The naming of colours: investigating a psychological curiosity using AI Em Denby & John Gammack School of Information Technology Murdoch University Perth, Western Australia Abstract This paper chronicles a case study investigation into colour naming decisions, a phenomenon at the boundary of language and perception. Colours in the region between blue and green are found difficult to name reliably, and it is suggested that decisions may be based primarily on intentional, context dependent language processes, rather than on simply perceptual mechanisms. Various models and codes for colour naming exist which make Merent assumptions based on theories of colour vision. A challenge for AI is to provide normative models to better understand aspects of colour vision, by using techniques such as fuzzy logic and artlficial neural networks. Th~s is based on the assumption that classlfications of colour stimuli can be established and corroborated with case data, thus enabling clarifkation of psychological models. This paper outlines a preliminary research proposition, and some initial findings. 1. Introduction The perception and naming of colours is an area at the heart of understanding the cognitive processes involved in language and perception. The main focus of this paper is on uncovering some important elements posed by theoretical models of colour perception and colour naming, with the aim of clarifying research questions which, in the authors estimation, can be further investigated by current AI technologies. The payoffs of this research are expected to impact on how human-machine interfaces are built. For the design of multimedia interfaces to information systems, it is essential to understand how colours are perceived and interpreted, and conversely, how they might be misconstrued (by both user and designer) in systems where colour is expected to carry a connotative, or attention grabbing information content. This investigation will raise an awareness of the assumptions at stake. It is relevant generally, to distinguish the physical classifications available to machine classifiers of coloured stimuli, as for example, Kuo and Hsu (1996), from those which are artefacts of human nervous and intentional processing, often referred to as, perceptual colour order systems. A number of theories of colour vision exist which go back centuries, but renewed interest has given impetus to interesting computational applications (Siminoff, 1997; 1998; Fagin, 1999). It is now widely accepted that the three colour mechanisms or RGB photoreceptors, and the opponent-colour phenomenon (red- green and blue-yellow exclusivity) actually explain different neurophysiological levels of colour vision (Skottun, 1998). Deficiencies such as achromatopia, and red-green (or other) "colour-blindnesses" may occur at either level as clinical and genetic studies reveal. The established test stimuli for detecting these deficiencies can also be approached using computer vision techniques to establish classification benchmarks for comparing and testing soft computing approaches (Chen & Hsu, 1995). The act of colour perception is not in-itself purely receptive, it involves a combination of external factors and mediating processes, influenced by phenomena of colour constancy, luminescence, and other cues from the contexts of active perception. Physical displays interact with conceptual processing to determine the psychological quality of the experienced colour in a perceiving subject. At another level, active perception is interfaced with language and logic for communication purposes. The vocabulary available in natural language is not only dependent on the subjective vocabulary of the perceiver, but on the terms available and understood by conventions. Since infinitely more variations exist within visually differentiable colours than in natural language, colour perceptions require refined 0-7803-5871-6/99/$10.00 0 1999 IEEE 964
Transcript
The naming of colours: investigating a psychological curiosity using AI
E m Denby & John Gammack School of Information Technology
Murdoch University Perth, Western Australia
Abstract
This paper chronicles a case study investigation into colour naming decisions, a phenomenon at the boundary of language and perception. Colours in the region between blue and green are found difficult to name reliably, and it is suggested that decisions may be based primarily on intentional, context dependent language processes, rather than on simply perceptual mechanisms. Various models and codes for colour naming exist which make Merent assumptions based on theories of colour vision. A challenge for AI is to provide normative models to better understand aspects of colour vision, by using techniques such as fuzzy logic and artlficial neural networks. Th~s is based on the assumption that classlfications of colour stimuli can be established and corroborated with case data, thus enabling clarifkation of psychological models. This paper outlines a preliminary research proposition, and some initial findings.
1. Introduction
The perception and naming of colours is an area at the heart of understanding the cognitive processes involved in language and perception. The main focus of this paper is on uncovering some important elements posed by theoretical models of colour perception and colour naming, with the aim of clarifying research questions which, in the authors estimation, can be further investigated by current AI technologies. The payoffs of this research are expected to impact on how human-machine interfaces are built. For the design of multimedia interfaces to information systems, it is essential to understand how colours are perceived and interpreted, and conversely, how they might be misconstrued (by both user and designer) in systems where colour is expected to carry a connotative, or attention grabbing information content. This investigation will raise an awareness of the assumptions at stake. It is relevant generally,
to distinguish the physical classifications available to machine classifiers of coloured stimuli, as for example, Kuo and Hsu (1996), from those which are artefacts of human nervous and intentional processing, often referred to as, perceptual colour order systems.
