Russian blues reveal the limits of language influencing colour discrimination

Exploring the categorical nature of colour perception: Insights from artificial networks

The electromagnetic spectrum of light from a rainbow is a continuous signal, yet we perceive it vividly in several distinct colour categories. The origins and underlying mechanisms of this phenomenon remain partly unexplained. We investigate categorical colour perception in artificial neural networks (ANNs) using the odd-one-out paradigm. In the first experiment, we compared unimodal vision networks (e.g., ImageNet object recognition) to multimodal vision-language models (e.g., CLIP text-image matching). Our results show that vision networks predict a significant portion of human data (approximately 80%), while vision-language models account for the remaining unexplained data, even in non-linguistic experiments. These findings suggest that categorical colour perception is a language-independent representation, though it is partly shaped by linguistic colour terms during its development. In the second experiment, we explored how the visual task influences the colour categories of an ANN by examining twenty-four Taskonomy networks. Our results indicate that human-like colour categories are task-dependent, predominantly emerging in semantic and 3D tasks, with a notable absence in low-level tasks. To explain this difference, we analysed kernel responses before the winner-takes-all stage, observing that networks with mismatching colour categories may still align in underlying continuous representations. Our findings quantify the dual influence of visual signals and linguistic factors in categorical colour perception and demonstrate the task-dependent nature of this phenomenon, suggesting that categorical colour perception emerges to facilitate certain visual tasks.

Division and spreading of attention across color

Biological systems must allocate limited perceptual resources to relevant elements in their environment. This often requires simultaneous selection of multiple elements from the same feature dimension (e.g. color). To establish the determinants of divided attentional selection of color, we conducted an experiment that used multicolored displays with four overlapping random dot kinematograms that differed only in hue. We manipulated (i) requirement to focus attention to a single color or divide it between two colors; (ii) distances of distractor hues from target hues in a perceptual color space. We conducted a behavioral and an electroencephalographic experiment, in which each color was tagged by a specific flicker frequency and driving its own steady-state visual evoked potential. Behavioral and neural indices of attention showed several major consistencies. Concurrent selection halved the neural signature of target enhancement observed for single targets, consistent with an approximately equal division of limited resources between two hue-selective foci. Distractors interfered with behavioral performance in a context-dependent fashion but their effects were asymmetric, indicating that perceptual distance did not adequately capture attentional distance. These asymmetries point towards an important role of higher-level mechanisms such as categorization and grouping-by-color in determining the efficiency of attentional allocation in complex, multicolored scenes.

Contingent capture by color is sensitive to categorical color perception

Contingent capture (CC) theory postulates that attention can only be captured by top-down matching stimuli. Although the contingent capture of attention is a well-known and thoroughly studied phenomenon, there is still no consensus on the characteristics of the top-down template which guides the search for colors. We tried to replicate the classical contingent capture effect on color (Experiment 1) and then added linguistic processing to this perceptual effect (Experiment 2). In Experiment 1, attention was indeed captured by the cues of the same color as the target, while the cues of different colors were successfully ignored. In Experiment 2, the cue color was never identical to the target color but would either belong to the same linguistic category or not (i.e., linguistic matching and linguistic nonmatching cues). In both cases, cues were made to be equally perceptually distant from the target. Although, attention was captured by both cue types, the degree of capture was significantly higher for linguistic matching cues. Our research replicated the classic contingent capture effect but on color, and also demonstrated the effect of color categories in the search task. In short, we demonstrated the effect of color categories in the search task. Results show that the template for color search contains physical characteristics of color, as well as information about color category names.

