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

Alessandra Sciutti, Valentina Squeri, Monica Gori, Lorenzo Masia, Giulio Sandini, Jürgen Konczak

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26 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)259-267
Number of pages9
JournalExperimental Brain Research
Issue number3-4
StatePublished - Jan 2010


  • Efference copy
  • Forward models
  • Human
  • Multisensory integration
  • Proprioception
  • Tactile


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