Brightness induction magnitude declines with increasing distance from the inducing field edge

Eliminating brightness induction effects when measuring motion centre-surround suppression of contrast

The perceived contrast of a central stimulus is supressed when it is embedded in a higher contrast surround, centre-surround suppression of contrast. Local brightness induction effects between the two stimulus regions have been proposed to account for conflicting results when relative grating phases were different. Here, suppression and brightness induction effects are dissociated using a centre-surround arrangement with moving gratings. Four experienced observers were involved in experiments, utilising two-interval forced-choice contrast matching tasks. The stimuli were drifting sinusoidal grating patterns with surrounds (95% contrast) differing in direction of motion and orientation relative to the 40% contrast centre grating. First a 90°-phase-offset same direction surround condition was compared to both same direction (phase aligned) and opposing direction conditions. The reduction in the suppression for the phase-offset condition suggested a reduction in brightness induction influences. Then suppression was examined when surround directions varied and where phase was either fixed or randomised. For small changes in the motion direction between centre and surround (0° to 26.6°) the amount of brightness induction varied sinusoidally with the difference in phase introduced by the direction difference. Finally, the spatial separation between the centre and surround was varied to determine the reduction of suppression and brightness induction with increasing spatial distance. We found both fit an exponential decay function, with surround suppression producing the larger range of influence. Our findings quantify both brightness induction and suppression effects and validate the use of phase randomisation to remove effects of brightness induction when evaluating surround suppression.

Luminance gradients and non-gradients as a cue for distinguishing reflectance and illumination in achromatic images: A computational approach

The brain analyses the visual world through the luminance patterns that reach the retina. Formally, luminance (as measured by the retina) is the product of illumination and reflectance. Whereas illumination is highly variable, reflectance is a physical property that characterizes each object surface. Due to memory constraints, it seems plausible that the visual system suppresses illumination patterns before object recognition takes place. Since many combinations of reflectance and illumination can give rise to identical luminance values, finding the correct reflectance value of a surface is an ill-posed problem, and it is still an open question how it is solved by the brain. Here we propose a computational approach that first learns filter kernels ("receptive fields") for slow and fast variations in luminance, respectively, from achromatic real-world images. Distinguishing between luminance gradients (slow variations) and non-gradients (fast variations) could serve to constrain the mentioned ill-posed problem. The second stage of our approach successfully segregates luminance gradients and non-gradients from real-world images. Our approach furthermore predicts that visual illusions that contain luminance gradients (such as Adelson's checker-shadow display or grating induction) may occur as a consequence of this segregation process.

Retinal Lateral Inhibition Provides the Biological Basis of Long-Range Spatial Induction

Retinal lateral inhibition is one of the conventional efficient coding mechanisms in the visual system that is produced by interneurons that pool signals over a neighborhood of presynaptic feedforward cells and send inhibitory signals back to them. Thus, the receptive-field (RF) of a retinal ganglion cell has a center-surround receptive-field (RF) profile that is classically represented as a difference-of-Gaussian (DOG) adequate for efficient spatial contrast coding. The DOG RF profile has been attributed to produce the psychophysical phenomena of brightness induction, in which the perceived brightness of an object is affected by that of its vicinity, either shifting away from it (brightness contrast) or becoming more similar to it (brightness assimilation) depending on the size of the surfaces surrounding the object. While brightness contrast can be modeled using a DOG with a narrow surround, brightness assimilation requires a wide suppressive surround. Early retinal studies determined that the suppressive surround of a retinal ganglion cell is narrow (< 100–300 μm; ‘classic RF’), which led researchers to postulate that brightness assimilation must originate at some post-retinal, possibly cortical, stage where long-range interactions are feasible. However, more recent studies have reported that the retinal interneurons also exhibit a spatially wide component (> 500–1000 μm). In the current study, we examine the effect of this wide interneuron RF component in two biophysical retinal models and show that for both of the retinal models it explains the long-range effect evidenced in simultaneous brightness induction phenomena and that the spatial extent of this long-range effect of the retinal model responses matches that of perceptual data. These results suggest that the retinal lateral inhibition mechanism alone can regulate local as well as long-range spatial induction through the narrow and wide RF components of retinal interneurons, arguing against the existing view that spatial induction is operated by two separate local vs. long-range mechanisms.