Acquisition of colour categories through learning: Differences between hue and lightness

Colour categories are acquired through learning, but the nature of this process is not fully understood. Some category distinctions are defined by hue (e.g. red/purple) but other by lightness (red/pink). The aim of this study was to investigate if the acquisition of key information for making accurate cross-boundary discriminations poses different challenges for hue-defined as opposed to lightness-defined boundaries. To answer this question, hue- and lightness-learners were trained on a novel category boundary within the GREEN region of colour space. After training, hue- and lightness-learners as well as untrained controls performed delayed same-different discrimination for lightness and hue pairs. In addition to discrimination data, errors during learning and category-labelling strategies were examined. Errors during learning distributed non-uniformly and in accordance with the Bezold-Brücke effect, which accounts for darker colours at the green-blue boundary appearing greener and lighter colours appearing bluer. Only hue-learners showed discrimination improvements due to category boundary acquisition. Thus, acquisition is more efficient for hue-category compared to lightness-category boundaries. Almost all learners reported using category-labelling strategies, with hue-learners almost exclusively using 'green'/'blue' and lightness learners using a wider range of labels, most often 'light'/'dark'. Thus, labels play an important role in colour category learning and such labelling does not conform to everyday naming: here, the label 'blue' is used for exemplars that would normally be named 'green'. In conclusion, labelling serves the purpose of highlighting key information that differentiates exemplars across the category boundary, and basic colour terms may be particularly effective in facilitating such attentional guidance.

Florence "blues" are clothed in triple basic terms

Psycholinguistic studies provide evidence that Italian has more than one basic color term (BCT) for “blue”: consensually, blu denotes “dark blue,” while “light-and-medium blue,” with diatopic variation, is termed either azzurro or celeste. For Tuscan speakers (predominantly from Florence), the BLUE area is argued to linguistically differentiate between azzurro “medium blue” and celeste “light blue.” We scrutinized “basicness” of the three terms. Participants (N = 31; university students/graduates born in Tuscany) named each chip of eight Munsell charts encompassing the BLUE area (5BG-5PB; N = 237) using an unconstrained color-naming method. They then indicated the “best exemplar” (focal color) of blu, azzurro and celeste. We found that frequencies of the three terms and of term derivatives were comparable. Referential meaning of blu, azzurro, and celeste was estimated in CIELAB space as L*a*b*-coordinates of the mean of focal colors and as “modal” categories, that is, dispersion around the mean. The three “blue” terms were distinct on both measures and separated along all three CIELAB dimensions but predominantly along the L*-dimension. Our results provide evidence that Tuscan speakers require all three terms for naming the BLUE area, categorically refined along the lightness dimension. Furthermore, celeste appears to be a third BCT for “blue,” along with commonly considered BCTs azzurro and blu. The “triple blues” as BCTs for Tuscan speakers are in contrast with outcomes of two “blue” basic terms estimated by using the same methodology in two other locations in Italy—azzurro and blu (Verona, Veneto region) or celeste and blu (Alghero, Sardinia).

Do You See What I See? Diversity in Human Color Perception

In our tendency to discuss the objective properties of the external world, we may fail to notice that our subjective perceptions of those properties differ between individuals. Variability at all levels of the color vision system creates diversity in color perception, from discrimination to color matching, appearance, and subjective experience, such that each of us lives in a unique perceptual world. In this review, I discuss what is known about individual differences in color perception and its determinants, particularly considering genetically mediated variability in cone photopigments and the paradoxical effects of visual environments in both contributing to and counteracting individual differences. I make the case that, as well as being of interest in their own right and crucial for a complete account of color vision, individual differences can be used as a methodological tool in color science for the insights that they offer about the underlying mechanisms of perception. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Language Modulates Categorical Effects of Moving Color Objects

Categorical perception (CP) of color claims that colors from different linguistic categories are discriminated more easily than those from the same category, suggesting that language may interact with visual perception. However, controversy remains regarding whether CP effects derive from language. Recently, CP effects were found in a dynamic paradigm named multiple object tracking (MOT). Here, we investigated whether this kind of CP is dependent on online use of language. We found that the CP effects are reduced by verbal interference when the participants were required to memorize color words during tracking (Experiment 2) but not when the interference stimuli were digits (Experiment 1). Our study suggested that the CP effects in tracking depend partly on online use of language and that the difficulty of verbal interference tasks influences the capability of disrupting CP.

