On manipulating motion gain in immersive virtual environments: An unidentified source of external noise and a new psychometric function
Year: 2026
Type: article
Abstract: In immersive virtual environments and other interactive settings the motion gain linking stimulus motion to self-movement is often manipulated to study perceptual performance. We show that this introduces a previously unidentified source of external noise we term gain-dependent noise. The noise arises from trial-to-trial variability in self-movement and can distort the shape of the psychometric function. This leads to overestimates of threshold and sensory measurement noise, and misinterpreted lapse rates when fitting standard sigmoidal functions like a cumulative Gaussian. Using a model observer with minimal assumptions, simulations show the situation is more critical when the coefficient of variation of self-movement (i.e., across-trial standard deviation/mean) is relatively high, and measurement noise associated with image-based and idiothetic signals is relatively low. We derive a new gain-dependent psychometric function that separates sensory measurement noise from gain-dependent noise, and provide a MATLAB fitting routine (fitgdpmf).
Source: Journal of Vision
DOI: 10.1167/jov.26.6.9
Institutions: Cardiff University, University of Manchester, University of Kent
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,850
OpenAlex ID: W7165861835