Influence of target size and luminance on the White-Todorovic effect

Dynamic decorrelation as a unifying principle for explaining a broad range of brightness phenomena

The visual system is highly sensitive to spatial context for encoding luminance patterns. Context sensitivity inspired the proposal of many neural mechanisms for explaining the perception of luminance (brightness). Here we propose a novel computational model for estimating the brightness of many visual illusions. We hypothesize that many aspects of brightness can be explained by a dynamic filtering process that reduces the redundancy in edge representations on the one hand, while non-redundant activity is enhanced on the other. The dynamic filter is learned for each input image and implements context sensitivity. Dynamic filtering is applied to the responses of (model) complex cells in order to build a gain control map. The gain control map then acts on simple cell responses before they are used to create a brightness map via activity propagation. Our approach is successful in predicting many challenging visual illusions, including contrast effects, assimilation, and reverse contrast with the same set of model parameters.

Lightness induction enhancements and limitations at low frequency modulations across a variety of stimulus contexts

Lightness illusions are often studied under static viewing conditions with figures varying in geometric design, containing different types of perceptual grouping and figure-ground cues. A few studies have explored the perception of lightness induction while modulating lightness illusions continuously in time, where changes in perceived lightness are often linked to the temporal modulation frequency, up to around 2–4 Hz. These findings support the concept of a cut-off frequency for lightness induction. However, another critical change (enhancement) in the magnitude of perceived lightness during slower temporal modulation conditions has not been addressed in previous temporal modulation studies. Moreover, it remains unclear whether this critical change applies to a variety of lightness illusion stimuli, and the degree to which different stimulus configurations can demonstrate enhanced lightness induction in low modulation frequencies. Therefore, we measured lightness induction strength by having participants cancel out any perceived modulation in lightness detected over time within a central target region, while the surrounding context, which ultimately drives the lightness illusion, was viewed in a static state or modulated continuously in time over a low frequency range (0.25–2 Hz). In general, lightness induction decreased as temporal modulation frequency was increased, with the strongest perceived lightness induction occurring at lower modulation frequencies for visual illusions with strong grouping and figure-ground cues. When compared to static viewing conditions, we found that slow continuous surround modulation induces a strong and significant increase in perceived lightness for multiple types of lightness induction stimuli. Stimuli with perceptually ambiguous grouping and figure-ground cues showed weaker effects of slow modulation lightness enhancement. Our results demonstrate that, in addition to the existence of a cut-off frequency, an additional critical temporal modulation frequency of lightness induction exists (0.25–0.5 Hz), which instead maximally enhances lightness induction and seems to be contingent upon the prevalence of figure-ground and grouping organization.

SPECTRAL DOMAIN-OPTICAL COHERENCE TOMOGRAPHY IMAGE CONTRAST AND BACKGROUND COLOR SETTINGS INFLUENCE IDENTIFICATION OF RETINAL STRUCTURES

PURPOSE

To evaluate image contrast and color setting on assessment of retinal structures and morphology in spectral-domain optical coherence tomography.

METHODS

Two hundred and forty-eight Spectralis spectral-domain optical coherence tomography B-scans of 62 patients were analyzed by 4 readers. B-scans were extracted in 4 settings: W + N = white background with black image at normal contrast 9; W + H = white background with black image at maximum contrast 16; B + N = black background with white image at normal contrast 12; B + H = black background with white image at maximum contrast 16. Readers analyzed the images to identify morphologic features. Interreader correlation was calculated. Differences between Fleiss-kappa correlation coefficients were examined using bootstrap method. Any setting with significantly higher correlation coefficient was deemed superior for evaluating specific features.

RESULTS

Correlation coefficients differed among settings. No single setting was superior for all respective spectral-domain optical coherence tomography parameters (P = 0.3773). Some variables showed no differences among settings. Hard exudates and subretinal fluid were best seen with B + H (κ = 0.46, P = 0.0237 and κ = 0.78, P = 0.002). Microaneurysms were best seen with W + N (κ = 0.56, P = 0.025). Vitreomacular interface, enhanced transmission signal, and epiretinal membrane were best identified using all color/contrast settings together (κ = 0.44, P = 0.042, κ = 0.57, P = 0.01, and κ = 0.62, P ≤ 0.0001).

CONCLUSION

Contrast and background affect the evaluation of retinal structures on spectral-domain optical coherence tomography images. No single setting was superior for all features, though certain changes were best seen with specific settings.

A Neurocomputational account of the role of contour facilitation in brightness perception

A new filling-in model is proposed in order to account for challenging brightness illusions, where inducing background elements are spatially separated from the gray target such as dungeon, cube and grating illusions, bullseye display and ring patterns. This model implements the simple idea that neural response to low-contrast contour is enhanced (facilitated) by the presence of collinear or parallel high-contrast contours in its wider neighborhood. Contour facilitation is achieved via dendritic inhibition, which enables the computation of maximum function among inputs to the node. Recurrent application of maximum function leads to the propagation of the neural signal along collinear or parallel contour segments. When a strong global-contour signal is accompanied with a weak local-contour signal at the same location, conditions are met to produce brightness assimilation within the Filling-in Layer. Computer simulations showed that the model correctly predicts brightness appearance in all of the aforementioned illusions as well as in White's effect, Benary's cross, Todorović's illusion, checkerboard contrast, contrast-contrast illusion and various variations of the White's effect. The proposed model offers new insights on how geometric factors (contour colinearity or parallelism), together with contrast magnitude contribute to the brightness perception.