scholarly journals Visual Discomfort and Variations in Chromaticity in Art and Nature

2021 ◽  
Vol 15 ◽  
Author(s):  
Olivier Penacchio ◽  
Sarah M. Haigh ◽  
Xortia Ross ◽  
Rebecca Ferguson ◽  
Arnold J. Wilkins
Keyword(s):  
Good Predictor ◽  
Luminance Contrast ◽  
Visual Ecology ◽  
Visual Images ◽  
Natural Scenes ◽  
Statistical Regularity ◽  
Metabolic Demand ◽  
Visual Discomfort ◽  
Efficient Coding ◽  
Natural Stimuli

Visual discomfort is related to the statistical regularity of visual images. The contribution of luminance contrast to visual discomfort is well understood and can be framed in terms of a theory of efficient coding of natural stimuli, and linked to metabolic demand. While color is important in our interaction with nature, the effect of color on visual discomfort has received less attention. In this study, we build on the established association between visual discomfort and differences in chromaticity across space. We average the local differences in chromaticity in an image and show that this average is a good predictor of visual discomfort from the image. It accounts for part of the variance left unexplained by variations in luminance. We show that the local chromaticity difference in uncomfortable stimuli is high compared to that typical in natural scenes, except in particular infrequent conditions such as the arrangement of colorful fruits against foliage. Overall, our study discloses a new link between visual ecology and discomfort whereby discomfort arises when adaptive perceptual mechanisms are overstimulated by specific classes of stimuli rarely found in nature.

scholarly journals Visual Information flow in Wilson-Cowan networks

2020 ◽  
Author(s):  
Alexander Gomez Villa ◽  
Marcelo Bertalmío ◽  
Jesus Malo
Keyword(s):  
Image Coding ◽  
Visual Information ◽  
Luminance Contrast ◽  
Natural Scenes ◽  
Local Contrast ◽  
Visual Responses ◽  
Cortical Model ◽  
Efficient Coding ◽  
Total Correlation ◽  
Divisive Normalization

In this work we study the communication efficiency of a psychophysically-tuned cascade of Wilson-Cowan and Divisive Normalization layers that simulate the retina-V1 pathway. This is the first analysis of Wilson-Cowan networks in terms of multivariate total correlation. The parameters of the cortical model have been derived through the relation between the steady state of the Wilson-Cowan model and the Divisive Normalization model.Efficiency has been analyzed in two ways: First, we provide an analytical expression for the reduction of the total correlation among the responses of a V1-like population after the Wilson-Cowan interaction. Second, we empirically study the efficiency with visual stimuli and statistical tools that were not available before: (1) a recent, radiometrically calibrated, set of natural scenes, and (2) a recent technique to estimate the multivariate total correlation in bits from sets of visual responses which only involves univariate operations, thus giving better redundancy estimates.The theoretical and the empirical results show that although this cascade of layers was not optimized for statistical independence in any way, the redundancy between the responses gets substantially reduced along the pathway. Specifically, we show that (1) the efficiency of a Wilson-Cowan network is similar to its equivalent Divisive Normalization, (2) while initial layers (Von-Kries adaptation and Weber-like brightness) contribute to univariate equalization, the bigger contributions to the reduction in total correlation come from the nonlinear local contrast and the local oriented filters, and (3) psychophysically-tuned models are more efficient in the more populated regions of the luminance-contrast plane. These results are an alternative confirmation of the Efficient Coding Hypothesis for the Wilson-Cowan systems. And from an applied perspective, they suggest that neural field models could be an option in image coding to perform image compression.

scholarly journals Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes

2021 ◽  
Author(s):  
Riccardo Caramellino ◽  
Eugenio Piasini ◽  
Andrea Buccellato ◽  
Anna Carboncino ◽  
Vijay Balasubramanian ◽  
...  
Keyword(s):  
Natural Images ◽  
Natural Scenes ◽  
Developmental Processes ◽  
Efficient Coding ◽  
Sensory Data ◽  
Natural Stimuli ◽  
Efficient Processing ◽  
Multipoint Statistics ◽  
Encoding Strategy ◽  
Neuronal Encoding

Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Humans, for instance, are most sensitive to multipoint correlations that vary the most across natural images. Here we show that rats possess the same sensitivity ranking to multipoint statistics as humans, thus extending a classic demonstration of efficient coding to a species where neuronal and developmental processes can be interrogated and causally manipulated.

scholarly journals The lawful imprecision of human surface tilt estimation in natural scenes

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Seha Kim ◽  
Johannes Burge
Keyword(s):  
Human Performance ◽  
Three Dimensional ◽  
Model Performance ◽  
Surface Orientation ◽  
Dimensional Structure ◽  
Natural Scenes ◽  
Natural Scene Statistics ◽  
Three Dimensional Structure ◽  
Natural Stimuli ◽  
Performance Patterns

