Comparison of Causal Inference Models for Agency attribution in goal-directed actions T F Beck1, B Wirxel2, C Wilke2, D Endres1, A Lindner2, M A Giese1 |
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1Computational Sensomotorics, HIH,CIN,BCCN, University Clinic Tuebingen, Germany |
| Perception of own actions is influenced by visual information and predictions from internal forward models[1]. Integrating these sources depends on associating visual consequences with one's own action (sense of agency) or with unrelated changes in the external world[2]. Attribution of percepts to consequences of own actions should rely on the consistency between predicted and actual visual signals. We investigate whether the data supports binary [3] or continuous[4] 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. In previous work[3] we showed that a causal inference model, assuming a binary latent agency variable, accounted for the empirical agency data. New models assuming continuous attribution of visual feedback to own action are presented and their prediction performance evaluated and compared to the binary model[2]. Results and Conclusion: The models correctly predict empirical agency ratings. We discuss their performance, applying methods for model comparison. [1]Wolpert et al.,Science,269,1995. [2]Körding et al.,PLoS ONE,2(9),2007. [3]Beck et al.,JVis,11(11):955,2011. [4]Wilke et al.,PLoS ONE,8(1):e54925,2013. Acknowledgements: This work was supported by: BMBF FKZ:01GQ1002, EC FP7-ICT grants TANGO 249858, AMARSi 248311, and DFG GI 305/4-1, DFG GZ:KA 1258/15-1. |
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