Weighted summation and contrast normalization account for short-latency disparity vergence responses to white noise stimuli in humans

Abstract

Natural images are typically broadband, whereas detectors in early visual processing are selective for narrow ranges of spatial frequency. White noise patterns are widely used in laboratory settings to investigate how responses are derived from Fourier components in the image. Here, we report disparity vergence responses (DVRs) to white noise stimuli in human subjects and compare these with responses to white noise patterns filtered with bandpass filters and notch filters and to sinusoidal gratings. Although the contribution of these short-latency eye movements to the overall vergence response to a given stimulus is generally small, they have proven to be a valuable tool for the study of the early mechanisms that process disparity stimuli in human subjects. Removing lower spatial frequency (SF) components reduced DVR amplitude, whereas removing higher SF components led to an increase in DVR amplitude. For larger disparities, the transition occurred at lower SFs. All of these effects were quantitatively well described by a model that combined two factors: (a) an excitatory drive determined by a weighted sum of stimulus Fourier components, which was scaled by (b) a contrast normalization mechanism.

Manipulating the Fourier spectra of stimuli comprising a two-frame kinematogram to study early visual motion-detecting mechanisms: Perception versus short latency ocular-following responses

“…For two-frame movies in which the cSF 1 and cSF 2 were the same, Equation 2 simplifies to: akin to a recently proposed model that successfully reproduced the disparity-vergence responses (DVRs) to broadband stimuli ( Sheliga, Quaia, FitzGibbon, & Cumming, 2022b ; see Discussion).…”

“…In several earlier studies, however, fits to the data were the best if the model entertained a weighted summation of the components, that is, when the contributions of Fourier components were weighted based on their SF: ∑ OFR i *( W i * C i ) m instead of ∑ . In the Sheliga et al (2022b) study, the best fits to the data were achieved when the weights of Fourier components were modeled by a power function of SF, adding one free parameter to the model (Equation 3b; Sheliga et al, 2022b ). When we added such weights to Equation 2 , the improvements in fits were not statistically significant for all subjects ( p > 0.05; general linear F-test).…”