Modeling perspective-taking upon observation of 3D biological motion

Abstract

It appears that the mirror neuron system plays a crucial role when learning by imitation. However, it remains unclear how mirror neuron properties develop in the first place. A likely prerequisite for developing mirror neurons may be the capability to transform observed motion into a sufficiently self-centered frame of reference. We propose an artificial neural network (NN) model that implements such a transformation capability by a highly embodied approach: The model first learns to correlate and predict self-induced motion patterns by associating egocentric visual and proprioceptive perceptions. Once these predictions are sufficiently accurate, a robust and invariant recognition of observed biological motion becomes possible by allowing a self-supervised, error-driven adaption of the visual frame of reference. The NN is a modified, dynamic, adaptive resonance model, which features self-supervised learning and adjustment, neural field normalization, and information-driven neural noise adaptation. The developed architecture is evaluated with a simulated 3D humanoid walker with 12 body landmarks and 10 angular DOF. The model essentially shows how an internal frame of reference adaptation for deriving the perspective of another person can be acquired by first learning about the own bodily motion dynamics and by then exploiting this self-knowledge upon the observation of other, relative, biological motion patterns. The insights gained by the model may have significant implications for the development of social capabilities and respective impairments.