Geometrical structure of perceptual color space: Mental representations and adaptation invariance

Eye Movement Indicator Difference Based on Binocular Color Fusion and Rivalry

Color fusion and rivalry are two key information integration mechanisms in binocular vision, representing the visual system's processing patterns for consistent and conflicting inputs, respectively. This study hypothesizes that there are quantifiable differences in eye movement indicators under states of binocular color fusion and rivalry, which can be verified through multi-paradigm eye movement experiments. The experiment recruited eighteen subjects with normal vision (nine males and nine females), employing the Gaze Stability paradigm, Straight Curve Eye Hopping paradigm, and Smoothed Eye Movement Tracking paradigm for eye movement tracking. Each paradigm included a binocular color rivalry experimental group (R-G) and two binocular color fusion control groups (R-R, G-G). Data analysis indicates significant differences in indicators such as Average Saccade Amplitude, Median Saccade Amplitude, and SD of Saccade Amplitude between binocular color fusion and rivalry states. For instance, through Z-Score normalization and cross-paradigm merged analysis, specific ranges of these indicators were identified to distinguish between the two states. When the Average Saccade Amplitude falls within the range of -0.905--0.693, it indicates a state of binocular color rivalry; when the range is 0.608-1.294, it reflects a state of binocular color fusion. Subsequently, ROC curve analysis confirmed the effectiveness of the experimental paradigms in analyzing the mechanisms of binocular color fusion and rivalry, with AUC values of 0.990, 0.741, and 0.967, respectively. These results reveal the potential of eye movement behaviors as biomarkers for the dynamic processing of visual conflicts. This finding provides empirical support for understanding the neural computational models of binocular vision and lays a methodological foundation for developing visual impairment assessment tools based on eye movement features.

Dissecting the components of error in analogue report tasks

Over the last two decades, the analogue report task has become a standard method for measuring the fidelity of visual representations across research domains including perception, attention, and memory. Despite its widespread use, there has been no methodical investigation of the different task parameters that might contribute to response variability. To address this gap, we conducted two experiments manipulating components of a typical analogue report test of memory for colour hue. We found that human response errors were independently affected by changes in storage and maintenance requirements of the task, demonstrated by a strong effect of set size even in the absence of a memory delay. In contrast, response variability remained unaffected by physical size of the colour wheel, implying negligible contribution of motor noise to task performance, or by its chroma radius, highlighting non-uniformity of the standard colour space. Comparing analogue report to a matched forced-choice task, we found variation in adjustment criterion made a limited contribution to analogue report variability, becoming meaningful only with low representational noise. Our findings validate the analogue report task as a robust measure of representational fidelity for most purposes, while also quantifying non-representational sources of noise that would limit its reliability in specialized settings.

Color appearance and the end of Hering's Opponent-Colors Theory

Hering's Opponent-Colors Theory has been central to understanding color appearance for 150 years. It aims to explain the phenomenology of colors with two linked propositions. First, a psychological hypothesis stipulates that any color is described necessarily and sufficiently by the extent to which it appears reddish-versus-greenish, bluish-versus-yellowish, and blackish-versus-whitish. Second, a physiological hypothesis stipulates that these perceptual mechanisms are encoded by three innate brain mechanisms. We review the evidence and conclude that neither side of the linking proposition is accurate: the theory is wrong. We sketch out an alternative, Utility-Based Coding, by which the known retinal cone-opponent mechanisms represent optimal encoding of spectral information given competing selective pressure to extract high-acuity spatial information; and phenomenological color categories represent an adaptive, efficient, output of the brain governed by behavioral demands.

Are Color Experiences the Same across the Visual Field?

It seems obvious to laypeople that neurotypical humans experience color equivalently across their entire visual field. To some neuroscientists, psychologists, and philosophers, though, this claim has been met with skepticism, as neurophysiological evidence indicates the mechanisms that support color perception degrade with eccentricity. However, the argument that this entails altered color experience in peripheral vision is not universally accepted. Here, we address whether color experience is essentially equivalent between central and peripheral vision. To assess this, we will obtain similarity relationships between color experiences across the visual field using both online and laboratory-based far-field displays, while removing the confounds of saccades, memory, and expectation about color experiences. Our experiment was designed to provide clear evidence that would favor either unchanged or altered color experience relationships in the periphery. Our results are consistent with lay people's phenomenological reports: Color experiences, as probed by similarity relationships in central vision and the far field (60°), are equivalent when elicited by large stimuli. These findings challenge the widespread view in philosophy and cognitive science that peripheral color experiences are illusory, and are discussed in the context of their related neurophysiological, psychophysical, and philosophical literature.

