A common rule for integration and suppression of luminance contrast across eyes, space, time, and pattern

Distinct spatiotemporal mechanisms underlie extra-classical receptive field modulation in macaque V1 microcircuits

Complex scene perception depends upon the interaction between signals from the classical receptive field (CRF) and the extra-classical receptive field (eCRF) in primary visual cortex (V1) neurons. Although much is known about V1 eCRF properties, we do not yet know how the underlying mechanisms map onto the cortical microcircuit. We probed the spatio-temporal dynamics of eCRF modulation using a reverse correlation paradigm, and found three principal eCRF mechanisms: tuned-facilitation, untuned-suppression, and tuned-suppression. Each mechanism had a distinct timing and spatial profile. Laminar analysis showed that the timing, orientation-tuning, and strength of eCRF mechanisms had distinct signatures within magnocellular and parvocellular processing streams in the V1 microcircuit. The existence of multiple eCRF mechanisms provides new insights into how V1 responds to spatial context. Modeling revealed that the differences in timing and scale of these mechanisms predicted distinct patterns of net modulation, reconciling many previous disparate physiological and psychophysical findings.

Lateral effects in pattern vision

There is a large literature on lateral effects in pattern vision but no consensus about them or comprehensive model of them. This paper reviews the literature with a focus on the effects of parallel context in the central fovea. It describes seven experiments that measure detection and discrimination thresholds in annular and Gabor-pattern contexts at different separations. It presents a model of these effects, which is an elaboration of Foley's (1994) model. The model describes the results well, and it shows that lateral context affects the response to the target by both multiplicative excitation and additive inhibition. Both lateral effects extend for several wavelengths beyond the target. They vary in relative strength, producing near suppression and far enhancement of the response to the target. The model describes the detection and discrimination results well, and it also describes the results of experiments on lateral effects on perceived contrast. The model is consistent with the physiology of V1 cells.

Binocular summation revisited: Beyond √2

Our ability to detect faint images is better with two eyes than with one, but how great is this improvement? A meta-analysis of 65 studies published across more than 5 decades shows definitively that psychophysical binocular summation (the ratio of binocular to monocular contrast sensitivity) is significantly greater than the canonical value of √2. Several methodological factors were also found to affect summation estimates. Binocular summation was significantly affected by both the spatial and temporal frequency of the stimulus, and stimulus speed (the ratio of temporal to spatial frequency) systematically predicts summation levels, with slow speeds (high spatial and low temporal frequencies) producing the strongest summation. We furthermore show that empirical summation estimates are affected by the ratio of monocular sensitivities, which varies across individuals, and is abnormal in visual disorders such as amblyopia. A simple modeling framework is presented to interpret the results of summation experiments. In combination with the empirical results, this model suggests that there is no single value for binocular summation, but instead that summation ratios depend on methodological factors that influence the strength of a nonlinearity occurring early in the visual pathway, before binocular combination of signals. Best practice methodological guidelines are proposed for obtaining accurate estimates of neural summation in future studies, including those involving patient groups with impaired binocular vision. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Evidence for an Optimal Algorithm Underlying Signal Combination in Human Visual Cortex

How does the cortex combine information from multiple sources? We tested several computational models against data from steady-state electroencephalography (EEG) experiments in humans, using periodic visual stimuli combined across either retinal location or eye-of-presentation. A model in which signals are raised to an exponent before being summed in both the numerator and the denominator of a gain control nonlinearity gave the best account of the data. This model also predicted the pattern of responses in a range of additional conditions accurately and with no free parameters, as well as predicting responses at harmonic and intermodulation frequencies between 1 and 30 Hz. We speculate that this model implements the optimal algorithm for combining multiple noisy inputs, in which responses are proportional to the weighted sum of both inputs. This suggests a novel purpose for cortical gain control: implementing optimal signal combination via mutual inhibition, perhaps explaining its ubiquity as a neural computation.

Perception of global image contrast involves transparent spatial filtering and the integration and suppression of local contrasts (not RMS contrast)

When adjusting the contrast setting on a television set, we experience a perceptual change in the global image contrast. But how is that statistic computed? We addressed this using a contrast-matching task for checkerboard configurations of micro-patterns in which the contrasts and spatial spreads of two interdigitated components were controlled independently. When the patterns differed greatly in contrast, the higher contrast determined the perceived global contrast. Crucially, however, low contrast additions of one pattern to intermediate contrasts of the other caused a paradoxical reduction in the perceived global contrast. None of the following metrics/models predicted this: max, linear sum, average, energy, root mean squared (RMS), Legge and Foley. However, a nonlinear gain control model, derived from contrast detection and discrimination experiments, incorporating wide-field summation and suppression, did predict the results with no free parameters, but only when spatial filtering was removed. We conclude that our model describes fundamental processes in human contrast vision (the pattern of results was the same for expert and naive observers), but that above threshold-when contrast pedestals are clearly visible-vision's spatial filtering characteristics become transparent, tending towards those of a delta function prior to spatial summation. The global contrast statistic from our model is as easily derived as the RMS contrast of an image, and since it more closely relates to human perception, we suggest it be used as an image contrast metric in practical applications.

