8):2085–2111, 2008

    work page 2085

  2. [2]

    Subject-specific a ssist-as-needed controllers for a hand exoskeleton for rehabilitation

    Priyanshu Agarwal and Ashish D Deshpande. Subject-specific a ssist-as-needed controllers for a hand exoskeleton for rehabilitation. IEEE Robotics and Automation Letters , 3(1):508–515, 2017

    work page 2017

  3. [3]

    A framework for ad aptation of training task, assistance and feedback for optimizing motor (re)-learning with a robotic exos keleton

    Priyanshu Agarwal and Ashish D Deshpande. A framework for ad aptation of training task, assistance and feedback for optimizing motor (re)-learning with a robotic exos keleton. IEEE Robotics and Au- tomation Letters , 4(2):808–815, 2019

    work page 2019

  4. [4]

    The effects o f motor modularity on performance, learning and generalizability in upper-extremity reaching: a computa tional analysis

    Mazen Al Borno, Jennifer L Hicks, and Scott L Delp. The effects o f motor modularity on performance, learning and generalizability in upper-extremity reaching: a computa tional analysis. Journal of the Royal Society Interface , 17(167):20200011, 2020

    work page 2020

  5. [5]

    Max Berniker, Anthony Jarc, Emilio Bizzi, and Matthew C. Tresch. Simplified and effective motor control based on muscle synergies to exploit musculoskeletal dyna mics. Proceedings of the National Academy of Sciences , 106(18):7601–7606, 2009

    work page 2009

  6. [6]

    The Coordination and Regulation of Movements

    NA Bernstein’s. The Coordination and Regulation of Movements . Oxford: Pergamon Press Ltd, 1967

    work page 1967

  7. [7]

    Cognitive Modeling

    Jerome R Busemeyer and Adele Diederich. Cognitive Modeling. Sage, 2010

    work page 2010

  8. [8]

    Dissociating error-based and reinforcement-based loss functions during sen sorimotor learning

    Joshua GA Cashaback, Heather R McGregor, Ayman Mohatarem , and Paul L Gribble. Dissociating error-based and reinforcement-based loss functions during sen sorimotor learning. PLoS Computational Biology, 13(7):e1005623, 2017

    work page 2017

  9. [9]

    Assist-as-needed exoskeleton for hand joint rehabilitatio n based on muscle effort detection

    Jenny Carolina Castiblanco, Ivan Fernando Mondragon, Catalina Alvarado-Rojas, and Julian D Col- orado. Assist-as-needed exoskeleton for hand joint rehabilitatio n based on muscle effort detection. Sensors, 21(13):4372, 2021

    work page 2021

  10. [10]

    Size of error affects cerebellar contributions to motor learning

    Sarah E Criscimagna-Hemminger, Amy J Bastian, and Reza Shadm ehr. Size of error affects cerebellar contributions to motor learning. Journal of Neurophysiology , 103(4):2275–2284, 2010

    work page 2010

  11. [11]

    The influence of visual motion on motor learning

    Zachary Danziger and Ferdinando A Mussa-Ivaldi. The influence of visual motion on motor learning. Journal of Neuroscience , 32(29):9859–9869, 2012

    work page 2012

  12. [12]

    K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elit ist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation , 6(2):182–197, 2002. 19

    work page 2002

  13. [13]

    Loco motor primitives in newborn babies and their development

    Nadia Dominici, Yuri P Ivanenko, Germana Cappellini, Andrea d’Ave lla, Vito Mond ` ı, Marika Cicchese, Adele Fabiano, Tiziana Silei, Ambrogio Di Paolo, Carlo Giannini, et al. Loco motor primitives in newborn babies and their development. Science, 334(6058):997–999, 2011

    work page 2011

  14. [14]

    Quantif ying generalization from trial-by-trial behavior of adaptive systems that learn with basis functions: Theo ry and experiments in human motor control

    Opher Donchin, Joseph T Francis, and Reza Shadmehr. Quantif ying generalization from trial-by-trial behavior of adaptive systems that learn with basis functions: Theo ry and experiments in human motor control. Journal of Neuroscience , 23(27):9032–9045, 2003

    work page 2003

  15. [15]

    Brain regions controlling non- synergistic versus synergistic movement of the digits: A functiona l magnetic resonance imaging study

    H Henrik Ehrsson, Johann P Kuhtz-Buschbeck, and Hans Fors sberg. Brain regions controlling non- synergistic versus synergistic movement of the digits: A functiona l magnetic resonance imaging study. Journal of Neuroscience , 22(12):5074–5080, 2002

    work page 2002

  16. [16]

    The information capacity of the human motor syst em in controlling the amplitude of movement

    Paul M Fitts. The information capacity of the human motor syst em in controlling the amplitude of movement. Journal of Experimental Psychology , 47(6):381, 1954

    work page 1954

  17. [17]

    Motor primitives in vertebrat es and invertebrates

    Tamar Flash and Binyamin Hochner. Motor primitives in vertebrat es and invertebrates. Current Opinion in Neurobiology , 15(6):660–666, 2005

    work page 2005

  18. [18]

