Category Archives: colors

Linguistic effects on unconscious color perception

The post headline may seem an oxymoron , but it is indeed possible to perceive colors unconsciously. How do we know that someone has perceived a color, when he doesn’t report the qualia. We do so by measuring the effects on subsequent behavior. Consider subliminal priming. Consider a subliminal stroop test, in which color patches are presented subliminally and then color lexical terms are presented consciously in neutral (say black) ink. I’m sure with this subliminal modified stroop test one could still get a color and lexical term interaction effect; the point is that color , when not perceived, may still influence subsequent behavior.

The experimental paradigm in this PNAS article did not go so far, but restricted itself to color stimuli that was not attended to; that is, the color was indeed perceived, but it was not attended to (the task involved attention to form rather than color) and so as the color was not attended to, they presumed that the effects that the color information would have on behavior would be completely unconscious. I’m not convinced, but that doesn’t invalidate their otherwise very beautiful study that once again provides strong evidence for the milder version of the Sapir-Whorf hypothesis, at least as it relates to categorical color perception. 

Now, I have written previously about Sapir- Whorf hypothesis in general,  and in particular about the ability of Russians( who have two separate terms for light and dark blue) to visually discriminate between light and dark blue significantly better than their English counterparts, thanks to their rich color lexicon; so this new study that found that Greek-natives (who also have different lexical terms for light and dark blue) were superior to English-natives in terms of discriminating categorical color perception for light and dark blue color, did not come as a surprise or seemed ground-breaking; but there are important differences both in terms of the procedures used and the processes involved.

This study, works at pre-attentive level, uses physiological measures like ERP (they studied the vMMN – visual Mismatch Negativity) to determine whether the color stimuli had differential effect even at pre-attentive perception and thus provides independent evidence for the effect of Language on color perception. I’ll now quote from the abstract and discussion section:

It is now established that native language affects one’s perception of the world. However, it is unknown whether this effect is merely driven by conscious, language-based evaluation of the environment or whether it reflects fundamental differences in perceptual processing between individuals speaking different languages. Using brain potentials, we demonstrate that the existence in Greek of 2 color terms—ghalazio and ble—distinguishing light and dark blue leads to greater and faster perceptual discrimination of these colors in native speakers of Greek than in native speakers of English. The visual mismatch negativity, an index of automatic and preattentive change detection, was similar for blue and green deviant stimuli during a color oddball detection task in English participants, but it was significantly larger for blue than green deviant stimuli in native speakers of Greek. These findings establish an implicit effect of language-specific terminology on human color perception.

This study tested potential effects of color terminology in different languages on early stages of visual perception using the vMMN, an electrophysiological index of perceptual deviancy detection. The vMMN findings show a greater distinction between different shades of blue than different shades of green in Greek participants, whereas English speakers show no such distinction. To our knowledge, this is the first demonstration of a relationship between native language and unconscious, preattentive color discrimination rather than simply conscious, overt color categorization.

To conclude, our electrophysiological findings reveal not only an effect of the native language on implicit color discrimination as indexed by preattentive change detection but even electrophysiological differences occurring as early as 100 ms after stimulus presentation, a time range associated with activity in the primary and secondary visual cortices (22). We therefore demonstrate that language-specific distinctions between 2 colors affect early visual processing, even when color is task irrelevant. At debriefing, none of the participants highlighted the critical stimulus dimension tested (luminance) or reported verbalizing the colors presented to them. The findings of the present study establish that early stages of color perception are unconsciously affected by the terminology specific to the native language. They lend strong support to the Whorfian hypothesis by demonstrating, for the first time, differences between speakers of different languages in early stages of color perception beyond the observation of high-level categorization and discrimination effects strategically and overtly contingent on language specific

I think this fits in with predictive models of perception, wherein, earlier stages of visual processing, that are unrelated to color discrimination, may still be primed by color information that one has obtained earlier and has processed pre-attentively. I, as always , am excited by this proof of whorfian hypotheses.

Encephalon #54 : up and running

The 54th edition of Encephalon, the premium brain carnival , is now up and running on the Neurophilosophy blog. There are many interesting articles there, like the article on color vision (I too have written about color vision extensively in the past-), so go and have a look, and savor what fancies you!

‘A’ is for a RED apple and ‘V’ is for a PURPLE van!

New research has unearthed that the grapheme-color synesthesia is not idiosyncratic , but follows some typical patterns. Grapheme – color synesthesia is one of the common types of synesthesia wherein one sees color associated with visualizing an alphabet / letter. Thus, whenever one see the alphabet ‘A’ one may also have a perception of color ‘red’. Till now, it was believed that this association of colors with alphabets was random and idiosyncratic; but new research has now revealed that it follows a pattern with most synesthetes more likely to associate typical colors with alphabets and for example report ‘A’ as red and V as ‘purple’.

Jamie Ward’s team that found this phenomenon speculates that the hue could be associated with the frequency of the word. Thus, as ‘A’ is a frequently used word it is associated with a common color ‘red’. ‘V’ which is infrequently used in the lexicon is associated with a similar infrequently encountered color purple. I am not sure how their new study is different from their earlier study that also found thus association and I believe that there would be some truth to their theory. however, the science daily article also talks about saturation. So I though I would jump in.

Colors can be conceptualized as per the HSV/ HSL or HSB system and understood in terms of hue , saturation and value/ brightness. I would personally be inclined to interpret the ‘A’ is red and ‘V’ is purple mapping as the outcome of a mapping of the alphabet order (a, b, c, ….x, y, z) to the color order in the rainbow / hue dimension (VIBGYOR). ‘A’ is one end ofthe spec trim and thus red in color, while ‘V’ is on another end of spectrum and thus more likely to be ‘violet’ in color. The frequency of usage of the alphabet should ideally map to brightness/ value of the synesthete color as in color space value is mapped to the amount of light reflected. saturation or ‘purity’ of color is a bit difficult to map onto the alphabet; but one could venture forth and suggest it has to do with how ‘pure’ the alphabet is ….is it always pronounced in one way….or are their multiple pronunciations associated with the same alphabet.

Mapping a linear progression of hues along VIBGYOR axis to alphabet order or numeral oredr is not that hard to envisage or visualize. If neurons of adjacent colorotopic and lexicotopic maps (assuming there are such maps for color and lexicon in the brain) in the brain overlap/ cross-over we would have the phenomenon of grapheme-color synestehesia that accounted for the commonalities in hues and alphabet association. However, we just know of retinotopic sort of maps in brains and these fit in with our existing knowledge. How the brain stores information about saturation/ value and correspondingly frequency and purity of alphabets and maps between the too, can lead to novel insights as to how information is stored in the brain.

I am excited and believe that we are on verge of breaking new ground ( I haven’t read the new Jamie ward paper though yet) and I have my own theories on why color is so important and may provide us many more clues (color and music are two most interesting phenomenon I believe). Are you excited? Do you have any theories?

