Neural model for the visual recognition of animacy and social interaction

Abstract

Humans reliably attribute social interpretations and agency to highly impoverished stimuli, such as interacting geometrical shapes. While it has been proposed that this capability is based on high-level cognitive processes, such as probabilistic reasoning, we demonstrate that it might be accounted for also by rather simple physiologically plausible neural mechanisms. Our model is a hierarchical neural network architecture with two pathways that analyze form and motion features. The highest hierarchy level contains neurons that have learned combinations of relative position-, motion-, and body-axis features. The model reproduces psychophysical results on the dependence of perceived animacy on motion smoothness and the orientation of the body axis. In addition, the model correctly classifies six categories of social interactions that have been frequently tested in the psychophysical literature. For the generation of training data we propose a novel algorithm that is derived from dynamic human navigation models, and which allows to generate arbitrary numbers of abstract social interaction stimuli by self-organization.