    Modular organization of finger movements by the human central nervous system

    Reinhard Gentner and Joseph Classen. Modular organization of finger movements by the human central nervous system. Neuron, 52(4):731–742, 2006

    work page 2006

  19. [19]

    Encoding of motor skill in the corticomuscular syst em of musicians

    Reinhard Gentner, Susanne Gorges, David Weise, Kristin aufm K ampe, Mathias Buttmann, and Joseph Classen. Encoding of motor skill in the corticomuscular syst em of musicians. Current Biol- ogy, 20(20):1869–1874, 2010

    work page 2010

  20. [20]

    The binding of learning to action in motor adaptation

    Luis Nicolas Gonzalez Castro, Craig Bryant Monsen, and Maurice A Smith. The binding of learning to action in motor adaptation. PLoS Computational Biology , 7(6):e1002052, 2011

    work page 2011

  21. [21]

    A memory of errors in sensorimotor learning

    David J Herzfeld, Pavan A Vaswani, Mollie K Marko, and Reza Shadm ehr. A memory of errors in sensorimotor learning. Science, 345(6202):1349–1353, 2014

    work page 2014

  22. [22]

    Contribution of e xplicit processes to reinforcement- based motor learning

    Peter Holland, Olivier Codol, and Joseph M Galea. Contribution of e xplicit processes to reinforcement- based motor learning. Journal of Neurophysiology , 119(6):2241–2255, 2018

    work page 2018

  23. [23]

    Robust Adaptive Control , volume 1

    Petros A Ioannou and Jing Sun. Robust Adaptive Control , volume 1. PTR Prentice-Hall Upper Saddle River, NJ, 1996

    work page 1996

  24. [24]

    Forward models: Super vised learning with a distal teacher

    Michael I Jordan and David E Rumelhart. Forward models: Super vised learning with a distal teacher. Cognitive Science, 16(3):307–354, 1992

    work page 1992

  25. [25]

    Towards modeling human motor learning dynamics in high-dimensional spaces

    Ankur Kamboj, Rajiv Ranganathan, Xiaobo Tan, and Vaibhav Sr ivastava. Towards modeling human motor learning dynamics in high-dimensional spaces. In American Control Conference, pages 683–688, Atlanta, GA, 2022

    work page 2022

  26. [26]

    H. K. Khalil. Nonlinear Systems . Prentice Hall, third edition, 2002

    work page 2002

  27. [27]

    Independent learning of internal models for kinematic and dynamic control of reaching

    Ghilardi MF Krakauer, John W and Ghez C. Independent learning of internal models for kinematic and dynamic control of reaching. Nature Neuroscience, 2:1026–1031, 1999

    work page 1999

  28. [28]

    Motor learning: its relevance to stroke reco very and neurorehabilitation

    John W Krakauer. Motor learning: its relevance to stroke reco very and neurorehabilitation. Current Opinion in Neurology , 19(1):84–90, 2006

    work page 2006

  29. [29]

    Motor learning

    John W Krakauer, Alkis M Hadjiosif, Jing Xu, Aaron L Wong, and Ad rian M Haith. Motor learning. Comprehensive Physiology, 9(2):613–663, 2019

    work page 2019

  30. [30]

    Human sensorimotor learn ing: adaptation, skill, and beyond

    John W Krakauer and Pietro Mazzoni. Human sensorimotor learn ing: adaptation, skill, and beyond. Current Opinion in Neurobiology , 21(4):636–644, 2011. 20

    work page 2011

  31. [31]

    Introduction to Stochastic Control

    Harold Joseph Kushner. Introduction to Stochastic Control . Holt, Rinehart and Winston New York, 1971

    work page 1971

  32. [32]

    A synergy-based hand control is encoded in human motor cortical a reas

    Andrea Leo, Giacomo Handjaras, Matteo Bianchi, Hamal Marino , Marco Gabiccini, Andrea Guidi, Enzo Pasquale Scilingo, Pietro Pietrini, Antonio Bicchi, Marco Santello, and Emiliano Ricciardi. A synergy-based hand control is encoded in human motor cortical a reas. eLife, 5:e13420, feb 2016

    work page 2016

  33. [33]

    Persistency of excitation c riteria for linear, multivariable, time- varying systems

    Iven MY Mareels and Michel Gevers. Persistency of excitation c riteria for linear, multivariable, time- varying systems. Mathematics of Control, Signals and Systems , 1:203–226, 1988

    work page 1988

  34. [34]

    Preparatory activity in motor cortex reflects learning of local visuomotor skills

    Rony Paz, Thomas Boraud, Chen Natan, Hagai Bergman, and E ilon Vaadia. Preparatory activity in motor cortex reflects learning of local visuomotor skills. Nature Neuroscience, 6(8):882–890, 2003

    work page 2003

  35. [35]

    The dynamics of motor learning through the formation of internal models

    Camilla Pierella, Maura Casadio, Ferdinando A Mussa-Ivaldi, and Sa ra A Solla. The dynamics of motor learning through the formation of internal models. PLoS Computational Biology, 15(12):e1007118, 2019

    work page 2019

  36. [36]

