August 2014
Volume 14, Issue 10
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2014
Active Sampling supported Comparison of Causal Inference Models for Agency Attribution in Goal-Directed Actions
Author Affiliations
  • Tobias F Beck
    Department of Cognitive Neurology, Section for Computational Sensomotorics. University Clinics, BCCN, CIN, HIH Tuebingen
  • Dominik Endres
    Department of Cognitive Neurology, Section for Computational Sensomotorics. University Clinics, BCCN, CIN, HIH Tuebingen
  • Axel Lindner
    Department of Cognitive Neurology, University Clinics, BCCN, CIN Tuebingen
  • Martin A Giese
    Department of Cognitive Neurology, Section for Computational Sensomotorics. University Clinics, BCCN, CIN, HIH Tuebingen
Journal of Vision August 2014, Vol.14, 838. doi:10.1167/14.10.838
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      Tobias F Beck, Dominik Endres, Axel Lindner, Martin A Giese; Active Sampling supported Comparison of Causal Inference Models for Agency Attribution in Goal-Directed Actions. Journal of Vision 2014;14(10):838. doi: 10.1167/14.10.838.

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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] and the accuracy of integrated signals [3]. Attribution of percepts to consequences of own actions depends thus on the consistency between internally predicted and actual visual signals. However, is the attribution of agency rather a binary decision ('I did, or did not cause the visual consequences of the action' [4]), or is this process based on a more gradual attribution of the degree of agency? Both alternatives result in different behaviors of causal inference models, which we try to distinguish by model comparison. METHODS. We used a virtual-reality setup to manipulate the consistency between pointing movements and their visual consequences. We investigated the influence of this manipulation on self-action perception. We compared two Bayesian causal inference models to the experimental data, one with a binary latent agency variable [2], and one with a continuous latent agency variable [4]. Here, subject-specific regions for stimulus conditions that maximally differentiate between the two models were identified online using Active Sampling methods [6] to evaluate relative model evidences with a small number of samples. RESULTS/CONCLUSION. Both models correctly predict the data, and specifically empirical agency ratings showing high attribution of agency for small deviations between sensory and predicted feedback. Some participants show signatures of a binary internal representation of agency. In addition, relationships with other inference models [5] are discussed. [1] Wolpert et al.,Science,269,1995. [2] Körding et al.,PLoS ONE,2(9),2007. Shams&Beierholm,TiCS,14,2010. [3] Burge et al.,JVis,8(4),2008. [4] Beck et al.,Jvis,13(9),2013. [5] Marko et al.,JNPhys,108,2012. Ernst,Jvis,7(5),2007. [6] MacKay,NeuralComp,4(4),1992. Paninski,NeuralComp,17(7),2005.

Meeting abstract presented at VSS 2014

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