Skip to main content
SpringerLink
Log in

Human Control of Interactions with Objects – Variability, Stability and Predictability

  • Chapter
  • First Online:
Geometric and Numerical Foundations of Movements

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 117))

Abstract

How do humans control their actions and interactions with the physical world? How do we learn to throw a ball or drink a glass of wine without spilling? Compared to robots human dexterity remains astonishing, especially as slow neural transmission and high levels of noise seem to plague the biological system. What are human control strategies that skillfully navigate, overcome, and even exploit these disadvantages? To gain insight we propose an approach that centers on how task dynamics constrain and enable (inter-)actions. Agnostic about details of the controller, we start with a physical model of the task that permits full understanding of the solution space. Rendering the task in a virtual environment, we examine how humans learn solutions that meet complex task demands. Central to numerous skills is redundancy that allows exploration and exploitation of subsets of solutions. We hypothesize that humans seek solutions that are stable to perturbations to make their intrinsic noise matter less. With fewer corrections necessary, the system is less afflicted by long delays in the feedback loop. Three experimental paradigms exemplify our approach: throwing a ball to a target, rhythmic bouncing of a ball, and carrying a complex object. For the throwing task, results show that actors are sensitive to the error-tolerance afforded by the task. In rhythmic ball bouncing, subjects exploit the dynamic stability of the paddle-ball system. When manipulating a “glass of wine”, subjects learn strategies that make the hand-object interactions more predictable. These findings set the stage for developing propositions about the controller: We posit that complex actions are generated with dynamic primitives, modules with attractor stability that are less sensitive to delays and noise in the neuro-mechanical system.

Submitted to: Laumond, J.-P. Geometric and Numerical Foundations of Movement.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. M. Abe, D. Sternad, Directionality in distribution and temporal structure of variability in skill acquisition. Front. Hum. Neurosci. 7 (2013). doi:10.3389/fnhum.2013.002

  2. D. Angelaki, Y. Gu, G. Deangelis, Multisensory integration: psychophysics, neurophysiology, and computation. Current Opin. Neurobiol. 19, 452–458 (2009)

    Article  Google Scholar 

  3. B. Berret, F. Jean, Why don’t we move slower? The value of time in the neural control of action. J. Neurosci. 36, 1056–1070 (2016)

    Article  Google Scholar 

  4. E. Bizzi, N. Accornero, W. Chapple, N. Hogan, Posture control and trajectory formation during arm movements. J. Neurosci. 4, 2738–2744 (1984)

    Google Scholar 

  5. W. Chu, S.-W. Park, T. Sanger, D. Sternad, Dystonic children can learn a novel motor skill: strategies that are tolerant to high variability. IEEE Trans. Neural Syst. Rehabil. Eng. 24(8), 847–858 (2016)

    Google Scholar 

  6. W. Chu, D. Sternad, T. Sanger, Healthy and dystonic children compensate for changes in motor variability. J. Neurophysiol. 109, 2169–2178 (2013)

    Article  Google Scholar 

  7. R.G. Cohen, D. Sternad, Variability in motor learning: relocating, channeling and reducing noise. Exp. Brain Res. 193, 69–83 (2009)

    Article  Google Scholar 

  8. R.G. Cohen, D. Sternad, State space analysis of intrinsic timing: exploiting task redundancy to reduce sensitivity to timing. J. Neurophysiol. 107, 618–627 (2012)

    Article  Google Scholar 

  9. T.M. Cover, J.A. Thomas, Elements of Information Theory (Wiley, Hoboken, 2006)

    Google Scholar 

  10. J.P. Cusumano, P. Cesari, Body-goal variability mapping in an aiming task. Biol. Cybern. 94, 367–379 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  11. T.M.H. Dijkstra, H. Katsumata, A. de Rugy, D. Sternad, The dialogue between data and model: passive stability and relaxation behavior in a ball bouncing task. Nonlinear Stud. 11, 319–345 (2004)