Estimating local surface orientation (slant and tilt) is fundamental to recovering the three-dimensional structure of the environment. It is unknown how well humans perform this task in natural scenes. Here, with a database of natural stereo-images having groundtruth surface orientation at each pixel, we find dramatic differences in human tilt estimation with natural and artificial stimuli. Estimates are precise and unbiased with artificial stimuli and imprecise and strongly biased with natural stimuli. An image-computable Bayes optimal model grounded in natural scene statistics predicts human bias, precision, and trial-by-trial errors without fitting parameters to the human data. The similarities between human and model performance suggest that the complex human performance patterns with natural stimuli are lawful, and that human visual systems have internalized local image and scene statistics to optimally infer the three-dimensional structure of the environment. These results generalize our understanding of vision from the lab to the real world.

scholarly journals The lawful imprecision of human surface tilt estimation in natural scenes

2017 ◽  
Author(s):  
Seha Kim ◽  
Johannes Burge
Keyword(s):  
Human Performance ◽  
Three Dimensional ◽  
Surface Orientation ◽  
Human Vision ◽  
Dimensional Structure ◽  
Natural Scenes ◽  
Natural Scene Statistics ◽  
Three Dimensional Structure ◽  
Natural Stimuli ◽  
Performance Patterns

AbstractEstimating local surface orientation (slant and tilt) is fundamental to recovering the three-dimensional structure of the environment, but it is unknown how well humans perform this task in natural scenes. Here, with a high-fidelity database of natural stereo-images with groundtruth surface orientation at each pixel, we find dramatic differences in human tilt estimation with natural and artificial stimuli. With artificial stimuli, estimates are precise and unbiased. With natural stimuli, estimates are imprecise and strongly biased. An image-computable normative model grounded in natural scene statistics predicts human bias, precision, and trial-by-trial errors without fitting parameters to the human data. These similarities suggest that the complex human performance patterns with natural stimuli are lawful, and that human visual systems have internalized local image and scene statistics to optimally infer the three-dimensional structure of the environment. The current results help generalize our understanding of human vision from the lab to the real world.

scholarly journals Scene statistics and noise determine the relative arrangement of receptive field mosaics

2021 ◽  
Vol 118 (39) ◽  
pp. e2105115118
Author(s):  
Na Young Jun ◽  
Greg D. Field ◽  
John Pearson
Keyword(s):  
Neural Coding ◽  
Coding Theory ◽  
Receptive Fields ◽  
Optimal Arrangement ◽  
Efficient Coding ◽  
Natural Stimuli ◽  
Signal Stimulus ◽  
Theoretical Predictions ◽  
On And Off Pathways ◽  
Contrast Response

Many sensory systems utilize parallel ON and OFF pathways that signal stimulus increments and decrements, respectively. These pathways consist of ensembles or grids of ON and OFF detectors spanning sensory space. Yet, encoding by opponent pathways raises a question: How should grids of ON and OFF detectors be arranged to optimally encode natural stimuli? We investigated this question using a model of the retina guided by efficient coding theory. Specifically, we optimized spatial receptive fields and contrast response functions to encode natural images given noise and constrained firing rates. We find that the optimal arrangement of ON and OFF receptive fields exhibits a transition between aligned and antialigned grids. The preferred phase depends on detector noise and the statistical structure of the natural stimuli. These results reveal that noise and stimulus statistics produce qualitative shifts in neural coding strategies and provide theoretical predictions for the configuration of opponent pathways in the nervous system.

scholarly journals Efficient coding of natural scenes improves neural system identification

2022 ◽  
Author(s):  
Yongrong Qiu ◽  
David A Klindt ◽  
Klaudia P Szatko ◽  
Dominic Gonschorek ◽  
Larissa Hoefling ◽  
...  
Keyword(s):  
System Identification ◽  
Visual Processing ◽  
Neural System ◽  
Natural Scenes ◽  
Natural Environments ◽  
Retinal Neurons ◽  
Efficient Coding ◽  
Recorded Data ◽  
Identification Model ◽  
Regularized Model

Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage coding principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an autoencoder which aims to efficiently encode natural stimuli. To this end, we built a hybrid model to predict the responses of retinal neurons to noise stimuli. This approach did not only yield a higher performance than the stand-alone system identification model, it also produced more biologically-plausible filters. We found these results to be consistent for retinal responses to different stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. In summary, our results support the hypothesis that efficiently encoding environmental inputs can improve system identification models of early visual processing.

scholarly journals Influence of artificially generated interocular blur difference on fusion stability under vergence stress

2019 ◽  
Vol 12 (4) ◽  
Author(s):  
Miroslav Dostalek ◽  
Jan Hejda ◽  
Karel Fliegel ◽  
Michaela Duchackova ◽  
Ladislav Dusek ◽  
...  
Keyword(s):  
Spatial Frequency ◽  
Healthy Subjects ◽  
Luminance Contrast ◽  
Natural Scenes ◽  
Frequency Content ◽  
Binocular Fusion ◽  
Dominant Eye ◽  
Blurred Image ◽  
The Stability ◽  
Mode A

