Back

The effect of unilateral cortical blindness on lane position and gaze behavior in a virtual reality steering task

Giguere, A. P.; Cavanaugh, M. R.; Huxlin, K. R.; Tadin, D.; Fajen, B. R.; Diaz, G. J.

2025-02-06 animal behavior and cognition
10.1101/2025.02.06.636925 bioRxiv
Show abstract

Adults with cortically-induced blindness (CB) affecting a quarter to a half of their visual field show greater variability in lane positioning when driving compared to those with intact vision. Because humans rely on visual information from optic flow to control steering, we hypothesized that these lane biases are caused in part by a disruption to motion processing caused by CB. To investigate, we examined the steering behavior of 21 CB drivers (11 left-sided, 10 right-sided visual deficits) and 9 visually intact controls in a naturalistic virtual environment. Participants were instructed to maintain a central lane position while traveling at 19 m/s along a procedurally generated single-lane road. Turn direction (left/right) and turn radius (35m/55m/75m) varied between trials, and the quality of optic flow information was indirectly manipulated by altering the environmental texture density (low/medium/high). Right-sided CB participants maintained a similar average distance from the inner road edge as controls. Those with left-sided CB were less affected by changes in optic flow and turn direction. These differences were not explained by age, time since stroke, sparing of central vision, gaze direction, or saccade rate. Our results suggest that some left-sided CB participants place a lower weighting on optic flow information in the control of steering, possibly as a result of lateralization in the processing of motion. More broadly, our findings show that CB steering and gaze behavior are remarkably preserved despite the presence of visual deficits across large portions of the visual field.

Published in Scientific Reports (predicted rank #3) · training set

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.