PS: I just found that Jamie Ward is writing a book called “The Frog who Croaked Blue: Synaesthesia and the Mixing of the Senses” in which he recounts the experience of a synesthete who heard frog croaks as blue and chirping of cricket as red. To me this immediately conjures up the colortopic map with red at one end (high, feminine, shrill noises) and blue at the other (more manly, bass noise). This mapping of sound with colors may again follow the hue, saturation and value (three dimensions) with loudness of sound being proportional to the value of color being perceived and the hue and pitch mapped. Also , this may be an idiosyncratic experience, or this may be true of the species as a whole that we map more shrill noises to red and soothing and duller sounds to blue/ violet.

Categorical color perception: the language effect

I touched on the sapir-whorf hypothesis and how Russians are better able to do better Categorical Perception (CP) of color, thanks to the fact that they have a richer color terms lexicon than English, last month.

I have also covered the research of P. Kay earlier regarding color terms and their evolution. Now a new PNAS paper by Kay et al shows that while the left hemisphere(LH) , which is involved in language, shows superior CP effect in adults, the reverse trend is shown in infants i.e.e the infants show a stronger CP of colors when the stimuli is presented to Left Visual field (LVF) and hence processed by RH.

Their hypothesis was that while the CP of colors in adults is mediated by language, the CP in infants is non-verbal and the cP in adults may or may not build on this childhood CP ability. The results go on to show that not only doers language affect the left hemisphere dominance on categorical perception of colors ; it does so by overriding an inborn RH dominance for the same task. thus, there is no doubt that the color term lexicon heavily influences how we categorize colors in the adulthood.

Here is their conclusion:

Evidence suggesting that color CP varies cross-linguistically, and that color CP is eliminated by verbal interference, has supported the hypothesis that color CP depends on access to lexical codes for color . However, the finding of color category effects in prelinguistic infants and toddlers has led others to argue that language cannot be the only origin of the effect . The current study finds evidence to support both positions. Color CP is found in 4- to 6-month-old infants, replicating previous infant studies. However, the absence of a category effect in the LH for infants, but the presence of a greater LH than RH category effect for adults, suggests that language-driven CP in adults may not build on prelinguistic CP, but that language instead imposes its categories on a LH that is not categorically prepartitioned. The current findings may therefore suggest a compromise between the two positions: there is a form of CP that is nonlinguistic and RH based (found in infancy) and a form of CP that is lexically influenced and biased to the LH (found in adulthood). Color CP is found for both infants and adults, but the contribution of the LH and RH to color CP appears to change across the life span.

Russsinas have a richer discriminative experience of light and dark blue qualia

I have blogged extensively earlier regarding language, color and the sapir -whorf hypothesis. My position in the above is clear, I lean towards the sapir-whorf hypothesis and a mild form of linguistic determinism. Now a new study (which I had missed earlier) by Lera Boroditsky presents further corroborating evidence that language influences even such basic functions as color perception. As per their 2007 PNAS paper, Russians are better (more speedily) able to distinguish between the light blue and dark blue color in an objective color perception task, thanks to the fact that Russian has a different color term for dark blue and a different one for the light blue. It is an excellent paper and I present some excerpts from the introduction:

Different languages divide color space differently. For example,the English term ‘‘blue’’ can be used to describe all of the colors in Fig. 1. Unlike English, Russian makes an obligatory distinction between lighter blues (‘‘goluboy’’) and darker blues (‘‘siniy’’). Like other basic color words, ‘‘siniy’’ and ‘‘goluboy’’ tend to be learned early by Russian children (1) and share many of the usage and behavioral properties of other basic color words (2). There is no single generic word for ‘‘blue’’ in Russian that can be used to describe all of the colors in Fig. 1 (nor to adequately translate the title of this work from English to Russian). Does this difference between languages lead to differences in how people discriminate colors?

The question of cross-linguistic differences in color perception has a long and venerable history (e.g., refs. 3–14) and has been a cornerstone issue in the debate on whether and how much language shapes thinking (15). Previous studies have found cross-linguistic differences in subjective color similarity judgments and color confusability in memory (4, 5, 10, 12, 16). For example, if two colors are called by the same name in a language, speakers of that language will judge the two colors to be more similar and will be more likely to confuse them in memory compared with people whose language assigns different names to the two colors. These cross-linguistic differences develop early in children, and their emergence has been shown to coincide with the acquisition of color terms (17). Further, cross-linguistic differences in similarity judgments and recognition memory can be disrupted by direct verbal interference (13, 18) or by indirectly preventing subjects from using their normal naming strategies (10), suggesting that linguistic representations are involved online in these kinds of color judgments.

Because previous cross-linguistic comparisons have relied on memory procedures or subjective judgments, the question of whether language affects objective color discrimination performance has remained. Studies testing only color memory leave open the possibility that, when subjects make perceptual discriminations among stimuli that can all be viewed at the same time, language may have no influence. In studies measuring subjective similarity, it is possible that any language-congruent bias results from a conscious, strategic decision on the part of the subject (19). Thus, such methods leave open the question of whether subjects’ normal ability to discriminate colors in an objective procedure is altered by language.

Here we measure color discrimination performance in two language groups in a simple, objective, perceptual task. Subjects were simultaneously shown three color squares arranged in a triad (see Fig. 1) and were asked to say which of the bottom two color squares was perceptually identical to the square on top.

This design combined the advantages of previous tasks in a way that allowed us to test for the effects of language on color perception in an objective task, with an implicit measure and minimal memory demands.

First, the task was objective in that subjects were asked to provide the correct answer to an unambiguous question, which they did with high accuracy. This feature of the design addressed the possibility that subjects rely only on linguistic representations when faced with an ambiguous task that requires a subjective judgment. If linguistic representations are only used to make subjective judgments in ambiguous tasks, then effects of language should not show up in an objective unambiguous task with a clear correct answer.

Second, all stimuli involved in a perceptual decision (in this case, the three color squares) were present on the screen simultaneously and remained in full view until the subjects responded. This allowed subjects to make their decisions in the presence of the perceptual stimulus and with minimal memory demands.

Finally, we used the implicit measure of reaction time, a subtle aspect of behavior that subjects do not generally modulate explicitly. Although subjects may decide to bias their decisions in choosing between two options in an ambiguous task, it is unlikely that they explicitly decide to take a little longer in responding in some trials than in others.

In summary, this design allowed us to test subjects’ discrimination performance of a simple, objective perceptual task. Further, by asking subjects to perform these perceptual discriminations with and without verbal interference, we are able to ask whether any cross-linguistic differences in color discrimination depend on the online involvement of language in the course of the task.

The questions asked here are as follows. Are there crosslinguistic differences in color discrimination even for simple, objective, perceptual discrimination tasks? If so, do these differences depend on the online involvement of language? Previous studies with English speakers have demonstrated that verbal interference changes English speakers’ performance in speeded color discrimination (21) and in visual searching (22, 23) across the English blue/green boundary. If a color boundary is present in one language but not another, will the two language groups differ in their perceptual discrimination performance across that boundary? Further, will verbal interference affect only the performance of the language group that makes this linguistic distinction?

