Critical features for the recognition of biological motion

Humans can perceive the motion of living beings from very impoverished stimuli like point-light displays. How the visual system achieves the robust generalization from normal to point-light stimuli remains an unresolved question. We present evidence on multiple levels demonstrating that this generalization might be accomplished by an extraction of simple mid-level optic flow features within coarse spatial arrangement, potentially exploiting relatively simple neural circuits: (1) A statistical analysis of the most informative mid-level features reveals that normal and point-light walkers share very similar dominant local optic flow features. (2) We devise a novel point-light stimulus (critical features stimulus) that contains these features, and which is perceived as a human walker even though it is inconsistent with the skeleton of the human body. (3) A neural model that extracts only these critical features accounts for substantial recognition rates for strongly degraded stimuli. We conclude that recognition of biological motion might be accomplished by detecting mid-level optic flow features with relatively coarse spatial localization. The computationally challenging reconstruction of precise position information from degraded stimuli might not be required.