    MathSciNet  MATH  Google Scholar 

  12. A.M. Dollar, R.D. Howe, Towards grasping in unstructured environments: grasper compliance and configuration optimization. Adv. Robot. 19, 523–543 (2005)

    Article  Google Scholar 

  13. A.A. Faisal, L.P. Selen, D.M. Wolpert, Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008)

    Article  Google Scholar 

  14. A.G. Feldman, Functional tuning of the nervous system with control of movement or maintenance of a steady posture: II) Controllable parameters of the muscle. Biophysics 11, 565–578 (1966a)

    Google Scholar 

  15. A.G. Feldman, Functional tuning of the nervous system with control of movement or maintenance of a steady posture: III) Mechanographic analysis of execution by man of the simplest motor task. Biophysics 11, 667–675 (1966b)

    Google Scholar 

  16. P.M. Fitts, The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47, 381–391 (1954)

    Article  Google Scholar 

  17. P.M. Fitts, J.R. Peterson, Information capacity of discrete motor responses. J. Exp. Psychol. 67, 103–112 (1964)

    Article  Google Scholar 

  18. T. Flash, A.A. Handzel, Affine differential geometry analysis of human arm movements. Biol. Cybern. 96, 577–601 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  19. M. Franek, J. Mates, T. Radil, K. Beck, E. Pöppel, Finger tappping in musicians and non-musicians. Int. J. Psychophysiol. 11, 277–279 (1991)

    Article  Google Scholar 

  20. H. Gomi, M. Kawato, Equilibrium-point control hypothesis examined by measured arm stiffness during multijoint movement. Science 272, 117–220 (1996)

    Google Scholar 

  21. J. Guckenheimer, P. Holmes, Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields (Springer, New York, 1983)

    Book  MATH  Google Scholar 

  22. H. Haken, J.A.S. Kelso, H. Bunz, A theoretical model of phase transition in human hand movements. Biol. Cybern. 51, 347–356 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  23. C. Hasson, T. Shen, D. Sternad, Energy margins in dynamic object manipulation. J. Neurophysiol. 108, 1349–1365 (2012)

    Article  Google Scholar 

  24. C. Hasson, D. Sternad, Safety margins in older adults increase with improved control of a dynamic object. Fronti. Aging Neurosci. 6 (2014). doi:10.3389/fnagi.2014.00158

  25. N. Hogan, An organizing principle for a class of voluntary movements. J. Neurosci. 4, 2745–2754 (1984)

    Google Scholar 

  26. N. Hogan, D. Sternad, On rhythmic and discrete movements: reflections, definitions and implications for motor control. Exp. Brain Res. 18, 13–30 (2007)

    Article  Google Scholar 

  27. N. Hogan, D. Sternad, Sensitivity of smoothness measures to movement duration, amplitude, and arrests. J. Motor Behavior 41, 529–534 (2009)

    Article  Google Scholar 

  28. N. Hogan, D. Sternad, Dynamic primitives of motor behavior. Biol. Cybern. 106, 727–739 (2012)

    Article  MathSciNet  Google Scholar 

  29. N. Hogan, D. Sternad, Dynamic primitives in the control of locomotion. Front. Comput. Neurosci. 7 (2013). doi:10.3389/fncom.2013.00071

  30. M. Huber, D. Sternad, Implicit guidance to stable performance in a rhythmic perceptual-motor skill. Exp. Brain Res. 233, 1783–1799 (2015)

    Article  Google Scholar 

  31. A. Ijspeert, Central pattern generators for locomotion control in animals and robots: a review. Neural Netw. 21, 642–653 (2008)

    Article  Google Scholar 

  32. E.R. Kandel, T.M.J. Schwartz, T.M. Jessel, Principles of Neural Sciences (Elsevier, New York, 1991)

    Google Scholar 

  33. J.A.S. Kelso, Phase transitions and critical behavior in human bimanual coordination. Am. J. Physiol.: Regul. Integr. Comp. Physiol. 15, R1000–R1004 (1984)