The Oriented Difference of Gaussians (ODOG) model of brightness perception: Overview and executable Mathematica notebooks

The Oriented Difference of Gaussians (ODOG) model of brightness (perceived intensity) by Blakeslee and McCourt (Vision Research 39:4361-4377, 1999), which is based on linear spatial filtering by oriented receptive fields followed by contrast normalization, has proven highly successful in parsimoniously predicting the perceived intensity (brightness) of regions in complex visual stimuli such as White's effect, which had been believed to defy filter-based explanations. Unlike competing explanations such as anchoring theory, filling-in, edge-integration, or layer decomposition, the spatial filtering approach embodied by the ODOG model readily accounts for the often overlooked but ubiquitous gradient structure of induction which, while most striking in grating induction, also occurs within the test fields of classical simultaneous brightness contrast and the White stimulus. Also, because the ODOG model does not require defined regions of interest, it is generalizable to any stimulus, including natural images. The ODOG model has motivated other researchers to develop modified versions (LODOG and FLODOG), and has served as an important counterweight and proof of concept to constrain high-level theories which rely on less well understood or justified mechanisms such as unconscious inference, transparency, perceptual grouping, and layer decomposition. Here we provide a brief but comprehensive description of the ODOG model as it has been implemented since 1999, as well as working Mathematica (Wolfram, Inc.) notebooks which users can employ to generate ODOG model predictions for their own stimuli.

Brightness induction and suprathreshold vision: effects of age and visual field

A variety of visual capacities show significant age-related alterations. We assessed suprathreshold contrast and brightness perception across the lifespan in a large sample of healthy participants (N=155; 142) ranging in age from 16 to 80 years. Experiment 1 used a quadrature-phase motion cancelation technique (Blakeslee & McCourt, 2008) to measure canceling contrast (in central vision) for induced gratings at two temporal frequencies (1 Hz and 4 Hz) at two test field heights (0.5° or 2°×38.7°; 0.052 c/d). There was a significant age-related reduction in canceling contrast at 4 Hz, but not at 1 Hz. We find no age-related change in induction magnitude in the 1 Hz condition. We interpret the age-related decline in grating induction magnitude at 4 Hz to reflect a diminished capacity for inhibitory processing at higher temporal frequencies. In Experiment 2 participants adjusted the contrast of a matching grating (0.5° or 2°×38.7°; 0.052 c/d) to equal that of both real (30% contrast, 0.052 c/d) and induced (McCourt, 1982) standard gratings (100% inducing grating contrast; 0.052 c/d). Matching gratings appeared in the upper visual field (UVF) and test gratings appeared in the lower visual field (LVF), and vice versa, at eccentricities of ±7.5°. Average induction magnitude was invariant with age for both test field heights. There was a significant age-related reduction in perceived contrast of stimuli in the LVF versus UVF for both real and induced gratings.

A neurodynamical model of brightness induction in v1

Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. Recent neurophysiological evidence suggests that brightness information might be explicitly represented in V1, in contrast to the more common assumption that the striate cortex is an area mostly responsive to sensory information. Here we investigate possible neural mechanisms that offer a plausible explanation for such phenomenon. To this end, a neurodynamical model which is based on neurophysiological evidence and focuses on the part of V1 responsible for contextual influences is presented. The proposed computational model successfully accounts for well known psychophysical effects for static contexts and also for brightness induction in dynamic contexts defined by modulating the luminance of surrounding areas. This work suggests that intra-cortical interactions in V1 could, at least partially, explain brightness induction effects and reveals how a common general architecture may account for several different fundamental processes, such as visual saliency and brightness induction, which emerge early in the visual processing pathway.