A number of theories of colour vision exist which go back centuries, but renewed interest has given impetus to interesting computational applications (Siminoff, 1997; 1998; Fagin, 1999). It is now widely accepted that the three colour mechanisms or RGB photoreceptors, and the opponent-colour phenomenon (red- green and blue-yellow exclusivity) actually explain different neurophysiological levels of colour vision (Skottun, 1998). Deficiencies such as achromatopia, and red-green (or other) "colour-blindnesses" may occur at either level as clinical and genetic studies reveal. The established test stimuli for detecting these deficiencies can also be approached using computer vision techniques to establish classification benchmarks for comparing and testing soft computing approaches (Chen & Hsu, 1995).
The act of colour perception is not in-itself purely receptive, it involves a combination of external factors and mediating processes, influenced by phenomena of colour constancy, luminescence, and other cues from the contexts of active perception. Physical displays interact with conceptual processing to determine the psychological quality of the experienced colour in a perceiving subject. At another level, active perception is interfaced with language and logic for communication purposes. The vocabulary available in natural language is not only dependent on the subjective vocabulary of the perceiver, but on the terms available and understood by conventions.
Since infinitely more variations exist within visually differentiable colours than in natural language, colour perceptions require refined
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colour models to utilise the information colour distinctions can potentially encode.
The evolution of colour terms has been tracked by Berlin and Kay (1969) and subsequently investigated by others (Rosch and Mervis, 1975). Various schemes for objectively coding colours exist, as we describe below. Generally, at archetypal points on the spectrum, canonical hues are labelled (red, green, blue.. . ) which correspond to the familiar terms of everyday language, allowing social interchange independent of the subjectively experienced colour for an individual. These wavelengths of light correspond to particular identifiable frequencies and intervals, which have presumably been found socially useful to distinguish. But potentially any scale points can be singled out to be named, and naturally numerous intermediate hues, or colour combinations exist. Prototype theory was developed in response to this, and the concepts of graded category membership have been initially developed by Rosch and Mervis (1 975). The psychology of conceptual categories has been significantly extended by Murphy and Medin (1985) and Barsalou (1987). These non-classical models emphasise individual "theories" or subjective world views and dynamic constructions involving contextual factors.
1.1 Colour order systems
For reasons of space these are summarised in Appendix 1 (1-3)- Another account is given in Gammack and Denby (1 999), which relates this work to wider theories of consciousness.
2. Introduction to Case Study
In this section we describe a phenomenon which may challenge purely physiological accounts of colour perception, and which has implications for the use of language conceptions in colour identification. It also impacts on any future lingua fruncu for communication in picture coded information systems, not limited by cultural or purely personal terms of reference, nor by purely rendered colours, but which allow for interactions with intentional processes. After we describe our preliminary findings from a case study, we discuss how developing a practical model using AI techniques can be useful in responding to the concerns reported in this study, and which can hrther be used to
test some of the assumptions of the NCS model.
It is necessary first to establish a region of identifiable colours in which naming decisions are affected by both conceptual and perceptual information to allow empirical studies to be conducted. Firstly we test whether an individual with apparently defective colour naming is able to give accurate NCS-type descriptions of hues, and indicate some of the related cognitive phenomena.
3. A case study - Preliminary Findings
A female individual (designated here as subject P.) presented to the second author claiming to be unable to distinguish between blue and green on the same basis as other people. As blue colour deficiency is very rare, and incidence is considerably less in females than in males due to genetic factors, this prima facie seemed worthy of hrther investigation. The female is in normal health, a mother of two, and otherwise psychologically normal. This reported condition had apparently "always" been there, without causing any significant problems, other than many instances of specific disagreements with others in which she accepted her judgement seemed to be the minority one.
Informal testing of P. ensued, using ready to hand materials. Firstly, book spines were shown, in no particular order, but as they naturally occurred on nearby bookshelves. The investigator selected these as being distinctively blue, distinctively green, or ambiguous in some particular way, and P. was asked to name them. Several classifications agree with the expectations of the investigator, and some terms named by P (such as aqua, turquoise and emerald), allowed a refined differentiation amongst the stimulus set, but there were enough disputed cases to suggest that the naming was subjectively rather than consensually based.