    Google Scholar 

  34. J.A.S. Kelso, Elementary coordination dynamics, in Interlimb coordination: Neural, dynamical, and cognitive constraints, ed. by S. Swinnen, H. Heuer, J. Massion P. Casaer (Academic Press, New York, 1994)

    Google Scholar 

  35. I. Kurtzer, J. Pruszynski, S. Scott, Long-latency reflexes of the human arm reflect an internal model of limb dynamics. Current Biol. 18, 449–453 (2008)

    Article  Google Scholar 

  36. M.L. Latash, Control of human movements. Human Kinetics (Urbana, Champaign, IL, 1993)

    Google Scholar 

  37. Z. Li, M. Latash, V. Zatsiorsky, Force sharing among fingers as a model of the redundancy problem. Exp. Brain Res. 119, 276–286 (1998)

    Article  Google Scholar 

  38. H.C. Mayer, R. Krechetnikov, Walking with coffee: why does it spill? Phys. Rev. E 85, 046117 (2012)

    Article  Google Scholar 

  39. B. Mehta, S. Schaal, Forward models in visuomotor control. J. Neurophysiol. 88, 942–953 (2002)

    Google Scholar 

  40. A. Nagengast, D. Braun, D. Wolpert, Optimal control predicts human performance on objects with internal degrees of freedom. PLoS Comput. Biol. 5, e1000419 (2009)

    Article  MathSciNet  Google Scholar 

  41. B. Nasseroleslami, C. Hasson, D. Sternad, Rhythmic manipulation of objects with complex dynamics: predictability over chaos. PLoS Comput. Biol. 10, e1003900 (2014). doi:10.1371/journal.pcbi.1003900

  42. R. Plamondon, A.M. Alimi, Speed/accuracy trade-offs in target-directed movements. Behavior Brain Sci. 20, 1–31 (1997)

    Google Scholar 

  43. E. Robertson, The serial reaction time task: implicit motor skill learning? J. Neurosci. 27, 10073–10075 (2007)

    Article  Google Scholar 

  44. R. Ronsse, D. Sternad, Bouncing between model and data: stability, passivity, and optimality in hybrid dynamics. J. Motor Behavior 6, 387–397 (2010)

    Google Scholar 

  45. R. Ronsse, D. Sternad, P. Lefevre, A computational model for rhythmic and discrete movements in uni- and bimanual coordination. Neural Comput. 21, 1335–1370 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  46. R. Ronsse, K. Wei, D. Sternad, Optimal control of cyclical movements: the bouncing ball revisited. J. Neurophysiol. 103, 2482–2493 (2010)

    Article  Google Scholar 

  47. J. Rothwell, Control of Human Voluntary Movement (Springer, New York, 2012)

    Google Scholar 

  48. T. Sanger, Risk-aware control. Neural Comput. 26, 2669–2691 (2014)

    Article  MathSciNet  Google Scholar 

  49. A. Sauret, F. Boulogne, J. Cappello, E. Dressaire, H. Stone, Damping of liquid sloshing by foams: from everyday observations to liquid transport. Phys. Fluids 27, 022103 (2015)

    Google Scholar 

  50. S. Schaal, S. Kotosaka, D. Sternad, Nonlinear dynamical systems as movement primitives, in Proceedings of the 1st IEEE-RAS International Conference on Humanoid Robotics (Humanoids 2000), Cambridge, MA, September 7–9 2000

    Google Scholar 

  51. S. Schaal, D. Sternad, Programmable pattern generators, in International Conference on Computational Intelligence in Neuroscience (ICCIN ’98), Research Triangle Park, NC, Oct 24–26 1998

