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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.

Critical features for the recognition of biological motion