The stability of fusion was evaluated by its breakage when interocular blur differences were presented under vergence demand to healthy subjects. We presumed that these blur differences cause suppression of the more blurred image (interocular blur suppression, IOBS), disrupt binocular fusion and suppressed eye leaves its forced vergent position. During dichoptic presentation of static grayscale images of natural scenes, the luminance contrast (mode B) or higher-spatial frequency content (mode C) or luminance contrast plus higher-spatial frequency content (mode A) were stepwise reduced in the image presented to the non-dominant eye. We studied the effect of these types of blur on fusion stability at various levels of the vergence demand. During the divergence demand, the fusion was disrupted  with approximately half blur than during convergence. Various modes  of blur influenced fusion differently. The mode C (isolated reduction of higher-spatial frequency content) violated fusion under the lowest vergence demand significantly more than either isolated or combined reduction of luminance contrast (mode B and A). According to our results, the image´s details (i.e. higher-spatial frequency content) protects binocular fusion from disruption by the lowest vergence demand.

Neuronal response to texture- and contrast-defined boundaries in early visual cortex

2007 ◽  
Vol 24 (1) ◽  
pp. 65-77 ◽  
Author(s):  
YUNING SONG ◽  
CURTIS L. BAKER
Keyword(s):  
Visual Cortex ◽  
Spatial Frequency ◽  
Cortical Neurons ◽  
Visual Cues ◽  
Neuronal Response ◽  
Luminance Contrast ◽  
Movement Direction ◽  
Natural Scenes ◽  
Neural Responses ◽  
Early Visual Cortex

Natural scenes contain a variety of visual cues that facilitate boundary perception (e.g., luminance, contrast, and texture). Here we explore whether single neurons in early visual cortex can process both contrast and texture cues. We recorded neural responses in cat A18 to both illusory contours formed by abutting gratings (ICs, texture-defined) and contrast-modulated gratings (CMs, contrast-defined). We found that if a neuron responded to one of the two stimuli, it also responded to the other. These neurons signaled similar contour orientation, spatial frequency, and movement direction of the two stimuli. A given neuron also exhibited similar selectivity for spatial frequency of the fine, stationary grating components (carriers) of the stimuli. These results suggest that the cue-invariance of early cortical neurons extends to different kinds of texture or contrast cues, and might arise from a common nonlinear mechanism.

From spatial frequency contrast to edge preponderance: the differential modulation of early visual evoked potentials by natural scene stimuli

2011 ◽  
Vol 28 (3) ◽  
pp. 221-237 ◽  
Author(s):  
BRUCE C. HANSEN ◽  
THEODORE JACQUES ◽  
AARON P. JOHNSON ◽  
DAVE ELLEMBERG
Keyword(s):  
Spatial Frequency ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Luminance Contrast ◽  
Systematic Investigation ◽  
Natural Scenes ◽  
Natural Scene ◽  
Sinusoidal Gratings ◽  
Edge Content ◽  
Visual Evoked

AbstractThe contrast response function of early visual evoked potentials elicited by sinusoidal gratings is known to exhibit characteristic potentials closely associated with the processes of parvocellular and magnocellular pathways. Specifically, the N1 component has been linked with parvocellular processes, while the P1 component has been linked with magnocellular processes. However, little is known regarding the response properties of the N1 and P1 components during the processing and encoding of complex (i.e., broadband) stimuli such as natural scenes. Here, we examine how established physical characteristics of natural scene imagery modulate the N1 and P1 components in humans by providing a systematic investigation of component modulation as visual stimuli are gradually built up from simple sinusoidal gratings to highly complex natural scene imagery. The results suggest that the relative dominance in signal output of the N1 and P1 components is dependent on spatial frequency (SF) luminance contrast for simple stimuli up to natural scene imagery possessing few edges. However, such a dependency shifts to a dominant N1 signal for natural scenes possessing abundant edge content and operates independently of SF luminance contrast.

scholarly journals Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Tiberiu Tesileanu ◽  
Mary M Conte ◽  
John J Briguglio ◽  
Ann M Hermundstad ◽  
Jonathan D Victor ◽  
...  
Keyword(s):  
Dimensional Space ◽  
Second Order ◽  
Visual Sensitivity ◽  
Natural Scenes ◽  
Large Set ◽  
Natural Scene Statistics ◽  
Image Statistics ◽  
Efficient Coding ◽  
Fractional Error ◽  
Relative Salience

Previously, in Hermundstad et al., 2014, we showed that when sampling is limiting, the efficient coding principle leads to a ‘variance is salience’ hypothesis, and that this hypothesis accounts for visual sensitivity to binary image statistics. Here, using extensive new psychophysical data and image analysis, we show that this hypothesis accounts for visual sensitivity to a large set of grayscale image statistics at a striking level of detail, and also identify the limits of the prediction. We define a 66-dimensional space of local grayscale light-intensity correlations, and measure the relevance of each direction to natural scenes. The ‘variance is salience’ hypothesis predicts that two-point correlations are most salient, and predicts their relative salience. We tested these predictions in a texture-segregation task using un-natural, synthetic textures. As predicted, correlations beyond second order are not salient, and predicted thresholds for over 300 second-order correlations match psychophysical thresholds closely (median fractional error <0.13).

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