# Optimal local estimates of visual motion in a natural environment

## TL;DR

This study develops an optimal visual motion estimator based on natural environment data, revealing that neural response errors may be due to optimal adaptation to environmental signals.

## Contribution

It introduces a method to derive the optimal motion estimator from natural scene data, linking neural errors to environmental signal properties.

## Key findings

- Optimal estimator matches neural response errors
- Errors include velocity-contrast confounding
- Results suggest sensory errors are environmentally optimal

## Abstract

Many organisms, from flies to humans, use visual signals to estimate their motion through the world. To explore the motion estimation problem, we have constructed a camera/gyroscope system that allows us to sample, at high temporal resolution, the joint distribution of input images and rotational motions during a long walk in the woods. From these data we construct the optimal estimator of velocity based on spatial and temporal derivatives of image intensity in small patches of the visual world. Over the bulk of the naturally occurring dynamic range, the optimal estimator exhibits the same systematic errors seen in neural and behavioral responses, including the confounding of velocity and contrast. These results suggest that apparent errors of sensory processing may reflect an optimal response to the physical signals in the environment.