July 2013
Volume 13, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   July 2013
Me - Not Me - Or In Between? Comparison of Causal Inference Models for Agency attribution in goal-directed actions
Author Affiliations
  • Tobias F Beck
    Dept. of Cognitive Neurology, Hertie Institute for Clinical Brain Research and Centre for Integrative Neuroscience, Tübingen, Germany\nSection Computational Sensomotorics
  • Carlo Wilke
    Dept. of Cognitive Neurology, Hertie Institute for Clinical Brain Research and Centre for Integrative Neuroscience, Tübingen, Germany
  • Barbara Wirxel
    Dept. of Cognitive Neurology, Hertie Institute for Clinical Brain Research and Centre for Integrative Neuroscience, Tübingen, Germany\nBernstein Center for Computational Neuroscience Tübingen
  • Dominik Endres
    Dept. of Cognitive Neurology, Hertie Institute for Clinical Brain Research and Centre for Integrative Neuroscience, Tübingen, Germany\nSection Computational Sensomotorics
  • Axel Lindner
    Dept. of Cognitive Neurology, Hertie Institute for Clinical Brain Research and Centre for Integrative Neuroscience, Tübingen, Germany\nBernstein Center for Computational Neuroscience Tübingen
  • Martin A Giese
    Dept. of Cognitive Neurology, Hertie Institute for Clinical Brain Research and Centre for Integrative Neuroscience, Tübingen, Germany\nSection Computational Sensomotorics
Journal of Vision July 2013, Vol.13, 745. doi:10.1167/13.9.745
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      Tobias F Beck, Carlo Wilke, Barbara Wirxel, Dominik Endres, Axel Lindner, Martin A Giese; Me - Not Me - Or In Between? Comparison of Causal Inference Models for Agency attribution in goal-directed actions. Journal of Vision 2013;13(9):745. doi: 10.1167/13.9.745.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Perception of own actions is influenced by visual information and predictions from internal forward models [1]. Integrating these information sources depends critically on whether visual consequences are associated with one's own action (sense of agency) or with changes in the external world unrelated to the action [2,3] and the accuracy of integrated signals [4,5]. Attribution of percepts to consequences of own actions should thus depend on the consistency between internally predicted and actual visual signals, but what does the data support: binary (me vs not me)[6] or continuous (partially me) attribution? METHODS. To examine this question, we used a virtual-reality setup to manipulate the consistency between pointing movements and their visual consequences and investigated the influence of this manipulation on self-action perception with visual stimuli of varying precision. In previous work we showed that a Bayesian causal inference model, assuming a binary latent agency variable controlling the attributed influence of the self-action on perceptual consequences [2,3], accounted for the empirical data [6]. Here, new models assuming a continuous variable for attribution of the visual feedback to own action are presented and their performance predicting the empirical data evaluated and compared to the binary model [2,3]. The models assume both visual feedback and internal estimate are directly caused by the (unobserved) real motor state. RESULTS AND CONCLUSION. The models correctly predict empirical agency ratings, showing attribution of visual signals to self-action for small, and stronger reliance on internal information for large deviations. We discuss the performance of these causal inference models, applying methods for model comparison. [1] Wolpert et al., Science, 269, 1995. [2] Körding et al., PLoS ONE, 2(9), 2007. [3] Shams & Beierholm, TiCS, 14, 2010. [4] Alais & Burr, CurBio, 14, 2004. [5] Burge et al., JVis, 8(4), 2008. [6] Beck et al., JVis, 11(11): 955, 2011.

Meeting abstract presented at VSS 2013

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