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Tobias F. Beck, Carlo Wilke, Barbara Wirxel, Dominik Endres, Axel Lindner, Martin A. Giese; Me or Not Me: Causal Inference of Agency in goal-directed actions. Journal of Vision 2011;11(11):955. doi: https://doi.org/10.1167/11.11.955.
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The perception of own actions is affected by both visual information and predictions derived from internal forward models . The integration of 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]. The attribution of percepts to consequences of own actions should thus depend on the consistency between internally predicted and actual visual signals.
METHODS. To test this idea, 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. We then asked whether a Bayesian causal inference model, which assumes a latent agency variable controlling the attributed influence of the own action on perceptual consequences [2,3], accounts for the empirical data: if the visual stimulus was attributed to the own action, visual and internal information should fuse in a Bayesian optimal manner, while this should not be the case if the percept was attributed to external influences. The model assumes that both the visual feedback and the internal estimate are directly caused by the (unobserved) real motor state.
RESULTS AND CONCLUSION. The model correctly predicts the data, showing that small deviations between predicted and actual visual information were attributed to one's own action. This was not the case for large deviations, where subjects relied more on internal information. We discuss the performance of this causal inference model in comparison to alternative biologically feasible statistical models, applying methods for Bayesian model comparison.
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