On manipulating motion gain in immersive virtual environments: An unidentified source of external noise and a new psychometric function

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).