ArticleJournal of vision2026
On manipulating motion gain in immersive virtual environments: An unidentified source of external noise and a new psychometric function.
Article in Journal of vision, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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).
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