Representing color as multiple independent scales: brightness versus saturation

The concept of color space has served as a basis for vast scientific inquiries into the representation of color, including colorimetry, psychology, and neuroscience. However, the ideal color space that can model color appearance attributes and color difference as a uniform Euclidean space is still, to our best knowledge, not yet available. In this work, based on the alternative representation of independent 1D color scales, the brightness and saturation scales for five Munsell principal hues were collected via partition scaling, where the MacAdam optimal colors served as anchors. Furthermore, the interactions between brightness and saturation were evaluated using maximum likelihood conjoint measurement. For the average observer, saturation as constant chromaticity is independent of luminance changes, while brightness receives a small positive contribution from the physical saturation dimension. This work further supports the feasibility of representing color as multiple independent scales and provides the framework for further investigation of other color attributes.

The non-Riemannian nature of perceptual color space

For over 100 y, the scientific community has adhered to a paradigm, introduced by Riemann and furthered by Helmholtz and Schrodinger, where perceptual color space is a three-dimensional Riemannian space. This implies that the distance between two colors is the length of the shortest path that connects them. We show that a Riemannian metric overestimates the perception of large color differences because large color differences are perceived as less than the sum of small differences. This effect, called diminishing returns, cannot exist in a Riemannian geometry. Consequently, we need to adapt how we model color differences, as the current standard, ΔE, recognized by the International Commission for Weights and Measures, does not account for diminishing returns in color difference perception.

The scientific community generally agrees on the theory, introduced by Riemann and furthered by Helmholtz and Schrödinger, that perceived color space is not Euclidean but rather, a three-dimensional Riemannian space. We show that the principle of diminishing returns applies to human color perception. This means that large color differences cannot be derived by adding a series of small steps, and therefore, perceptual color space cannot be described by a Riemannian geometry. This finding is inconsistent with the current approaches to modeling perceptual color space. Therefore, the assumed shape of color space requires a paradigm shift. Consequences of this apply to color metrics that are currently used in image and video processing, color mapping, and the paint and textile industries. These metrics are valid only for small differences. Rethinking them outside of a Riemannian setting could provide a path to extending them to large differences. This finding further hints at the existence of a second-order Weber–Fechner law describing perceived differences.

BMEFIQA: Blind Quality Assessment of Multi-Exposure Fused Images Based on Several Characteristics

A multi-exposure fused (MEF) image is generated by multiple images with different exposure levels, but the transformation process will inevitably introduce various distortions. Therefore, it is worth discussing how to evaluate the visual quality of MEF images. This paper proposes a new blind quality assessment method for MEF images by considering their characteristics, and it is dubbed as BMEFIQA. More specifically, multiple features that represent different image attributes are extracted to perceive the various distortions of MEF images. Among them, structural, naturalness, and colorfulness features are utilized to describe the phenomena of structure destruction, unnatural presentation, and color distortion, respectively. All the captured features constitute a final feature vector for quality regression via random forest. Experimental results on a publicly available database show the superiority of the proposed BMEFIQA method to several blind quality assessment methods.

Temporal dynamics of the neural representation of hue and luminance polarity

Hue and luminance contrast are basic visual features. Here we use multivariate analyses of magnetoencephalography data to investigate the timing of the neural computations that extract them, and whether they depend on common neural circuits. We show that hue and luminance-contrast polarity can be decoded from MEG data and, with lower accuracy, both features can be decoded across changes in the other feature. These results are consistent with the existence of both common and separable neural mechanisms. The decoding time course is earlier and more temporally precise for luminance polarity than hue, a result that does not depend on task, suggesting that luminance contrast is an updating signal that separates visual events. Meanwhile, cross-temporal generalization is slightly greater for representations of hue compared to luminance polarity, providing a neural correlate of the preeminence of hue in perceptual grouping and memory. Finally, decoding of luminance polarity varies depending on the hues used to obtain training and testing data. The pattern of results is consistent with observations that luminance contrast is mediated by both L-M and S cone sub-cortical mechanisms.