Although the experimenter made the provisional assumption that his own classification provided an objective standard for comparison, it was recognised that further study would be required, and the next set of materials showed the phenomenon's essence. Shown a framed map of Australia on the wall, P. reported that she perceived the sea as green,
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but ''knew'' it had to be blue. To the investigator, the hue involved was clearly blue. This demonstrated that the cognitive act of naming was affected both by theoretical (socialised) knowledge, as well as by immediate sensation, albeit moderated by localised perceptual factors, such as colour contrast and luminescence. The question arose as to whether the naming of specific colours would be consistent under more controlled conditions, and whether that corresponded to conventional norms'? If not, what might this imply? Since the materials at hand did not permit more than informal diagnosis, it was decided to follow up with some more specific qualitative testing at a later point.
4. Subsequent Tests
Further tests began using the twenty four items from the original Ishihara (1962) tests for colour blindness, which were presented on a computer monitor under normal daylight illumination. This (digitised) set was found at the web address under Ishihara in the reference list. Although some items proved more troublesome than others, taking evidently longer to identify, this test indicated no presence of red-green colour blindness, although it is recognised that such a test could not substitute filly for a proper optometric examination. In particular, items 7, 8 and 13 took longer, and item 8 took two attempts to identify correctly. Most other items were identified immediately, although P. commented that some were relatively more obvious than others, as may be expected from one with normal vision.
To test colour naming in a replicable manner requires objectively agreed standards, and for this two schemes were employed. Firstly, the RGB codes which ' provide the colour palette for computer monitors was used, and secondly the NCS colour scheme devised by Hird (1969; 1991). In addition to aiding replicability of the study in future situations, the former was seen as particularly useful in terms of its implications for web design and multimedia interface work, in which colour may be used for semantic purposes. The HCI literature (Wilson, 1997) has already established several guidelines for colour use in web design.
For our next study, the shareware applet WhatCoIor (Nakahara,1999) was useful in
providing the RGB coefficients of each component, along with indicative named categorisations. This allows specific identification of colours found discrepant from normative classifications to be detected. Using a digitised image of the Berlin and Kay (1969) colour set, a range of computer generated colours were presented to P. who provided the ratings in table 1. The target colour was seen in relation to a context of the squares of colour from the Berlin and Kay display since the application unavoidably provided contextual information on the neighbouring colours.
On a computer screen, a pixel from the image was presented in a large square, with the neighbouring area of the graphic displayed at one side. Figure 1 shows a sample screen display from a single trial for the subject. The RGB and name information at the top of the display was blotted out to the subject.
Figure 1: Screenshot from Whatcolor; P. saw bottom half only.
Under standardised luminescence conditions (namely elimination of ambient light), the subject was asked to name the colour displayed. This was tabulated along with the RGB coordinates, and the name provided by the application (which is arbitrary and software dependent). After each trial the subject was informed of the "correct" answer. The same RGB coordinates were identified in different contexts as different colours, and selected raw results are shown in table 1. Although context was not manipulated systematically, it was sampled from the palette as indicated by the Berlin and Kay co- ordinates in the table. Within each square, many pixels of the indicated colours existed, so peripheral context was kept relatively constant, albeit ambiguous. Subsequent matching with NCS colour chips by the first
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author took place, and these identifications are also shown in the table.
Some dramatic rather than marginal moves to change this context were made periodically, moving to a different square in the region. This was intended to ascertain whether the peripheral context affected naming, when the RGB values were identical to a previous trial.
This did seem to be the case, with the colour “light sea green” being named as blue in one context and green in another in successive trials. The subject learned this name after two trials, and named it accurately on two subsequent occasions. This was considered to potentially confound any quantitative results in this trial span, so was discontinued. Subsequent stimuli given in different contexts were also inconsistently named. Although P. seemed confident with most judgments, some took longer, and her decision in these cases were less confident. Sufficient observations had been collected however to suggest the following conclusions about the materials used and about the subject P..
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Findings
Identifiable colours in the blue-green area of the spectrum do not have agreed names to begin with. The Microsoft colour name given by the WhatColor applet disagreed with P., and with other named codes, which are platform dependent. There is evidence that names, at least in the short term, can be learned quickly (by this subject) irrespective of the perceived colour stimuli.
