# A naturalistic visual task for studying distance estimation

> **NIH NIH R21** · UNIVERSITY OF OREGON · 2022 · $171,598

## Abstract

PROJECT SUMMARY
Vision facilitates navigation through the world by providing sensory information about the environment,
such as the distance to relevant objects. A key feature of visual perception is the active exploration of
the visual scene through translation of the eyes, head, and body. Visual cortex has been proposed to
combine these self-generated motor signals with visual input to compute information about objects in
the environment. While recent studies have shown that a significant fraction of neurons in mouse V1
encode movement information and do not simply act as visual feature detectors, models of V1 function
have largely ignored motor efference and sensory reafferent contributions. We aim to elucidate the
neural mechanisms underlying active vision by investigating depth perception from motion parallax - a
fundamental visual computation that combines observer self-motion and retinal image displacement to
calculate the distance to objects in the environment.
We will first adapt an ethological, freely-moving gerbil/rat distance estimation task to mice in order to
determine the types of visual cues mice use to gauge depth when jumping across a gap. We will then
manipulate the activity of visual cortex and its inputs from brain regions conveying movement-related
signals, in order to test their roles specifically in distance estimation from motion parallax. The
experiments proposed in this R21 application provide the foundation for future studies at the neural
circuit level, to determine how visual and movement signals are integrated for computations such as
distance estimation, particularly in a natural context.

## Key facts

- **NIH application ID:** 10415984
- **Project number:** 5R21EY032708-02
- **Recipient organization:** UNIVERSITY OF OREGON
- **Principal Investigator:** Cristopher M Niell
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $171,598
- **Award type:** 5
- **Project period:** 2021-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10415984

## Citation

> US National Institutes of Health, RePORTER application 10415984, A naturalistic visual task for studying distance estimation (5R21EY032708-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10415984. Licensed CC0.

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