Abstract

Animals detect motion using a variety of visual cues that reflect regularities in the natural world. Experiments in animals across phyla have shown that motion percepts incorporate both pairwise and triplet spatiotemporal correlations that could theoretically benefit motion computation. However, it remains unclear how visual systems assemble these cues to build accurate motion estimates. Here, we used systematic behavioral measurements of fruit fly motion perception to show how flies combine local pairwise and triplet correlations to reduce variability in motion estimates across natural scenes. By generating synthetic images with statistics controlled by maximum entropy distributions, we show that the triplet correlations are useful only when images have light-dark asymmetries that mimic natural ones. This suggests that asymmetric ON-OFF processing is tuned to the particular statistics of natural scenes. Since all animals encounter the world's light-dark asymmetries, many visual systems are likely to use asymmetric ON-OFF processing to improve motion estimation.

Highlights

  • For any visual system, motion estimation is an important but computationally challenging task

  • It remains unclear how visual systems use the statistics of natural scenes and the motion signals in them to aid in motion estimation (Salisbury and Palmer, 2016; Sinha et al, 2018)

  • To evaluate how canonical motion detectors performed with natural scene inputs, we simulated responses of the Hassenstein-Reichardt Correlator (HRC) to rigidly translating natural scenes

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Summary

Introduction

Motion estimation is an important but computationally challenging task. Higher order correlations could contribute to motion computation, and Bayes optimal visual motion estimators can be written as a sum of terms specialized for detecting different correlation types (Potters and Bialek, 1994; Fitzgerald et al, 2011). This mathematical result follows from a Volterra series expansion, which provides a general and systematic way to represent nonlinear computational systems.

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