RecruitingNot ApplicableNCT06047717

Vision Loss Impact on Navigation in Virtual Reality

The Impact of Vision Loss on Naturalistic Behavior and Navigation in Virtual Reality

Sponsor

University of Rochester

Enrollment

40 participants

Start Date

Nov 28, 2023

Study Type

INTERVENTIONAL

Conditions

Stroke, Ischemic Hemianopsia, Homonymous Hemianopia Quadrantanopia Visual Field Defect, Peripheral Vision Loss Partial Hemianopia Homonymous Stroke Hemorrhagic Hemianopsia Occipital Lobe Infarct Quadrantanopsia

Summary

The purpose of this research is to better understand the impact of cortically-induced blindness (CB) and the compensatory strategies subjects with this condition may develop on naturalistic behaviors, specifically, driving. Using a novel Virtual Reality (VR) program, the researchers will gather data on steering behavior in a variety of simulated naturalistic environments. Through the combined use of computer vision, deep learning, and gaze-contingent manipulations of the visual field, this work will test the central hypothesis that changes to visually guided steering behaviors in CB are a consequence of changes to the visual sampling and processing of task-related motion information (i.e., optic flow).

Eligibility

Min Age: 21 YearsMax Age: 75 Years

Plain Language Summary

Simplified for easier understanding

This clinical trial is studying Virtual Reality Driving Task for people with hemianopia, hemianopia homonymous, and other related conditions. The study is currently recruiting participants at 1 location. People eligible for this study include aged 21 Years to 75 Years.

This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.

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Interventions

OTHERVirtual Reality Driving Task

Participants will steer a virtual car with the goal of staying in the center of a single-lane roadway while traveling at a constant speed of 26.6 m/s (approximately 60 miles/hr). The roadway alternates between a series of straights and turns of different radii to both the left and the right. This allows for careful control of task difficulty, and for the repeated presentation of specific conditions across multiple "trials" (i.e. turns in the road) in a randomized order. In addition, the density of the visual texture elements in the virtual environment that provide optic flow (OF) signal is also varied. The low-density OF condition has no road texture or foliage, and only the solid road edges on a flat-black ground plane. The medium-density OF condition has sparse textural elements distributed on the ground plane, and the high-density OF condition has high density road texture and a canopy of road-side trees that provide texture extending far above the horizon.