Experimental Brain Research

, Volume 200, Issue 3–4, pp 259–267 | Cite as

Predicted sensory feedback derived from motor commands does not improve haptic sensitivity

  • Alessandra SciuttiEmail author
  • Valentina Squeri
  • Monica Gori
  • Lorenzo Masia
  • Giulio Sandini
  • Jürgen Konczak
Research Article


Haptic perception is based on the integration of afferent proprioceptive and tactile signals. A further potential source of information during active touch is predicted sensory feedback (PSF) derived from a copy of efferent motor commands that give rise to the exploratory actions. There is substantial evidence that PSF is important for predicting the sensory consequences of action, but its role in perception is unknown. Theoretically, PSF leads to a higher redundancy of haptic information, which should improve sensitivity of the haptic sense. To investigate the effect of PSF on haptic precision, blindfolded subjects haptically explored the curved contour of a virtual object generated by a robotic manipulandum. They either actively moved their hand along the contour, or their hand was moved passively by the device along the same contour. In the active condition afferent sensory information and PSF were present, while in the passive condition subjects relied solely on afferent information. In each trial, two stimuli of different curvature were presented. Subjects needed to indicate which of the two was more “curved” (forced choice). For each condition, the detection and three discrimination thresholds were computed. The main finding is that absence of efference copy information did not systematically degrade haptic acuity. This indirectly implies that PSF does not aid or enhance haptic perception. We conclude that when maximum haptic sensitivity is required to explore novel objects, the perceptual system relies primarily on afferent tactile and proprioceptive information, and PSF has no added effect on the precision of the perceptual estimate.


Efference copy Forward models Human Multisensory integration Proprioception Tactile 