    Learning to be lazy: Exploiting redundancy in a novel task to minimize movement-related effort

    Rajiv Ranganathan, Adenike Adewuyi, and Ferdinando A Mussa- Ivaldi. Learning to be lazy: Exploiting redundancy in a novel task to minimize movement-related effort. Journal of Neuroscience , 33(7):2754– 2760, 2013

    work page 2013

  37. [37]

    Wearable technologies for hand joints monitoring for rehabilitation: A survey

    Adnan Rashid and Osman Hasan. Wearable technologies for hand joints monitoring for rehabilitation: A survey. Microelectronics Journal, 88:173–183, 2019

    work page 2019

  38. [38]

    Human Motor Control

    David A Rosenbaum. Human Motor Control . Academic Press, 2009

    work page 2009

  39. [39]

    Neur al bases of hand synergies

    Marco Santello, Gabriel Baud-Bovy, and Henrik J¨ orntell. Neur al bases of hand synergies. Frontiers in Computational Neuroscience, 7:23, 2013

    work page 2013

  40. [40]

    Postu ral hand synergies for tool use

    Marco Santello, Martha Flanders, and John F Soechting. Postu ral hand synergies for tool use. Journal of Neuroscience, 18(23):10105–10115, 1998

    work page 1998

  41. [41]

    Adaptive Control: Stability, Convergence, and Robustness

    Shankar Sastry and Marc Bodson. Adaptive Control: Stability, Convergence, and Robustness . Prentice- Hall, Inc., 1989

    work page 1989

  42. [42]

    Adaptive repre sentation of dynamics during learning of a motor task

    Reza Shadmehr and Ferdinando A Mussa-Ivaldi. Adaptive repre sentation of dynamics during learning of a motor task. Journal of Neuroscience , 14(5):3208–3224, 1994

    work page 1994

  43. [43]

    Rational choice and the structure of the env ironment

    Herbert A Simon. Rational choice and the structure of the env ironment. Psychological Review, 63(2):129, 1956

    work page 1956

  44. [44]

    Interact ing adaptive processes with different timescales underlie short-term motor learning

    Maurice A Smith, Ali Ghazizadeh, and Reza Shadmehr. Interact ing adaptive processes with different timescales underlie short-term motor learning. PLoS Biology, 4(6):e179, 2006

    work page 2006

  45. [45]

    It’s not (only) the mean that matters: varia bility, noise and exploration in skill learning

    Dagmar Sternad. It’s not (only) the mean that matters: varia bility, noise and exploration in skill learning. Current Opinion in Behavioral Sciences , 20:183–195, 2018

    work page 2018

  46. [46]

    R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction . MIT Press, 1998

    work page 1998

  47. [47]

    Flexible cognitive strategies during motor learning

    Jordan A Taylor and Richard B Ivry. Flexible cognitive strategies during motor learning. PLoS Com- putational Biology, 7(3):e1001096, 2011

    work page 2011

  48. [48]

    Mat rix factorization algorithms for the identification of muscle synergies: Evaluation on simulated and ex perimental data sets

    Matthew C Tresch, Vincent CK Cheung, and Andrea d’Avella. Mat rix factorization algorithms for the identification of muscle synergies: Evaluation on simulated and ex perimental data sets. Journal of Neurophysiology, 95(4):2199–2212, 2006

    work page 2006

  49. [49]

    Sen- sory prediction errors drive cerebellum-dependent adaptation of reaching

    Ya-weng Tseng, Jorn Diedrichsen, John W Krakauer, Reza Sha dmehr, and Amy J Bastian. Sen- sory prediction errors drive cerebellum-dependent adaptation of reaching. Journal of Neurophysiology , 98(1):54–62, 2007. 21

    work page 2007

  50. [50]

    Elaborated reichardt d etectors

    Jan PH Van Santen and George Sperling. Elaborated reichardt d etectors. JOSA A , 2(2):300–321, 1985

    work page 1985

  51. [51]

    Sclabassi, and Zhi-Hong Mao

    Ramana Vinjamuri, Mingui Sun, Cheng-Chun Chang, Heung-No L ee, Robert J. Sclabassi, and Zhi-Hong Mao. Dimensionality reduction in control and coordination of the hum an hand. IEEE Transactions on Biomedical Engineering, 57(2):284–295, 2010

    work page 2010

  52. [52]

    Internal mode ls in the cerebellum

    Daniel M Wolpert, R Chris Miall, and Mitsuo Kawato. Internal mode ls in the cerebellum. Trends in Cognitive Sciences, 2(9):338–347, 1998

    work page 1998

  53. [53]

    Design and development of a hand exoskeleton for rehabilitation of hand injuries

    Fuhai Zhang, Lei Hua, Yili Fu, Hongwei Chen, and Shuguo Wang . Design and development of a hand exoskeleton for rehabilitation of hand injuries. Mechanism and Machine Theory , 73:103–116, 2014. 22

    work page 2014