They then go on to discuss their experimental setup (which I recommend you go and read). Finally they present their findings:

We found that Russian speakers were faster to discriminate two colors if they fell into different linguistic categories in Russian (one siniy and the other goluboy) than if the two colors were from the same category (both siniy or both goluboy). This category advantage was eliminated by a verbal, but not a spatial, dual task. Further, effects of language were most pronounced on more difficult, finer discriminations. English speakers tested on the identical stimuli did not show a category advantage under any condition. These results demonstrate that categories in language can affect performance of basic perceptual color discrimination tasks. Further, they show that the effect of language is online, because it is disrupted by verbal interference. Finally, they show that color discrimination performance differs across language groups as a function of what perceptual distinctions are habitually made in a particular language.

They end on a philosophical note:

The Whorfian question is often interpreted as a question of whether language affects nonlinguistic processes. Putting the question in this way presupposes that linguistic and nonlinguistic processes are highly dissociated in normal human cognition, such that many tasks are accomplished without the involvement of language. A different approach to the Whorfian question would be to ask the extent to which linguistic processes are normally involved when people engage in all kinds of seemingly nonlinguistic tasks (e.g., simple perceptual discriminations that can be accomplished in the absence of language). Our results suggest that linguistic representations normally meddle in even surprisingly simple objective perceptual decisions.

To me this is another important paper that puts sapir-whorf hypothesis on the forefront. I would love to hear from those who do not endorse the spair-whorf hypothesis as to what they make of these results?

hat tip: Neuroanthropology blog.

The evolutionary trajectory of color vision

In a recent comment, a reader of this blog had highlighted some concerns regarding an evolutionary trajectory of color vision evolution that I had proposed in conjunction with the evolution of color terms in human languages and as a possible explanation for the linguistic trend. At that time, I had proposed a possible evolutionary scenario, without doing due diligence investigation of existing evolutionary theories of color vision, as my post had more of a linguistic and developmental focus and the evolutionary conjecture was just that- a conjecture, which, if found true, would lend more credence to my linguistic trend. Thanks to Andreas, I reviewed the literature on color vision evolution and was surprised to find some support for my theorization.

Before I discuss the color vision evolution, I’ll strongly recommended reading two posts on the evolution of color vision and the evolution of retinal structures (more Avian focus here) , for getting some basic familiarities with the retinal structures involved in color vision and how they might have evolved.

To recap,

An animal has color vision if it has the capability of discriminating lights (scattered light as well as light sources) on the basis of the lights’ spectral content, even when those lights are of equal subjective brightness.

The front end requirement for such a system is that the animal must have at least two different spectral classes of receptor, where each class is defined by the sensitivity of the receptor to light as a function of wavelength.

The above succinctly defines what we usually mean by color vision. You can either have a dichromatic color vision, when you have two differently tuned receptors to detect different light wavelengths and the different signal combinations from these receptors yield different hues; or you can have trichroimatic / tetrachromatic vision where three/four independent color signals are combined to yield an entire Hue range. One familiar with the RGB color system used in computers, would note that it is based on the assumption of 3 pure colors, which can be mixed in different amounts to yield most of the color hues we see on the monitor.Pigeons, and birds in general, have a tetrachromatic color vision.

Now for some basic visual circuitry:

The retinal structures involved in vision, in mammals, are, pohotorecptors (classified as cones and rods), horizontal, bipolar, amaracine and ganglion cells.

However, for all vertebrates (mammals as well as reptiles and birds) and invertebrates as well, the receptor mechanism is conserved and is basically the same and we will discuss that first:

The first step in the transduction of light energy to a neural signal is the light-induced isomerization (change of shape) of a chromophore, specifically a vitamin A derivative. Each chromophore is bound to a membrane protein called an opsin. The main function of the opsin is to change shape after light absorption triggers the isomerization of the chromophore: the opsin is an enzyme that is activated by the chromophore’s isomerization. However, because of the linkage between the opsin and the chromophore, the opsin also serves to tune the wavelength dependence of the light induced isomerization reaction in the chromophore. That is, the chromophore’s sensitivity to light at a given wavelength is established in part by the opsin–different opsins (i.e. opsins with different amino acid sequences) bound to identical chromophores will have different absorption probabilities at each wavelength. The result is that photoreceptors which express the gene for only one type of opsin will form a different class than photoreceptors that express a gene coding for a different opsin. Although there are other mechanisms that animals could use to differentiate photoreceptor classes (most notably some animals use more than one chromophore, and many vertebrates have colored oil droplets that screen individual receptors) it seems that the expression of only one of their possible opsin coding genes in each receptor is the mechanism that all animals use.

The above clarifies, that in mammals, we associate color vision with cones or specialized photoreceptors that contain a single pigment and are responsive to a single wavelength range. In reptiles, we also have double cones, wherein, two photopigment/ receptors are part of the same cell and then there are other mechanism like oil droplets that are also involved in color vision (but thankfully not in mammals). Rods are also a type of receptors, tuned to a frequency, but we normally do not associate rods with color vision, because they are usually used for night vision and their signals are not combined to create the color hue; yet a limited form of monochromatic color vision is possible by having a combination of one rod and one cone receptor types.

Next we need to differentiate between the rhabodermic eyes of invertebrates (based on r-opsin and the ciliary eyes of vertebrates based on c-opsins. Pharyngula does an excellent job here.

Eyes can be further categorized as rhabdomeric or ciliary by the nature of the cellular elements that make up the photoreceptors, by the kind of opsin molecule used to transduce the light signal, and by the signaling pathway used to convert a conformation change of the opsin molecule into a change in the electrical potential across the cell membrane.

As many accounts of color vision evolution focus on the phylogentic tress of opsin genes evolution to make their case, it is important to distinguish between the levels of analysis. All the known Opsin genes can be classifies in seven sub-families: two of these the r-opsin families and the c-opsin families are pertinent to, and expressed in, the photorecptors found in invertebrates and vertebrates respectively.

Thus, if one wants to focus on mammal color vision evolution, one needs to focus on c-opsins mostly. Many studies have been conducted over these and the phylogentic data indicates that the vertebrate opsins too form a neat tree with five sub-families relevant for (color) vision and 3 other sub-families having non-visual functions.

Thus, in mammals we have a five types of opsins : one rhodopsin-type and expressed in rods, and four other chromatic types (detecting Red, Blue, Green and U/V colors) and expressed in cones.

One should pause here and note that the human S(short) or blue receptor actually belongs to the U/V (S) family; while the human L (red) and M (green) receptors both belong to the Red (L) family.

These 5 opsin families (Red, Gree, Blue, U/V and Rhodipsin)have been variously characterized as (L, ML, MS, S and Rh) or as(RH1, RH2, LWS, SWS1 and SWS2).