Bongard and Smirnov on the tetrachromacy of extra-foveal vision

In Moscow in the 1950's, the physicist M. M. Bongard developed the use of silent substitution to establish the number of dimensions of human or animal colour vision and to derive colour-matching functions either for whole organisms or for individual neuronal channels. In 1956, he and his colleague M. S. Smirnov reported that extra-foveal human vision was tetrachromatic when tested by the silent-substitution method that they called 'replacement colorimetry'. In the steady state, trichromatic matches were possible in extra-foveal regions, but transients were visible when one such match was replaced by another. If, however, a match was made with four primaries, then a silent substitution was possible; and such matches - unlike trichromatic ones - were stable with light level and with changes in the state of chromatic adaptation. Bongard and Smirnov believed that the fourth receptor had the spectral sensitivity of the rods, but of course they were working long before the discovery of intrinsically photosensitive retinal ganglion cells. On the fiftieth anniversary of Bongard's grievous death, we provide a translation of Bongard and Smirnov's paper on the tetrachromacy of extra-foveal vision. In a commentary, we give the background to their work and provide further details of their apparatus and procedure. We briefly discuss related research and the reception in the West of Bongard and Smirnov's claims. We suggest that an analogy can be made between the tetrachromacy of the parafovea and the 'weak tetrachromacy' of heterozygotes for anomalous colour vision, whose trichromatic matches are not stable with chromatic adaptation.

In the eye of the beholder: Is color classification consistent among human observers?

Colorful displays have evolved in multiple plant and animal species as signals to mutualists, antagonists, competitors, mates, and other potential receivers. Studies of color have long relied on subjective classifications of color by human observers. However, humans have a limited ability to perceive color compared to other animals, and human biological, cultural, and environmental variables can influence color perception. Here, we test the consistency of human color classification using fruit color as a model system. We used reflectance data of 67 tropical fruits and surveyed 786 participants to assess the degree to which (a) participants of different cultural and linguistic backgrounds agree on color classification of fruits; and (b) human classification to a discrete set of commonly used colors (e.g., red, blue, green) corresponds to natural clusters based on light reflectance measures processed through visual systems of other animals. We find that individual humans tend to agree on the colors they attribute to fruits across language groups. However, these colors do not correspond to clearly discernible clusters in di- or tetrachromatic visual systems. These results indicate that subjective color categorizations tend to be consistent among observers and can be used for large synthetic studies, but also that they do not fully reflect natural categories that are relevant to animal observers.

Color Vision: Decoding Color Space

A new study has used magnetoencephalography to track cortical responses to color as they emerge in time. Similarities and differences within these neural responses parallel characteristics of the perceptual experience of color.

Ensemble coding of color and luminance contrast

Ensemble coding has been demonstrated for many attributes including color, but the metrics on which this coding is based remain uncertain. We examined ensemble percepts for stimulus sets that varied in chromatic contrast between complementary hues, or that varied in luminance contrast between increments and decrements, in both cases focusing on the ensemble percepts for the neutral gray stimulus defining the category boundary. Each ensemble was composed of 16 circles with four contrast levels. Observers saw the display for 0.5 s and then judged whether a target contrast was a member of the set. False alarms were high for intermediate contrasts (within the range of the ensemble) and fell for higher or lower values. However, for ensembles with complementary hues, gray was less likely to be reported as a member, even when it represented the mean chromaticity of the set. When the settings were repeated for luminance contrast, false alarms for gray were higher and fell off more gradually for out-of-range contrasts. This difference implies that opposite luminance polarities represent a more continuous perceptual dimension than opponent-color variations, and that "gray" is a stronger category boundary for chromatic than luminance contrasts. For color, our results suggest that ensemble percepts reflect pooling within rather than between large hue differences, perhaps because the visual system represents hue differences more like qualitatively different categories than like quantitative differences within an underlying color "space." The differences for luminance and color suggest more generally that ensemble coding for different visual attributes might depend on different processes that in turn depend on the format of the visual representation.