Estimating the contribution of early and late noise in vision from psychophysical data

Human performance in psychophysical detection and discrimination tasks is limited by inner noise. It is unclear to what extent this inner noise arises from early noise (e.g., in the photoreceptors) or from late noise (at or immediately prior to the decision stage, presumably in cortex). Very likely, the behaviorally limiting inner noise is a nontrivial combination of both early and late noise. Here we propose a method to quantify the contributions of early and late noise purely from psychophysical data. Our approach generalizes classical results for linear systems by combining the theory of noise propagation through a nonlinear network with expressions to obtain a perceptual metric through a nonlinear network. We show that from threshold-only data, the relative contributions of early and late noise can only be disentangled when the experiments include substantial external noise. When full psychometric functions are available, early and late noise sources can be quantified even in the absence of external noise. Our psychophysical estimate of the magnitude of early noise-assuming a standard cascade of linear and nonlinear model stages-is substantially lower than the noise in cone photocurrents computed via an accurate model of retinal physiology, the ISETBio. This is consistent with the idea that one of the fundamental tasks of early vision is to reduce the comparatively large retinal noise.