With this background, information, we can now go straight to the heart of the problem: the evolutionary trajectory of these different receptors / opsins and how the color vision evolved in humans. I’ll limit the discussion here to mammals first and then to primates , as my original thesis that color terms evolution follows the color vision evolution requires the analysis to happen only in that time frame in which linguistic abilities make sense. Assuming some proto-language in Apes and primates, it is reasonable to expect that whatever sequence of color terms we see in languages, would reflect the successive levels of color vision as experienced by Primates, and would be independent of how color was perceived in invertebrates. (I’m sure no one contends that the color terms of human languages should capture the early chromatic experiences of invertebrates).

Although I do not buy the bottleneck theory of Mammal evolution -stock, barrel and lock – I believe we can take that as a reasonable starting point. It posits that mammals were reduced to being a nocturnal burrowing species during the age pf the dinosaurs and thus were reduced to having just the rods, and lost the earlier, cones, double cones and oil pigments that reptiles still have. In any case, in mammals, rods seem to be older and more conserved than cones. (Pat on the back: one claim originally made defended to satisfaction!!)

Amongst vertebrates, the rod opsin seems to be the most conserved; cone opsins have arisen principally by duplication and subsequent mutation of the rod opsin gene.


Which of the two primary classes, rods or cones, is the ancestral photoreceptor? Given the tremendous variation seen photoreceptors across vertebrate and invertebrate species, this in not an easy question to answer based on simple phylogenetic assumptions. In addition, it is often difficult to clearly distinguish certain rod and cone types from each other, or classify them into one or the other category. Rods appear to be relatively more conserved in vertebrates in terms of pigments and structure than cones, and therefore could be considered the more ancestral form. However, rods in some respects are more morphologically complex than cones, having developed extreme sensitivity (capable of detecting as little as one photon of light).

Now,coming to the evolution(or re-evolution) of the cones or the chromatic system in mammals, it is instructive to pause here and note that having three cones does not necessarily mean that the two species will have the same qualia of color hues.

If we restrict ourselves to animals which have the same number of receptor classes, might we expect that their color vision systems are equivalent? The answer is a resounding no. Let’s compare the color vision systems of two animals that both have three photopic (e.g. active under bright illumination) photoreceptor classes. One is the human, the other is the honey bee (specifically the worker–I don’t know how the other castes are endowed). Does anybody here think that what a bee sees when it looks at a rainbow has the same appearance as what we see? We’ll ignore optical polarization (which the bee is sensitive to and we’re not) and focus on what we can infer about “color” based on, among other things, our knowledge of the bee’s receptor classes. To begin with, at the inside of the rainbow where the violet-appearing light fades off to invisibility for us, the bee will still see more rainbow. On the outside, where we see red, the bee would see nothing for although bees have an ability to see what for us is UV, we have the ability to see what bees might call infrared.

Also, it is instructive to note here how a higher level chromatic vision (dichromatic for instance) may arise form a lower level chromatic vision (monochromatic in this example). Although, along with the photoreceptors, we will need additional supporting neural wiring, in both the retina and the brain, for the opponent-processing mediated color perception to take place, we will restrict the discussion to the emergence of a new photoreceptor.

A new photoreceptor, may come into existence by a duplication and polymorphisms of an existing receptor (opsin) gene. The new receptor would have a slightly different frequency sensitivity than the original receptor and, by selectively expressing these two genes in different receptors, we can have two types of receptors. By processing and combining the two types of signals, one can now get dichromatic vision, from the original monochromatic vision.

Much confusion, in primate color vision evolution, depends on the fact that one takes as base the other mammals like dogs, and their blue-yellow world as a baseline from where to start. It should be emphasized that even though dogs may currently have two receptors, tuned to detect blue and yellow, we cannot conclude form that anything about humans or ancient ancestral mammals. In the human ancestry lineage, the dichromatic phase may have involved Red-Green perception. This is evident form the bee-human trichromatic example given above.

A very good paper summarizing the latest research on primate color evolution concludes that their are five types of primate color vision systems- beginning with a Monochromatic (L opsin only)_ system in nocturnal primates to a S + M+L (multiple copies) trichromatic system in humans.

It is interesting to note here that the human Green evolved, by replication and polymerization of the Red opsin present on the X chromosome. From the hierarchy of primate color systems, it is reasonable to conclude, that initially when we were nocturnal primates, we had a dysfunctional S-opsin gene and a functional L gene- conferring us the ability to perceive the red qualia to some extent.

In diurnal prosimians, the S become functional and they have two qualia- that of red and blue.

In the new world monkeys, the L gene is polymorphic (it is on X chromosome and as explained in the paper, if we have two alleles for that L gene, that encode for slightly different frequencies, then as females have two X chromosomes, they can have both the alleles; the males meanwhile have only one X chromosome; so at at a time they can have only one of the alleles present. By X chromosome inactivation process, all cells of a female new world monkey, will have only one of the alleles; but different cells may have different alleles expressed and thus, the females may have 3 types of receptors (one S type and two L types), thus endowing them with trichromatic vision. The Males meanwhile will have dichromatic vision, but as the gene is polymorphic, we will differences in their dichromatic perceptions. This is exactly what is observed.

The old world monkeys, have the full apparatus for trichromatic vision- with one S and two L genes. The second L (or rather M as it detects green) gene was formed by replication and polymorphisms of the L gene that detected red. thus, they had the qualia of Red, Blue and Green.

Lastly, the humans, are more or less the same as old world Monkeys; but their L gene shows polymorphisms. This has the effect of making some females tetrachromatic (as this polymorphisms will only affect females- only they have two copies of X chromosome) and it seems , that by fortuitous replication, we might get a fourth cone type in all humans. Till then, this polymorphisms will explain some of the color perception differences that we may exhibit.

Suffice it to say, that the evolution of color terms should follow the same trajectory- with Black and White (rod based) color terms preceding Red, Blue, Green and Yellow color terms.

A final note of caution: only receptor types do not guarantee that the qualia experienced would change. In an experiment with mice, in which the mice were endowed with human pigments, they could not still learn to distinguish Red, as presumably the latter opponent-processing wiring, required for that qualia generation was not present/ couldn’t develop.

Thats all for now. Hope you found this post Eye opening!! Do let me know via comments of any incompatible/recent evidences and arguments.

Incongruence perception and linguistic specificity: a case for a non-verbal stroop test

In a follow up to my last post on color memory and how it affects actual color perception, I would like to highlight a classical psychological study by Bruner and Postaman, that showed that even for non-natural artifacts like suits in a playing card deck, our expectation of the normal color or shape of a suit, affects our perception of a stimuli that is incongruent to our expectations.

In a nutshell, in this study incongruent stimuli like a red spade card or a black heart card was presented for brief durations and the subjects asked to identify the stimuli completely – the form or shape (heart/spade/club/diamond), the color (red/black) and the number( 1..10…face cards were not used) of the stimuli.

The trial used both congruent ( for eg a red heart, a black club) as well as incongruent stimuli (a black heart, a red spade).

