Dynamics of Normalization Underlying Masking in Human Visual Cortex

Abstract

Stimulus visibility can be reduced by other stimuli that overlap the same region of visual space, a process known as masking. Here we studied the neural mechanisms of masking in humans using source-imaged Steady State Visual Evoked Potentials (SSVEPs) and frequency-domain analysis over a wide range of relative stimulus strengths of test and mask stimuli. Test and mask stimuli were tagged with distinct temporal frequencies and we quantified spectral response components associated with the individual stimuli (self terms) and responses due to interaction between stimuli (intermodulation terms). In early visual cortex, masking alters the self-terms in a manner consistent with a reduction of input contrast. We also identify a novel signature of masking: a robust intermodulation term that peaks when the test and mask stimuli have equal contrast and disappears when they are widely different. We fit all of our data simultaneously with family of a divisive gain control models that differed only in their dynamics. Models with either very short or very long temporal integration constants for the gain pool performed worse than a model with an integration time of approximately 30 ms. Finally, the absolute magnitudes of the response were controlled by the ratio of the stimulus contrasts, not their absolute values. This “contrast-contrast” invariance suggests that many neurons in early visual cortex code relative rather than absolute contrast. Together, these results provide a more complete description of masking within the normalization framework of contrast gain control and suggest that contrast normalization accomplishes multiple functional goals.

Steady-State Contrast Response Functions Provide a Sensitive and Objective Index of Amblyopic Deficits

“…2a, green data), particularly at 26% target contrast. This gain control effect is similar to those reported previously, 26,27 though somewhat weaker. A two-way repeated measures ANOVA on the normalized amplitudes (which satisfied the assumption of sphericity) revealed a significant effect of target contrast (F 4,16 ¼ 52.3, P < 0.001, partial g 2 ¼ 0.93), but no significant effect of mask contrast (F 1,16 ¼ 6.94, P ¼ 0.58) and no interaction between the two variables (F 4,16 ¼ 1.94, P ¼ 0.15).…”

“…In normal observers (with no attenuation), a dichoptic mask shown to the nontarget eye also produces a rightward shift of the contrast response function (dotted green curve). This is a classic contrast gain control effect that resembles closely those measured with overlaid masks in both single-and multiunit electrophysiology, 24,25 and steady-state EEG 26,27 studies. In amblyopia, the attenuator reduces the suppressive effect from the amblyopic onto the fellow eye (dashed light blue curve is to the left of the green dotted curve) and increases the suppressive effect from the fellow onto the amblyopic eye (dashed orange curve), resulting in an imbalance of binocular suppression.…”

“…This is consistent with previous work. 26,27 When a 12-Hz mask was shown to the other eye at high contrast, this slightly reduced the response to the target (Fig. 2a, green data), particularly at 26% target contrast.…”

“…We predicted the pattern of contrast response functions in normal observers and in amblyopes by using the attenuator model of Baker et al 22 Although the model includes a binocular summation process, when the stimuli in the two eyes flicker at different frequencies we do not expect their EEG responses to sum across the two fundamental frequencies (though there may be intermodulation responses 26 ). Therefore, the response of the model to stimuli in the nonamblyopic eye was given by…”

Targets but not flankers are suppressed in crowding as revealed by EEG frequency tagging

“…We did not observe any prominent intermodulation components, thus, arguing against pooling and basic inhibition models. However, it may be that we did not find intermodulation components because the strong flanker signals fully suppressed the target signals in a non-linear manner, reminiscent of findings in single cell recordings and EEG studies (Busse et al, 2009;Tsai et al, 2012). However, stimuli in these studies spatially overlapped, which is not the case with our crowding stimuli, which are clearly separated in space.…”