The color appearance of curved transparent objects

Studies on colored transparent objects have elucidated potential mechanisms, but these studies have mainly focused on flat filters overlaying flat backgrounds. While they have provided valuable insight, these studies have not captured all aspects of transparency, like caustics, specular reflections/highlights, and shadows. Here, we investigate color-matching experiments with curved transparent objects for different matching stimuli: a uniform patch and a flat filter. Two instructions were tested: simply match the color of the glass object and the test element (patch and flat filter) or match the color of the dye that was used to tint the transparent object (patch). Observers’ matches differed from the mean, the most frequent, and the most saturated color of the transparent stimuli, whereas the brightest regions captured the chromaticity, but not the lightness, of patch matches. We applied four models from flat filter studies: the convergence model, the ratios of either the means (RMC) or standard deviations (RSD) of cone excitations, and a robust ratio model. The original convergence model does not fully generalize but does not perform poorly, and with modifications, we find that curved transparent objects cause a convergence of filtered colors toward a point in color space, similar to flat filters. Considering that, the RMC and robust ratio models generalized more than the RSD, with the RMC performing best across the stimuli we tested. We conclude that the RMC is probably the strongest factor for determining the color. The RSD seems instead to be related to the perceived “clarity” of glass objects.

Color Space Geometry Uncovered with Magnetoencephalography

The geometry that describes the relationship among colors, and the neural mechanisms that support color vision, are unsettled. Here, we use multivariate analyses of measurements of brain activity obtained with magnetoencephalography to reverse-engineer a geometry of the neural representation of color space. The analyses depend upon determining similarity relationships among the spatial patterns of neural responses to different colors and assessing how these relationships change in time. We evaluate the approach by relating the results to universal patterns in color naming. Two prominent patterns of color naming could be accounted for by the decoding results: the greater precision in naming warm colors compared to cool colors evident by an interaction of hue and lightness, and the preeminence among colors of reddish hues. Additional experiments showed that classifiers trained on responses to color words could decode color from data obtained using colored stimuli, but only at relatively long delays after stimulus onset. These results provide evidence that perceptual representations can give rise to semantic representations, but not the reverse. Taken together, the results uncover a dynamic geometry that provides neural correlates for color appearance and generates new hypotheses about the structure of color space.

Multiplicative modulations enhance diversity of hue-selective cells

There is still much to understand about the brain's colour processing mechanisms and the transformation from cone-opponent representations to perceptual hues. Moreover, it is unclear which area(s) in the brain represent unique hues. We propose a hierarchical model inspired by the neuronal mechanisms in the brain for local hue representation, which reveals the contributions of each visual cortical area in hue representation. Hue encoding is achieved through incrementally increasing processing nonlinearities beginning with cone input. Besides employing nonlinear rectifications, we propose multiplicative modulations as a form of nonlinearity. Our simulation results indicate that multiplicative modulations have significant contributions in encoding of hues along intermediate directions in the MacLeod-Boynton diagram and that our model V2 neurons have the capacity to encode unique hues. Additionally, responses of our model neurons resemble those of biological colour cells, suggesting that our model provides a novel formulation of the brain's colour processing pathway.

The Verriest Lecture: Adventures in blue and yellow

Conventional models of color vision assume that blue and yellow (along with red and green) are the fundamental building blocks of color appearance, yet how these hues are represented in the brain and whether and why they might be special are questions that remain shrouded in mystery. Many studies have explored the visual encoding of color categories, from the statistics of the environment to neural processing to perceptual experience. Blue and yellow are tied to salient features of the natural color world, and these features have likely shaped several important aspects of color vision. However, it remains less certain that these dimensions are encoded as primary or "unique" in the visual representation of color. There are also striking differences between blue and yellow percepts that may reflect high-level inferences about the world, specifically about the colors of light and surfaces. Moreover, while the stimuli labeled as blue or yellow or other basic categories show a remarkable degree of constancy within the observer, they all vary independently of one another across observers. This pattern of variation again suggests that blue and yellow and red and green are not a primary or unitary dimension of color appearance, and instead suggests a representation in which different hues reflect qualitatively different categories rather than quantitative differences within an underlying low-dimensional "color space."