To me this appears to be a form of stroop task , in which, if one assumes that form is a more salient stimulus than color, then a presentation of a spade figure would automatically activate the black color perception and the prepotent color naming response would be black, despite the fact that the spade was presented in red color. This prepotent ‘black’ verbal response would, as per standard stroop effect explanations, be inhibited for the successful ‘red’ verbal response to happen. I am making an analogy here that the form of a suit is equivalent to the linguistic color-term and that this triggers a prepotent response.

In these lights, the results of the experiment do seem to suggest a stroop effect in this playing-deck task, with subjects taking more trials to recognize incongruent stimuli as compared to congruent stimuli.

Perhaps the most central finding is that the recognition threshold for the incongruous playing cards (whose with suit and color reversed) is significantly higher than the threshold for normal cards. While normal cards on the average were recognized correctly — here defined as a correct response followed by a second correct response — at 28 milliseconds, the incongruous cards required 114 milliseconds. The difference, representing a fourfold increase in threshold, is highly significant statistically, t being 3.76 (confidence level < .01).

Further interesting is the fact that this incongruence threshold decreases if one or more incongruent trials precede the incongruent trial in question; or increases if the preceding trials are with normal cards. This is inline with current theories of stroop effect as involving both memory and attention, whereby the active maintenance of the goal (ignore form and focus on color while naming color) affects performance on all trials and also affects the errors , while the attentional mechanism to resolve incongruence affects only reaction times (and leads to RT interference).

As in the playing card study, no reaction time measures were taken, but only the threshold reached to correctly recognize the stimuli were used, so we don’t have any RT measures, but a big threshold is indicative of and roughly equal to an error on a trial. The higher thresholds on incongruent trial means that the errors on incongruent trial were more than on congruent trials. The increase in threshold , when normal card precede and a decrease when incongruent cards precede is analogous to the high-congruency and low-congruency trials described in Kane and Engel study and analyzed in my previous posts as well as in a Developing Intelligence post. It is intuitive to note that when incongruent trials precede, then the goal (ignore form and focus on color while naming color) becomes more salient; when normal cards precede one may have RT facilitation and the (implicit) goal to ignore color may become less salient.

Experience with an incongruity is effective in so far as it modifies the set of the subject to prepare him for incongruity. To take an example, the threshold recognition time for incongruous cards presented before the subject has had anything else in the tachistoscope — normal or incongruous — is 360 milliseconds. If he has had experience in the recognition of one or more normal cards before being presented an incongruous stimulus, the threshold rises slightly but insignificantly to 420 milliseconds. Prior experience with normal cards does not lead to better recognition performance with incongruous cards (see attached Table ). If, however, an observer has had to recognize one incongruous card, the threshold for the next trick card he is presented drops to 230 milliseconds. And if, finally, the incongruous card comes after experience with two or three previously exposed trick cards, threshold drops still further to 84 milliseconds.

Thus clearly the goal maintenance part of stroop effect is clearly in play in the playing-card task and affects the threshold for correct recognition.

The second part of explanation of stroop task is usually based on directed inhibition and an attentional process that inhibits the perpotent response. This effect comes into play only on incongruent trials. An alternate explanation is that their is increased competition of competing representations on incongruent trials and instead of any top-down directed inhibition, inline with the goal/expectation, their is only localized inhibition. The dissociation of a top-down goal maintenance mechanism ad another attentional selection mechanism seems to be more inline with the new model, wherein inhibition is local and not top-directed.

While RT measures are not available it is intersecting to take a look at some of the qualitative data that supports a local inhibition and attentional mechanism involved in reacting to incongruent stimuli. The authors present evidence that the normal course of responses that are generated by the subjects for (incongruent) stimuli is dominance, compromise, disruption and finally recognition.

Generally speaking, there appear to be four kinds of reaction to rapidly presented incongruities. The first of these we have called the dominance reaction. It consists, essentially, of a “perceptual denial” of the incongruous elements in the stimulus pattern. Faced with a red six of spades, for example, a subject may report with considerable assurance, “the six of spades” or the “six of hearts,” depending upon whether he is color or form bound (vide infra). In the one case the form dominates and the color is assimilated to it; in the other the stimulus color dominates and form is assimilated to it. In both instances the perceptual resultant conforms with past expectations about the “normal” nature of playing cards.

A second technique of dealing with incongruous stimuli we have called compromise. In the language of Egon Brunswik , it is the perception of a Zwischengegenstand or compromise object which composes the potential conflict between two or more perceptual intentions. Three examples of color compromise: (a) the red six of spades is reported as either the purple six of hearts or the purple six of spades; (b) the black four of hearts is reported as a “grayish” four of spades; (c) the red six of clubs is seen as “the six of clubs illuminated by red light.”

A third reaction may be called disruption. A subject fails to achieve a perceptual organization at the level of coherence normally attained by him at a given exposure level. Disruption usually follows upon a period in which the subject has failed to resolve the stimulus in terms of his available perceptual expectations. He has failed to confirm any of his repertory of expectancies. Its expression tends to be somewhat bizarre: “I don’t know what the hell it is now, not even for sure whether it’s a playing card,” said one frustrated subject after an exposure well above his normal threshold.

Finally, there is recognition of incongruity, the fourth, and viewed from the experimenter’s chair, most successful reaction. It too is marked by some interesting psychological by-products, of which more in the proper place.

This sequence points towards a local inhibition mechanism in which either one of the responses is selected and dominates the other; or both the responses mix and yield to give a mixed percept —this is why a gray banana may appear yellowish—or why a banana matched to gray background by subjects may actually be made bluish—as that of a blackish red perception of suit color; or in some cases there may be frustration when the incongruent stimuli cannot be adequately reconciled with expectations- leading to disruption- in the classical stroop task this may explain the skew in RT for some incongruent trials—-some take a lot of time as maybe one has just suffered from disruption—; and finally one may respond correctly but only after a reasonable delay. This sequence is difficult to explain in terms of top-down expectation model and directed inhibition.

Finally, although we have been discussing the playing card task in terms of stroop effect, one obvious difference is striking. In the playing cards and t e pink-banana experiments the colors and forms or objects are tightly coupled- we have normally only seen a yellow banana or a red heart suit. This is not so for the printed grapheme and linguistic color terms- we have viewed then in all colors , mostly in black/gray- but the string hue association that we still have with those colors is on a supposedly higher layer of abstraction.

Thus, when an incongruent stimuli like a red heart is presented , then any of the features of the object may take prominence and induce incongruence in the other feature. For eg, we may give more salience to form and identity it as a black spade; alternately we may identify the object using color and perceive incongruence in shape- thus we may identify it as a red spade. Interestingly, both kind of errors were observed in the Bruner study. Till date, one hast not really focussed on the reverse stroop test- whereby one asks people to name the color word and ignore the actual color- this seems to be an easy task as the linguistic grapheme are not tied to any color in particular- the only exception being black hue which might be reasonably said to be associated with all grapheme (it is the most popular ink). Consistent with this, in this reverse stroop test, sometimes subjects may respond ‘black’ when watching a ‘red’ linguistic term in black ink-color. This effect would be for ‘black’ word response and black ink-color only and for no other ink color. Also, the response time for ‘black’ response may be facilitated when the ink-color is black (and the linguistic term is also ‘black’) compared to other ink-colors and other color-terms. No one has conducted such an experiment, but one can experiment and see if there is a small stroop effect involved here in the reverse direction too.

