Flies and humans share a motion estimation strategy that exploits natural scene statistics

Damon A Clark, James E Fitzgerald, Justin M Ales, Daryl M Gohl, Marion A Silies, Anthony M Norcia, Thomas R Clandinin

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)296-303
Number of pages8
JournalNature Neuroscience
Volume17
Issue number2
Early online date5 Jan 2014
DOIs
Publication statusPublished - Feb 2014

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