Contrast Gain Control in Plaid Pattern Detection

A plaid is a combination of two gratings whose orientations are orthogonal to each other with the same or similar contrasts. We used plaid patterns as stimuli to investigate the mechanisms underlying the detection of a plaid to understand how the visual system combines information from orientation-selective channels. We used a masking paradigm in which an observer was required to detect a target (either a spiral or a plaid) superimposed on a pedestal. We measured the target threshold versus pedestal contrast (TvC) functions at 7 pedestal contrasts for various target-pedestal combinations with a temporal 2AFC paradigm and a staircase procedure. All TvC functions, except the one with an orthogonal spiral pedestal, showed a dipper shape, although the position of the dip and the slope varied across conditions. The result can be explained by a multiple-mechanism divisive inhibition model, which contains several orientation-selective mechanisms. The response of each mechanism is the excitation of a linear filter divided by a broadband inhibitory input. The threshold is determined by a nonlinear combination of the responses of those mechanisms. Alternative models with mechanism(s) specific for plaid did not provide a better description of the data. Thus, a plaid pattern is mediated by a combination of orientation-selective mechanisms. An early plaid-specific mechanism is not necessary for plaid detection.

What Do Contrast Threshold Equivalent Noise Studies Actually Measure? Noise vs. Nonlinearity in Different Masking Paradigms

The internal noise present in a linear system can be quantified by the equivalent noise method. By measuring the effect that applying external noise to the system's input has on its output one can estimate the variance of this internal noise. By applying this simple "linear amplifier" model to the human visual system, one can entirely explain an observer's detection performance by a combination of the internal noise variance and their efficiency relative to an ideal observer. Studies using this method rely on two crucial factors: firstly that the external noise in their stimuli behaves like the visual system's internal noise in the dimension of interest, and secondly that the assumptions underlying their model are correct (e.g. linearity). Here we explore the effects of these two factors while applying the equivalent noise method to investigate the contrast sensitivity function (CSF). We compare the results at 0.5 and 6 c/deg from the equivalent noise method against those we would expect based on pedestal masking data collected from the same observers. We find that the loss of sensitivity with increasing spatial frequency results from changes in the saturation constant of the gain control nonlinearity, and that this only masquerades as a change in internal noise under the equivalent noise method. Part of the effect we find can be attributed to the optical transfer function of the eye. The remainder can be explained by either changes in effective input gain, divisive suppression, or a combination of the two. Given these effects the efficiency of our observers approaches the ideal level. We show the importance of considering these factors in equivalent noise studies.

Measuring the spatial extent of texture pooling using reverse correlation

The local image representation produced by early stages of visual analysis is uninformative regarding spatially extensive textures and surfaces. We know little about the cortical algorithm used to combine local information over space, and still less about the area over which it can operate. But such operations are vital to support perception of real-world objects and scenes. Here, we deploy a novel reverse-correlation technique to measure the extent of spatial pooling for target regions of different areas placed either in the central visual field, or more peripherally. Stimuli were large arrays of micropatterns, with their contrasts perturbed individually on an interval-by-interval basis. By comparing trial-by-trial observer responses with the predictions of computational models, we show that substantial regions (up to 13 carrier cycles) of a stimulus can be monitored in parallel by summing contrast over area. This summing strategy is very different from the more widely assumed signal selection strategy (a MAX operation), and suggests that neural mechanisms representing extensive visual textures can be recruited by attention. We also demonstrate that template resolution is much less precise in the parafovea than in the fovea, consistent with recent accounts of crowding.

Paradoxical psychometric functions ("swan functions") are explained by dilution masking in four stimulus dimensions

The visual system dissects the retinal image into millions of local analyses along numerous visual dimensions. However, our perceptions of the world are not fragmentary, so further processes must be involved in stitching it all back together. Simply summing up the responses would not work because this would convey an increase in image contrast with an increase in the number of mechanisms stimulated. Here, we consider a generic model of signal combination and counter-suppression designed to address this problem. The model is derived and tested for simple stimulus pairings (e.g. A + B), but is readily extended over multiple analysers. The model can account for nonlinear contrast transduction, dilution masking, and signal combination at threshold and above. It also predicts nonmonotonic psychometric functions where sensitivity to signal A in the presence of pedestal B first declines with increasing signal strength (paradoxically dropping below 50% correct in two-interval forced choice), but then rises back up again, producing a contour that follows the wings and neck of a swan. We looked for and found these "swan" functions in four different stimulus dimensions (ocularity, space, orientation, and time), providing some support for our proposal.