Also, another important question of prime concern is whether the stroop interference in both cases, the normal stroop test, and the playing card test, is due to a similar underlying mechanism, whereby due to past sensory (in case of playing cards) or semantic associations (in case of linguistic color terms) the color terms or forms (bananas/ suits) get associated with a hue and seeing that stimulus feature automatically activates a sensory or semantic activation of the corresponding hue. This prepotent response then competes with the response that is triggered by the actual hue of the presented stimulus and this leads to local inhibition and selection leading to stroop interference effects.

If the results of the non-verbal stroop test, comprising of natural or man-made objects, with strong color associations associated with them, results in similar results as observed in the classical stroop test, then this may be a strong argument for domain-general associationist/ connectionist models of language semantics and imply that linguistic specificity may be over hyped and at least the semantics part of language acquisition, is mostly a domain general process. On the other hand, dissimilar results on non-verbal stroop tests form the normal stroop test, may indicate that the binding of features in objects during perception; and the binding of abstract meaning to linguistic words in a language have different underlying mechanisms and their is much room for linguistic specificity. Otherwise, it is apparent that the binding of abstract meaning to terms is different a problem from that of binding of different visual features to represent and perceive an object. One may use methods and results from one field and apply them in the other.

To me this seems extremely interesting and promising. The evidence that stroop test is due to two processes – one attentional and the other goal maintenance/ memory mediated – and its replication in a non-verbal stroop tests, would essentially help us a lot by focusing research on common cognitive mechanisms underlying working memory – one dependent on memory of past associations and their active maintenance- whether verbal/abstract or visual/sensory- and the other dependent on a real-time resolution of incongruity/ambiguity by focusing attention on one response to the exclusion of the other. This may well correspond to the Gc and Gf measures of intelligence. One reflecting how good we are at handling and using existing knowledge; the other how good we are able to take into account new information and respond to novel situations. One may even extend this to the two dissociated memory mechanisms that have been observed in parahippocampal regions- one used when encountering familiar situations/stimuli and the other when encountering novel stimuli. One essentially a process of assimilation as per existing schema/ conceptual metaphors; the other a process of accommodation, involving perhaps, an appreciation/formation of novel metaphors and constructs.

Enough theorizing and speculations for now. Maybe I should act on this and make an online non-verbal stroop test instead to test my theories!

Endgame: Another interesting twist to the playing cards experiment could be in terms of motivated perception. Mixing Memory discusses another classical study by Bruner in this regard. Suppose that we manipulate motivations of people so that they are either expecting to see a heart or a red color as the next stimuli- because only this desired stimuli would yield them a desired outcome, say, orange juice; then in this case when presented with an incongruent stimuli – a red spade- would we be able to differentially manipulate the resolution of incongruence; that is those motivated to see red would report seeing a ‘red spade’ and those motivated to see a heart would report a ‘black heart’ . Or is the effect modality specific with effects on color more salient than on form. Is it easier to see a different color than it is to see a different form? And is this related to the modality specific Sham’s visual illusion that has asymmetry in the sense that two beeps, one flash leads to perception of two flashes easily but not vice versa.

color memory, stroop test and models of working memory

BPS research digest as well as Mixing Memory have both commented on a recent study that showed that our memory of colors associated with a particular object, affects our actual color perception.

As per this study, as we have normally only seen a yellow banana and that color association is quite strong in our minds, hence when we perceive a ‘different’ colored banana, we are bound to see it more yellowish than is the actual hue in which the different color banana is presented.

Basically, they used 2 extremely good experiments that show that when viewing a banana (which is generally yellow), the yellow color perception is automatically activated in our brains: thus a gray matched banana would appear yellowish; while the task that requires matching a pink banana to a gray background would result in a bluish-gray banana, as blue is the opponent color for yellow and blue is added to the background gray to compensate for the memory-activated yellow color perception.

It is interesting to draw parallels here with the stroop test. In this test, color words like ‘red’, ‘yellow’ etc also appear to invoke automatic activation of the corresponding color in the brain and thus interferes with the correct naming of the actual color in which the color word is presented. Developing Intelligence has a very interesting and promising post, in which he explores the current research and computation models, that seem to suggest that the mechanism underlying stroop interference is not directed inhibition of prepotent responses, but lateral excitation among color and linguistic perception modules, with color perception area of the brain being always activated when a color linguistic term is presented and in the incongruent trials more activation seen in this to-be-ignored module as the conflicting activations of color – one due to the actual color of the word and the other due to the color perception activated by the linguistic color word (‘red’ ) both competing against each other lead to more activation. This is in contrast to the view that the more activation is due to directed inhibition . The new explanation advocated seems also to fit with the brain anatomy, with there being only local inhibition processes and is reconcilable with a lack of long range inhibiting pathways in the neocortex.

Thus to me, it seems more and more possible that stroop effect may be due to actual ‘yellowish’ hue perception in brain on watching the linguistic term ‘yellow’. I know that the two examples are not the same– a yellow banana actually has yellow color and thus its memory may affect the perception of a strange colored banana; but maybe the ‘yellow’ linguistic term is also somehow related in our mind very strongly with actual yellow hue perception and maybe we are all synaesthetic to the extent that all of us literally see the linguistic color terms in color rather than in black-and-white (or whatever the text color).

Language and Cognition: a developmental framework revealed by color term analysis

There have been various claims about the ability of language to shape thought and perception, and one of the oft-cited phenomenon supporting this sapir-whorf hypothesis is the evolution of color terms in languages and how the lack of a color term in a language may influence the ability of that language user to make categorical distinctions between colors or to perceive the differing colors.

The basic color terms were originally proposed by Berlin and Kay (1969) in their seminal study ‘Basic Color Terms, their Universality and Evolution’ in which they proposed that different languages (written/ oral) have evolved to differing levels and that a culture would start with only two color terms, equivalent to black and white or dark and light, before adding subsequent colors closely in the order of red; green and yellow; blue; brown; and orange, pink, purple, and gray. Based on this they proposed a grouping of the ninety-eight languages studied into seven stages of an evolutionary sequence running from primitive languages with words only for WHITE and BLACK to more advanced languages with words for the whole range of colors.