    Google Scholar 

  52. S. Schaal, D. Sternad, Origins and violations of the 2/3 power law. Exp. Brain Res. 136, 60–72 (2001)

    Article  Google Scholar 

  53. S. Schaal, D. Sternad, C.G. Atkeson, One-handed juggling: a dynamical approach to a rhythmic movement task. J. Motor Behavior 28, 165–183 (1996)

    Article  Google Scholar 

  54. S. Schaal, D. Sternad, R. Osu, M. Kawato, Rhythmic arm movement is not discrete. Nature Neurosci. 7, 1136–1143 (2004)

    Article  Google Scholar 

  55. J. Scholz, G. Schöner, The uncontrolled manifold concept: identifying control variables for a functional task. Exp. Brain Res. 126, 289–306 (1999)

    Article  Google Scholar 

  56. S.H. Scott, Optimal feedback control and the neural basis of volitional motor control. Nature Rev. Neurosci. 5, 532–546 (2004)

    Article  Google Scholar 

  57. R. Shadmehr, F.A. Mussa-Ivaldi, Adaptive representation of dynamics during learning of a motor task. J. Neurosci. 14, 3208–3224 (1994)

    Google Scholar 

  58. R. Shadmehr, S.P. Wise, Computational Neurobiology of Reaching and Pointing: A Foundation for Motor Learning (MIT Press, Cambridge, 2005)

    Google Scholar 

  59. D. Sternad, Towards a unified framework for rhythmic and discrete movements: behavioral, modeling and imaging results, in Coordination: Neural, Behavioral and Social Dynamics, ed. by A. Fuchs, V. Jirsa (Springer, New York, 2008)

    Google Scholar 

  60. D. Sternad, From theoretical analysis to clinical assessment and intervention: three interactive motor skills in a virtual environment, in Proceedings of the IEEE International Conference on (ICVR) Virtual Rehabilitation, June 9–12 2015, Valencia, Spain (2015), pp. 265–272

    Google Scholar 

  61. D. Sternad, M.O. Abe, X. Hu, H. Müller, Neuromotor noise, sensitivity to error and signal-dependent noise in trial-to-trial learning. PLoS Comput. Biol. 7, e1002159 (2011)

    Article  Google Scholar 

  62. D. Sternad, D. Collins, M.T. Turvey, The detuning factor in the dynamics of interlimb rhythmic coordination. Biol. Cybern. 73, 27–35 (1995)

    Article  Google Scholar 

  63. D. Sternad, W.J. Dean, Rhythmic and discrete elements in multijoint coordination. Brain Res 989, 151–172 (2003)

    Article  Google Scholar 

  64. D. Sternad, W.J. Dean, S. Schaal, Interaction of rhythmic and discrete pattern generators in single-joint movements. Hum. Mov. Sci. 19, 627–665 (2000a)

    Article  Google Scholar 

  65. D. Sternad, M. Duarte, H. Katsumata, S. Schaal, Dynamics of a bouncing ball in human performance. Phys. Rev. E 63, 011902-1–011902-8 (2000)

    Google Scholar 

  66. D. Sternad, M. Duarte, H. Katsumata, S. Schaal, Bouncing a ball: tuning into dynamic stability. J. Exp. Psychol.: Hum. Percept. Perform. 27, 1163–1184 (2001)

    Google Scholar 

  67. D. Sternad, C. Hasson, Predictability and robustness in the manipulation of dynamically complex objects, Adv. Exp. Med. Biol. 957, 55–77 (2016)

    Google Scholar 

  68. D. Sternad, M.E. Huber, N. Kuznetsov, Acquisition of novel and complex motor skills: stable solutions where intrinsic noise matters less. Adv. Exp. Med. Biol. 826, 101–124 (2014)

    Article  Google Scholar 

  69. D. Sternad, S. Park, H. Müller, N. Hogan, Coordinate dependency of variability analysis. PLoS Comput. Biol. 6, e1000751 (2010)

    Article  Google Scholar 

  70. D. Sternad, M.T. Turvey, E.L. Saltzman, Dynamics of 1:2 coordination in rhythmic interlimb movement: I. Generalizing relative phase. J. Motor Behavior 31, 207–223 (1999)

