Sighted animals extract motion information from visual scenes by processing spatiotemporal patterns of light falling on the retina. The dominant models for motion estimation exploit intensity correlations only between pairs of points in space and time. Moving natural scenes, however, contain more complex correlations. We found that fly and human visual systems encode the combined direction and contrast polarity of moving edges using triple correlations that enhance motion estimation in natural environments. Both species extracted triple correlations with neural substrates tuned for light or dark edges, and sensitivity to specific triple correlations was retained even as light and dark edge motion signals were combined. Thus, both species separately process light and dark image contrasts to capture motion signatures that can improve estimation accuracy. This convergence argues that statistical structures in natural scenes have greatly affected visual processing, driving a common computational strategy over 500 million years of evolution.
Bibliographical noteFunding Information:
We thank J. Burge, P. Varghese, B. Wandell and members of the Clandinin laboratory for helpful comments on this manuscript. D.A.C. was supported by an US National Institutes of Health T32 Vision Research Training grant and a postdoctoral fellowship from the Jane Coffin Childs Foundation. J.E.F. was supported by a National Science Foundation Graduate Research Fellowship and by NSF-0801700. J.M.A. and A.M.N. were supported by a grant from the US National Institutes of Health (EY015790). D.M.G. was supported by a post-doctoral fellowship from the US National Institutes of Health, and M.A.S. was supported by a postdoctoral fellowship from the Jane Coffin Childs Foundation. In T.R.C.’s laboratory, this work was supported by US National Institutes of Health Director’s Pioneer Award (DP1 OD003530) and by R01 EY022638.