  1. STAGE I : WHITE BLACK: Nine languages:7 New Guinea 1 Congo 1 South India
  2. STAGE II: WHITE BLACK RED: Twenty-one languages:2 Amerindian 16 African 1 Pacific 1 Australian Aboriginal 1 South India
    1. STAGE IIIa: WHITE BLACK RED GREEN: Eight languages:6 African 1 Philippine 1 New Guinea
    1. STAGE IlIb: WHITE BLACK RED YELLOW:Nine languages:2 Australian Aboriginal 1 Philippine 3 Polynesian 1 Greek (Homeric) 2 African
  4. STAGE IV: WHITE BLACK RED GREEN YELLOW:Eighteen languages:12 Amerindian 1 Sumatra 4 African 1 Eskimo 380
  5. STAGE V: WHITE BLACK RED GREEN YELLOW BLUE:Eight languages:5 African 1 Chinese 1 Philippine 1 South India
  6. STAGE VI : WHITE BLACK RED GREEN YELLOW BLUE BROWN:Five languages:2 African 1 Sumatra 1 South India 1 Amerindian
  7. STAGE VII: COMPLETE ARRAY OF COLORS :Twenty languages: 1 Arabic 2 Malayan 6 European 1 Chinese 1 Indian 2 African 1 Hebrew 1 Japanese 1 Korean 2 South East Asian 1 Amerindian 1 Philippine

This schema of classification has been revisited in light of recent research, mostly the World Color Survey, and Kay and Maffi (1999), in Color Appearance and the Emergence and Evolution of Basic Color Lexicons, discuss the results to come up with a five stage developmental model of languages based on black, white, red, yellow, green, blue terms only and leave from the analysis other basic terms like brown, orange, purple and pink.

Their stages of languages are essentially the same as that of Berlin and Kay with stage IIIA (White, black, red, green) being more conman than stage IIIB (White, black, red, yellow) in the stage III languages.

Cognitive Daily ran a recent commentary on the World Color Survey , and as per the analysis presented there, it is apparent that the 41 languages covered there belonged to the stage V languages and the rest 69 languages belonged to stage IV languages (and in these languages as no separate word for Blue is present, hence the blue-green color is perceived as same and also labeled as Grue i.e. Blue and green are confused. The results that across cultures, people, if they have a term for a particular color in their language, then they do agree to the actual color hue that the color term corresponds to, across cultures, is a strong argument in favor of universality of color categories. thus, the blue of one language is the same as the blue of the other language and this is most probably due to the underlying physiology. See my blog posts related to color perception in humans in this regard.

Conversely, the fact that those languages that had no term for blue (but only had a common term Grue for blue and green), also found it difficult to distinguish between blue and green hues, suggests that having a term for a color does influence the way in which we categorize the colors and possibly also the way we perceive them. The latter (influence on perception) may be a more controversial claim, but the fact that color terms affect cognition (categorization) is relatively uncontroversial.

It is instructive to pause here, and note some facts from color vision physiology. The rods give us an ability to see even in dark and may have been the first to evolve, giving us the concepts of black and white. The cones may have evolved later to give a sense of colors. The opponent process utilizing Red cones and green cones gives rise to the perception of colors Red and Green. It is plausible that first the Red cones evolved (in evolutionary time-frame), giving a Red signal and thus a Red qualia/ Red color term. Later came the green cones to give a green signal and a green qualia/ green color term. The R+G opponent process was born later and refined the perception of Red and Green. It is also plausible that the brain started combining Red and Green signal (R+G) to perceive Yellow. Thus , a perception of Red, Green and Yellow would be generated by the brain, based on the two Red and Green cones only. The R+G =Y signal does exist in the brain and is one of the signals involved in the opponent processes of Blue-Yellow perception. The Blue cones apparently came the last and using the signal from blue cone and the Y=R+G signal, the opponent process of Blue-Yellow perception enabled, the perception of Blue qualia too and a corresponding color term for Blue too. Further, it is instructive to note that brown color (the stage V to stage VI transition of languages based on color terms) is perceived in the brain by a complex process involving signals from both R-G and B-Y opponent processes (specifically mixing of Red and Yellow at a point in space to give orange) and comparing and contrasting this information with the intensity (Black-white achromatic signal) of the surrounding region. This, leap from opponent-processes to a perception based on contrast with surrounding areas, marks a significant leap ( as is common in all developmental stage VI transformations) in perceptual mechanism employed and correspondingly the terms for Brown are more rare and difficult to be claimed as being universal in all languages and must have evolved later. The stage VII and VIII perceptual processes may determine how we perceive purple, pink, orange and gray but a more physiological analysis of perceptual mechanism involve would have to wait for another day, and by another more informed vision researcher. Here it would suffice to note that there are sound physiological reasons for why the color terms may have evolved in the way did over historical and evolutionary time scales and how some modern languages may still not be having terms for some colors the ability to distinguish which might have evolved recently and based on the different perceptual processes involved may not be the same in all cultures.

Before speculating further, it would serve us well to get acquainted with the latest consensus regarding the color terms and what they inform us regarding language and cognition. Kay and Regeir (2005) in their article Language, thought, and color: Recent developments, TICS , aptly summarize the state of the art view that involves an interactionist view where both Nature and Nurture, Universalism and Relativism have their place and are involved. As per them,

The language-and-thought debate in the color domain has been framed by two questions:
1. Is color naming across languages largely a matter of arbitrary linguistic convention?
2. Do cross-language differences in color naming cause corresponding differences in color cognition?

In the standard rhetoric of the debate, a ‘relativist’ argues that both answers are Yes, and a ‘universalist’ that both are No. However, a number of recent studies, when viewed in aggregate, undermine these traditional stances. These studies suggest instead that there are universal tendencies in color naming (i.e. No to question 1) but that naming differences across languages do cause differences in color cognition (i.e. Yes to question 2).

We have already seen how the concept of Focal colors (as outlined by Kay) is valid and seems to constitute a universal cognitive basis for both color language and color memory. Further, we have seen some neuro-physiological support for the emergence of focal colors red, yellow, green, blue and brown. Jameson and D’Andrade have argued that the universal focal colors are
salience maxima in color space and that universals of color naming flow from a process that partitions color space in a way that maximizes information. A recent study by Griffin LD (2006), The Basic Colour Categories are optimal for classification. J Roy Soc: Interface 3(6):71-85, seems to support this hypothesis and posits that the eleven basic color categories identified by Kay are optimal and useful in computer machine vision too. All these evidences are compatible with each other and suggest that the basic properties and number of color categories, compatible with optimal color space partitioning, have led to the emergence of corresponding neuro-physiological/ perceptual apparatus in humans to detect these categories, and has thus led to that many number of color terms to evolve in the degree of complexity of these mechanisms/ incremental advantage they provide in categorization.

