# A Framework for Understanding How Humans Perceive the Depth of Moving Objects

> **NIH NIH F30** · UNIVERSITY OF ROCHESTER · 2021 · $51,036

## Abstract

Project Summary
 One of the brain’s major functions is to represent the 3D structure of the world from a sequence of 2D images
projected onto the retinae. During observer translation, the relative image motion between stationary objects at
different distances (motion parallax, MP) provides potent depth information, in the absence of binocular cues.
However, if an object is moving in the world, this complicates the computation of depth from MP since there will
be an additional component of image motion related to the object’s motion in the world. Previous experimental
and theoretical work on depth perception from MP has assumed the objects are stationary in the world. We
propose to use a combination of human psychophysics and computational modelling to address, for the first time,
how humans infer the depth of moving objects during self-motion.
 We consider two ways that the brain might compute the depth of moving objects from MP. First, if the brain
can accurately parse retinal image motion into components related to self-motion and object motion, then depth
can be computed from the component of image motion that is caused by self-motion. In Aim 1, we test this
hypothesis by asking subjects to judge the depth sign (near vs. far) of objects that are moving or stationary in
the world. We hypothesize that subjects’ depth judgements will be biased by object motion in the world, since
recent studies suggest that flow parsing is not completely accurate. Our preliminary data support this hypothesis.
Second, we consider that the brain may not be able to accurately isolate the component of image motion caused
by self-motion because there is uncertainty in inferring whether or not objects are moving in the world. This leads
us to hypothesize that the biases observed in Aim 1 can be explained by considering perception as joint inference
of both depth and object motion in the world. In Aim 2, we test this hypothesis by asking subjects to answer two
questions: 1) is the object moving or stationary in the world? 2) is the object farther or nearer than the fixation
point? We hypothesize that depth estimates will depend on the subject’s belief about object motion in the world,
and should also depend systematically on the reliability of depth cues. Our preliminary results support predictions
of a causal inference scheme, and we will compare the data to predictions of a Bayesian ideal observer model.
 This fellowship will provide the candidate training in computational and systems neuroscience through
interactions with the research mentor, the broader neuroscience community at the University of Rochester, and
formal coursework. The proposed research is consistent with NEI goals to “understand how the brain processes
visual information” (National Plan for Eye and Vision Research). In addition, the knowledge gained from this work
may help us better understand the various neural and ophthalmological diseases that affect depth perception,
and assist in the development...

## Key facts

- **NIH application ID:** 10076550
- **Project number:** 5F30EY031183-02
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** Ranran Li French
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $51,036
- **Award type:** 5
- **Project period:** 2020-01-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10076550

## Citation

> US National Institutes of Health, RePORTER application 10076550, A Framework for Understanding How Humans Perceive the Depth of Moving Objects (5F30EY031183-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10076550. Licensed CC0.

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