  1. Adamovich SV, Berkinblit MB, Fookson O, Poizner H (1998) Pointing in 3D space to remembered targets. I. Kinesthetic versus visual target presentation. J Neurophysiol 79:2833–2846PubMedGoogle Scholar
  2. Angel RW, Malenka RC (1982) Velocity-dependent suppression of cutaneous sensitivity during movement. Exp Neurol 77:266–274CrossRefPubMedGoogle Scholar
  3. Chapman CE, Bushnell MC, Miron D, Duncan GH, Lund JP (1987) Sensory perception during movement in man. Exp Brain Res 68:516–524CrossRefPubMedGoogle Scholar
  4. Collins DF, Cameron T, Gillard DM, Prochazka A (1998) Muscular sense is attenuated when humans move. J Physiol 508(Pt 2):635–643CrossRefPubMedGoogle Scholar
  5. Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman & Hall, LondonGoogle Scholar
  6. Ehrich JM, Flanders M, Soechting JF (2008) Factors influencing haptic perception of complex shapes. IEEE Trans Haptics 1:19–26CrossRefPubMedGoogle Scholar
  7. Ernst MO, Banks MS (2002) Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415:429–433CrossRefPubMedGoogle Scholar
  8. Evarts EV (1971) Central control of movement. V. Feedback and corollary discharge: a merging of the concepts. Neurosci Res Program Bull 9:86–112PubMedGoogle Scholar
  9. Fechner GT (1889) Elemente der Psychophysik. Breitkopf & Härtel, LeipzigGoogle Scholar
  10. Feinberg I (1978) Efference copy and corollary discharge: implications for thinking and its disorders. Schizophr Bull 4:636–640PubMedGoogle Scholar
  11. Flanagan JR, Wing AM (1997) The role of internal models in motion planning and control: evidence from grip force adjustments during movements of hand-held loads. J Neurosci 17:1519–1528PubMedGoogle Scholar
  12. Gori M, Viva MD, Sandini G, Burr DC (2008) Young children do not integrate visual and haptic form information. Curr Biol 18:694–698CrossRefPubMedGoogle Scholar
  13. Gritsenko V, Krouchev NI, Kalaska JF (2007) Afferent input, efference copy, signal noise, and biases in perception of joint angle during active versus passive elbow movements. J Neurophysiol 98:1140–1154CrossRefPubMedGoogle Scholar
  14. Henriques DYP, Soechting JF (2005) Approaches to the study of haptic sensing. J Neurophysiol 93:3036–3043CrossRefPubMedGoogle Scholar
  15. Jordan MI, Rumelhart DE (1992) Forward models: supervised learning with a distal teacher. Cogn Sci 16:307–354CrossRefGoogle Scholar
  16. Kawato M (1999) Internal models for motor control and trajectory planning. Curr Opin Neurobiol 9:718–727CrossRefPubMedGoogle Scholar
  17. Kawato M, Furukawa K, Suzuki R (1987) A hierarchical neural-network model for control and learning of voluntary movement. Biol Cybern 57:169–185CrossRefPubMedGoogle Scholar
  18. Kawato M, Kuroda T, Imamizu H, Nakano E, Miyauchi S, Yoshioka T (2003) Internal forward models in the cerebellum: fMRI study on grip force and load force coupling. Prog Brain Res 142:171–188CrossRefPubMedGoogle Scholar
  19. Konczak J, Li K-Y, Tuite PJ, Poizner H (2008) Haptic perception of object curvature in Parkinson’s disease. PLoS One 3:e2625CrossRefPubMedGoogle Scholar
  20. Laufer Y, Hocherman S, Dickstein R (2001) Accuracy of reproducing hand position when using active compared with passive movement. Physiother Res Int 6:65–75CrossRefPubMedGoogle Scholar
  21. Lillis KP, Scheidt RA (2003) Sensitivity to hand path curvature during reaching. In: 25th international conference of IEEE EMBS, vol 2, Cancun, Mexico, pp 1754–1757Google Scholar
  22. Lönn J, Crenshaw AG, Djupsjöbacka M, Pedersen J, Johansson H (2000) Position sense testing: influence of starting position and type of displacement. Arch Phys Med Rehabil 81:592–597CrossRefPubMedGoogle Scholar
  23. MacKay DM (1966) Brain and conscious experience. In: Eccles JC (ed), Springer, New York, pp 422–445Google Scholar
  24. Mehta B, Schaal S (2002) Forward models in visuomotor control. J Neurophysiol 88:942–953PubMedGoogle Scholar
  25. Miall R, Weir D, Wolpert D, Stein J (1993) Is the cerebellum a Smith predictor? J Mot Behav 25:203–216PubMedGoogle Scholar
  26. Scarchilli K, Vercher JL (1999) The oculomanual coordination control center takes into account the mechanical properties of the arm. Exp Brain Res 124:42–52CrossRefPubMedGoogle Scholar
  27. Soechting JF, Poizner H (2005) The use of motion cues in the haptic sense of circularity. Exp Brain Res 165:413–421CrossRefPubMedGoogle Scholar
  28. van Beers RJ, Baraduc P, Wolpert DM (2002) Role of uncertainty in sensorimotor control. Philos Trans R Soc Lond B Biol Sci 357:1137–1145CrossRefPubMedGoogle Scholar
  29. Vaziri S, Diedrichsen J, Shadmehr R (2006) Why does the brain predict sensory consequences of oculomotor commands? Optimal integration of the predicted and the actual sensory feedback. J Neurosci 26:4188–4197CrossRefPubMedGoogle Scholar
  30. Von Helmholtz H (1925) Physiological optics. Banta Publishing Co, MenashaGoogle Scholar
  31. von Holst E, Mittelstaedt H (1950) Das Reafferenzprinzip. Naturwissenschaften 37:464–476CrossRefGoogle Scholar
  32. Voss M, Ingram JN, Haggard P, Wolpert DM (2006) Sensorimotor attenuation by central motor command signals in the absence of movement. Nat Neurosci 9:26–27CrossRefPubMedGoogle Scholar
  33. Watson AB, Pelli DG (1983) QUEST: a Bayesian adaptive psychometric method. Percept Psychophys 33:113–120PubMedGoogle Scholar
  34. Wolpert D, Kawato M (1998) Multiple paired forward and inverse models for motor control. Neural Netw 11:1317–1329CrossRefPubMedGoogle Scholar
  35. Wolpert DM, Ghahramani Z, Jordan MI (1995) An internal model for sensorimotor integration. Science 269:1880–1882CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Alessandra Sciutti
    • 1
    • 2
    Email author
  • Valentina Squeri
    • 1
    • 2
  • Monica Gori
    • 1
  • Lorenzo Masia
    • 1
  • Giulio Sandini
    • 1
  • Jürgen Konczak
    • 1
    • 3
  1. 1.Department of Robotics, Brain and Cognitive SciencesItalian Institute of TechnologyGenoaItaly
  2. 2.Dipartimento di Informatica Sistemistica e TelematicaUniversity of GenovaGenoaItaly
  3. 3.Human Sensorimotor Control LaboratoryUniversity of MinnesotaMinneapolisUSA

Personalised recommendations