On the relativistic side it is claimed, that the cognitive variables like privileged memory, similarity judgments, or paired associates learning for focal colors are well predicted by the boundaries of each language’s color categories: a form of categorical perception of color. Since these boundaries vary across languages, speakers of different languages apprehend color differently. Moreover, these linguistic differences seem to actually cause, rather than merely correlate with, cognitive differences.The argument is further that color terms are arbitrary and the color terms determine the perception of colors absolutely. Roberson, Davidoff et al, in Color Categories are not universal: New evidence from Traditional and Western cultures , argue that the evidence supporting focal colors and the concept of universal categorical perception arising from them, . viz privileged memory for them or paired associate learning for the proposed universal colors, is rendered incorrect, when the effect of verbalization (or use of linguistic tokens) is taken into account. As per them (emphasis mine) :

In native English speakers a series of experiments found that verbal interference selectively removed the defining features of Categorical Perception. Under verbal interference, there was no longer the greater accuracy normally observed for cross-category judgments compared to within-category judgments. It thus appears that while both visual and verbal codes may be employed in the recognition memory of colors, subjects only make use of verbal coding when demonstrating Categorical Perception (Roberson & Davidoff, 2000). In a brain-damaged patient suffering from a naming disorder, the loss of labels radically impaired his ability to categorize colors

Participants from a traditional hunter-gatherer culture, whose language contains five basic color terms (under the definition of Kay Berlin & Merrifield, 1991), showed no tendency towards a cognitive organization of color resembling that of English speakers. They did not find best examples of English color categories easier to learn or remember than poor examples and, in a further set of experiments, evidence of Categorical Perception was found in both languages, but only at their own linguistic category boundaries.

Although the authors draw extreme conclusions from their findings, but Kay moderates the viewpoint and concludes: (emphasis mine)

It has been widely assumed that language is the cause of color categorical perception. This is suggested since – as we have seen – named category boundaries vary across languages, and categorical perception varies with them. However, Franklin and Davies have found startling evidence of categorical perception at some of these same boundaries in pre-linguistic infants and toddlers of several languages. Thus, some categorical color distinctions apparently exist prior to language, and may then be reinforced, modulated, or eliminated by learning a particular language.

This finally brings us to the post by Developing Intelligence regarding labels as an accelerator of ontological development. In this, though in the beginning itself, Chris dismisses the strong form of Sapir-Whorf hypothesis (esp. in relation to colors) , he presnts a study that leads to a reasonable conclusion that language can accelerate the process of sortal/kind discrimination, such that a skill normally only demonstrated by 12-month-olds was in this case demonstrated by 9-month-olds with the proper linguistic input. Here, one is not arguing that the sortal/kind discrimination would not have been possible in the absence of linguistic inputs- one is merely claiming that the sortal/kind discrimination is facilitated by language and happens early in the developmental cycle based on linguistic labels. And definitely not having labels leads to a different cognitive/ perceptual experience in the infants as compared to those infants who use labels and can make the sortal/kind discrimination.

Form the above, it may be inferred, that though universal focal colors and color categories do exist (based on underlying neurophysiology or spectral properties of the visible-to-humans world), they may be available to consciousness at different stages of an infant’s (or a culture or a language’s ) development, and having labels or color terms for the categories may facilitate an early maturation of the color categorizations faculty. Depending on where a culture, or language is on its developmental path, lack of proper color terms may limit their ability to perceive colors as belonging to different categories for which they don’t have a label.

Interestingly, in the Davidoff study, a brain damaged patient suffering from an inability to label things, was impaired in categorizing colors.

Though the exact mechanism by which labels or color terms may work is still elusive with multiple competing hypothesis (viz., labels facilitate sortal/kind distinctions by aiding a domain-general, non-linguistic process, such as memory; or that labels increase the salience of perceptual feature differences between object) , yet it is clear that labels are instrumental and play a definitive role in the ontological development of the child.

One may take a strong line and argue, that in the absence of color terms or labels, one would not be able to have a full cognitive color categorization or sortal/kind discrimination experience, but even if one does not subscribe to the extreme view, it seems plausible that different developmental levels of languages identified by the linguistic color terms in the languages correspond to different levels of cognitive experiences that are more readily available in the corresponding culture.

Thus, while language does affect thought and vice versa, both may be constrained by the developmental stage at which a culture is. The cognitive experience and the cognitive developmental stage from which that experience results would correspond to the stage of development of that language and vice versa. Thus, some cultures, by not using a language that is fully evolved/ developed, may not be experiencing the full range of cognition and emotion that is humanly possible. Conversely, based on the linguistic devices utilized by a culture, their cognitive experiences may differ from another culture that utilizes another incompatible set of linguistic devices.

4 (or more) cone vision : Tetrachromancy in Human Females vis-a-vis birds.

Cognitive Daily has a posting related to Human Female Tetrachromancy that refers to some old article on the subject. An interesting and must-read article on the web by Ryan Sutherland in detail explains the rationale as to how four cone receptors may arise due to X-chromosome related procedures. Also It is instructive to note here that if the additional cone that has shifted from Red(Long cone) towards green (the red-shifted) or from Green(The Medium cone) towards the red (green-shifted) has shifted to a considerable extent, then it may assume the role of phantom Yellow and thus lead to some radical re-wiring of the optical system in brain whereby Red(L) and Green(M) do not have to combine to give Yellow that can be considered along with output from Blue (S) cone to give rise to Blue-Yellow opponent process. In this case a simple consideration of output from Blue (S) and Shifted-red/shifted-green (Yellow) would give rise to the Blue-Yellow opponent process. I don’t think such radical shifts are possible or would lead to such radical rewiring, but post-mortem analysis of Tetrachromat women may shed some light. Even if such a shift does occur , it may not lead to any change in the number of hues that could be distinguished, though the colors may appear more colorful and saturated.

Of further interest is the shift from red away from green side towards the ultraviolet. This shift may indeed give rise to ability to perceive Hues differently and to see some infra-red not normally visible to trichromatic humans.

coming back to different dimensions of vision, it is interesting to note that dogs (like most other mammals) have dichromatic vision and utilize the blue-yellow opponent process.

Cats utilize the same trichromatic color mechanisms as humans, but their total perceivable color range is sort of ‘contracted’ i.e. they don’t see some of the human Red and some of the human Blue.

Bees have also trichromatic vision, but apparently their cones lie in UV, Blue and green. Thus they are unable to see human red but able to see beyond Violet (the UV). Maybe the genes coding blue lie on X chromosomes for Bees (instead of the red-green genes of humans and yellow of dogs) and its breakup into two (just like the breakup of mammalian yellow is hypothesized to have resulted in human Red-Green) has resulted in some infra-blue and Ultra-violet cones in the bees.

Further most birds (and some fish and turtles) have tetrachromatic vision with 4 cones : one in UV, one in Blue, one in green/yellow and the other in red. Thus, if humans do want to have a tetrachromatic vision a better way forward would be the split of blue cone in infra-blue and UV cones. That would really give us the capacity to for example view the human-white feathers of some birds as actually ‘colored’/shining’ in UV (as they reflect UV). For more details on comparative chromatic vision information please visit this excellent page on comparative chromatic vision. Also some evolutionary rationale for chromatic vision (and UV in particular) can be found here.

Endgame: Would introduction of a UV cone lead to radical changes in perception of the blue (blue-indigo-violet) end of the spectrum, just like splitting of Yellow into Red and Green led to totally new colors on the original Yellow part of the spectrum?