    Article  Google Scholar 

  71. D. Sternad, M.T. Turvey, R.C. Schmidt, Average phase difference theory and 1:1 phase entrainment in interlimb coordination. Biol. Cybern. 67, 223–231 (1992)

    Article  Google Scholar 

  72. S. Sternberg, R. Knoll, P. Zukovsky, Timing by skilled musicians, in The Psychology of Music (Academic Press, New York, 1982), pp. 181–239

    Google Scholar 

  73. E. Todorov, Optimality principles in sensorimotor control. Nature Neurosci. 7, 907–915 (2004)

    Article  Google Scholar 

  74. E. Todorov, M.I. Jordan, Optimal feedback control as a theory of motor coordination. Nature Neurosci. 5, 1226–1235 (2002)

    Article  Google Scholar 

  75. N.B. Tufillaro, T. Abbott, J. Reilly, An Experimental Approach to Nonlinear Dynamics and Chaos (Redwood City, Addison-Wesley, 1992)

    MATH  Google Scholar 

  76. R. van der Linde, P. Lammertse, HapticMaster - a generic force controlled robot for human interaction. Ind. Robot - An Int. J. 30, 515–524 (2003)

    Article  Google Scholar 

  77. R.P.R.D. van der Wel, D. Sternad, D.A. Rosenbaum, Moving the arm at different rates: Slow movements are avoided. J. Motor Behavior 1, 29–36 (2010)

    Google Scholar 

  78. R. van Ham, T. Sugar, B. Vanderborght, K. Hollander, D. Lefeber, Compliant actuator design. IEEE Robtoics Autom. Mag. 9, 81–94 (2009)

    Article  Google Scholar 

  79. K. Wei, T.M.H. Dijkstra, D. Sternad, Passive stability and active control in a rhythmic task. J. Neurophysiol. 98, 2633–2646 (2007)

    Article  Google Scholar 

  80. K. Wei, K.Körding, Uncertainty of feedback and state estimation determines the speed of motor adaptation. Front. Comput. Neurosci. 4, 11 (2010)

    Google Scholar 

  81. A.M. Wing, A.B. Kristofferson, The timing of interresponse intervals. Percept. Psychophys. 1, 455–460 (1973)

    Article  Google Scholar 

  82. V. Zatsiorsky, R. Gregory, M. Latash, Force and torque production in static multifinger prehension: biomechanics and control. I. Biomech. Biol. Cybern. 87, 50–57 (2002)

    Article  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Institute of Health, R01-HD045639, R01-HD081346, and R01HD087089, and the National Science Foundation DMS-0928587 and EAGER-1548514. I would like to acknowledge all my graduate and postdoctoral students who worked hard to generate this body of research. I would also like to thank Neville Hogan for many inspiring discussions and contributions.

Author information

Authors and Affiliations

  1. Departments of Biology, Electrical and Computer Engineering, and Physics, Center for the Interdisciplinary Research on Complex Systems, Northeastern University, 134 Mugar Life Science Building, 360 Huntington Avenue, Boston, MA, 02115, USA

    Dagmar Sternad

Authors
  1. Dagmar Sternad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dagmar Sternad .

Editor information

Editors and Affiliations

  1. LAAS-CNRS , Toulouse, France

    Jean-Paul Laumond

  2. LAAS-CNRS , Toulouse, France

    Nicolas Mansard

  3. LAAS-CNRS , Toulouse, France

    Jean-Bernard Lasserre

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Sternad, D. (2017). Human Control of Interactions with Objects – Variability, Stability and Predictability. In: Laumond, JP., Mansard, N., Lasserre, JB. (eds) Geometric and Numerical Foundations of Movements . Springer Tracts in Advanced Robotics, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-319-51547-2_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-51547-2_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51546-5

  • Online ISBN: 978-3-319-51547-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation