# Establishing the Limits of Perceptual Inference for Visual Motion

> **NIH NIH F32** · UNIVERSITY OF CALIFORNIA BERKELEY · 2022 · $69,802

## Abstract

Learned statistics about the world play an important role in dictating our sensory perception. When incoming
sensory inputs carry limited information, such as in low-contrast conditions like dusk, percepts appear to be
heavily dictated by implicit assumptions about the probability of different sensory experiences. More
specifically, contemporary research suggests that human visual motion perception is well-described by an ideal
observer model that gathers environmental information about motion, but also assumes that objects in the
environment are most likely either stationary or moving relatively slowly. Theoretical work implementing this
model, referred to as a Bayesian observer with a "slow speed prior", has successfully explained many
disparate perceptual studies that found curious biases in perceived motion, and has had far-reaching influence
on how we think about human spatial vision. As useful as this slow speed prior hypothesis is, it makes several
critical, untested assumptions: namely that the visual system represents a motion prior in an accurate, world-
based coordinate system. While this is ideal for a Bayesian observer, it is at odds with evidence from the
psychophysical literature on visual motion perception. It is also unclear how flexible this prior is in adults in the
face of changing environmental conditions or stimuli. Thus the overarching hypotheses of this proposal are
that human representations of motion statistics (1) are updated in the light of strong evidence for either general
changes in environmental statistics or changes in stimulus-specific statistics, and (2) are best characterized by
coordinate system that is intermediate between retinal and world systems. The proposed research will address
hypothesis (1) in Specific Aim 1 by testing whether changes in perceived visual motion following exposure to
altered motion statistics are well-explained by updates to slow speed prior that generalizes across stimuli and
tasks. This proposal will address hypothesis (2) in Specific Aim 2 by estimating priors under conditions that
dissociate retinal and world motion statistics. In each aim, the questions will be studied using a combination of
visual psychophysics and computational modeling that formalizes the representation of the priors. Together,
these aims will address the substantial gap in the current literature that is marked by a vast number of
perceptual learning studies and relatively few studies addressing the ways in which the visual system updates
its representation of sensory statistics. These studies will contribute to our knowledge on the fundamental
computations involved in the transformation of sensory evidence from the periphery into robust percepts. This
fellowship proposal includes a detailed training plan at a world-class research institution (University of
California, Berkeley) with several specialists in psychophysical research and access to modern visual display
technology and computational resources. The pr...

## Key facts

- **NIH application ID:** 10318920
- **Project number:** 5F32EY032321-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Tyler Manning
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $69,802
- **Award type:** 5
- **Project period:** 2021-01-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10318920

## Citation

> US National Institutes of Health, RePORTER application 10318920, Establishing the Limits of Perceptual Inference for Visual Motion (5F32EY032321-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10318920. Licensed CC0.

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