Distinguishing Self, Other, and Autonomy From Visual Feedback: A Combined Correlation and Acceleration Transfer Analysis - Demirel, Berkay; Moulin-Frier, Clément; Arsiwalla, Xerxes D.; Verschure, Paul F. M. J.; Sánchez-Fibla, Martí (Frontiers in Human Neuroscience, 2021)
In cognitive science, Theory of Mind (ToM) is the mental faculty of assessing intentions and beliefs of others and requires, in part, to distinguish incoming sensorimotor signals and accordingly attribute these to either the self-model, the model of the other, or one pertaining to the external world, including inanimate objects. To gain an understanding of this mechanism, we perform a computational analysis of sensorimotor interactions in a dual-arm robotic setup. Our main contribution is that, under the common fate principle, a correlation analysis of the velocities of visual pivots is shown to be sufficient to characterize "the self" (including proximo-distal arm-joint dependencies) and assess motor to sensory influences, and "the other" by computing clusters in the correlation dependency graph. A correlational analysis, however, is not sufficient to assess the non-symmetric/directed dependencies required to infer autonomy, the ability of entities to move by themselves. We subsequently validate 3 measures that can potentially quantify a metric for autonomy: Granger Causality, Transfer Entropy, as well as a novel `Acceleration Transfer' (AT) measure, which is an instantaneous measure that computes the estimated instantaneous transfer of acceleration between visual features, from which one can compute a directed sensorimotor graph. Subsequently, autonomy is characterized by the sink nodes in this directed graph. Our results show that although Transfer Entropy is able to capture the directional dependencies, a rectified subtraction operation denoted here as AT is both sufficient and computationally cheaper.