• DocumentCode
    1637914
  • Title

    Perception during interaction is not based on statistical context

  • Author

    Sciutti, Alessandra ; Del Prete, Andrea ; Natale, L. ; Sandini, G. ; Gori, Marco ; Burr, D.

  • Author_Institution
    RBCS, Ist. Italiano di Tecnol., Genoa, Italy
  • fYear
    2013
  • Firstpage
    225
  • Lastpage
    226
  • Abstract
    To evaluate the central tendency we plotted the reproduced lengths against the presented length and measured the regression index, defined as the difference in slope between the best linear fit of the data and the identity line. This index varies from 0 (veridical performance) to 1 (complete regression to the mean). In the control "alone" conditions, all subjects showed on average a clear central tendency mechanism (average regression index 0.417 ± 0.028) which decreased significantly when the task was performed with the robot (0.156 ± 0.028, one-tailed, pair-sample t-test, p<;0.01). Part of this common decrease can be explained by the difference in the richness of the two kinds of stimuli: on the one hand just two brief flashes of lights represented the length to be reproduced, while in both robotics conditions the whole arm motion was visible, providing a richer stimulation. Indeed, according to the Bayesian models described in [1,4] the presence of less sensory noise would yield to less regression to the mean. However, the amount of change in regression was different between the two robotic groups.
  • Keywords
    Bayes methods; dexterous manipulators; human-robot interaction; regression analysis; robot vision; visual perception; Bayesian models; arm motion; central tendency mechanism; regression index; robotic conditions; robotic groups; sensory noise; statistical context; Accuracy; Context; Humanoid robots; Indexes; Presses; Robot sensing systems; Human-robot interaction; regression to the mean; robot autonomy; robot gaze behavior;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Human-Robot Interaction (HRI), 2013 8th ACM/IEEE International Conference on
  • Conference_Location
    Tokyo
  • ISSN
    2167-2121
  • Print_ISBN
    978-1-4673-3099-2
  • Electronic_ISBN
    2167-2121
  • Type

    conf

  • DOI
    10.1109/HRI.2013.6483583
  • Filename
    6483583