Naming of colours is apparently affected by peripheral perceptual context.
Hue chromaticness or different saturations of the colour, affect identification; for example, “Sea green ‘ I .
Light sea green (RGB: 5 1, 153, 153) is on the greedblue category borderline for this subject. (See Table 1, appendix 2, for other details)
Sharp classifications into green and blue are problematic, a fuzzy gradation among a repertoire of descriptive linguistic terms may be better as a dependent variable, in more thorough studies. Latency data is likely to suggest regions of ambiguity. Such factors are likely to vary with the
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subject’s vocabulary, and colour lexicon available in the culture.
Regions of ambiguity can be modelled using a fuzzy gradation or category membership functions. The domain of colour also gives a nice area in which to test a number of aspects of categorical decision making. The role of context identified in this case study suggests that further controlled situations should sharpen the distinction between perceptual and conceptual effects. Skottun (1998) has philosophically determined why ’’a color cannot be both red and green”, and this may be a starting point for addressing the above problem.
The findings of this study challenge studies in which simple naming is the dependent variable and categorical semantic labels are used to draw conclusions about perceptual processes.
6. Discussion
What can we gather from this brief and preliminary case study? Firstly, there seems to be discrepancy between this subject’s primary perceptual process and the elementary perceptions of consensual opinion of the NCS. Is this a physiological defect, and if so, what levels of the perceptual system account for this defect? Secondly, There also appears to be an ensuing, but overriding conceptual decision, using contextual information to settle the colour name. This substantiates the argument for intentionality.
One of the reasons for this investigation was to find out initially whether an individual with apparently defective colour naming is able to give accurate NCS-type nuances of hues. The closest was “wishy washy blue” and “grey-green” which may translate to a “whitish blue” and “greyish- green” respectively, as an NCS-type description. Nonetheless, these were ostensibly wrong from a normal vision perspective (See Table 1, appendix 2).
Nuances, whether in language or in art, carry specific semantic intentions, and some natural languages (for example, Hungarian) are more “nuanceable“ than others (Maruyama, 1993, p.92).
Other problems relate to the use of the RGB scheme. There are three co-ordinates which produce "millions of colourstt and which can ascertain the specific components of the ambiguities detected. The limitations of not being able to display all the nuance changes with each different RGB inputs, urges the need for appropriate hardware and software tools for such testing.
In view of the above discussion, to what extent has this study challenged the purely physiological accounts of colour perception? Having provided a description of how specific colours are perceived by an individual, in comparison to their coded descriptions, a further set of studies should provide objective data to facilitate a fuzzy gradation among a repertoire of descriptive linguistic terms. Latency data may be used to suggest regions of ambiguity to be further investigated.
A fuzzy neural network system utilizing part of the NCS as the accepted colour notation is proposed together with the ability to map cases in the form of rules (derived from artificial neural networks). The fact that the NCS may not provide the ideal intentional colour model, for example, the NCS suggests that the observer's intentionality plays a part in colour perception yet colour attributes remain constant for all normal viewers. This, however, may not prevent us from being able to use the model on a normative basis. What is important is that the NCS notions are consistent and less confusing than other available models, and can be used to give accurate ordering of colours in perceptual- logical terms.
To investigate why, and under what conditions individual and social distortion of the NCS colour solid occurs, will be another major concern for t h s paradigm, and will also augment previous psychological models.
A fuzzy neural network can be created to model three distinct levels of colour vision; (i) the photoreceptor or RGB level, (ii) the opponent-colour level, and (iii) the conceptual knowledge level (or intentional). The system can be designed to give explanations as to why an individual's colour responses differ from the norm, by pin-pointing at which level the problem occurs.
This research is at a very early stage, and the authors welcome further discussion on these matters.
7. Conclusion
Although definitive conclusions cannot be drawn from this brief case study, it is possible to hypothesise areas that may lead to significant research. Aspects of colour visions that have been identified fall into three levels: Photoreceptors, opponent- colour cells, and knowledge mappings.
It was argued that colours in the region between blue and green were found difficult to name reliably, and that decisions may have been done primarily on intentional, rather than on perceptual mechanisms. Despite its limitations, it is suggested that the NCS system provides consistent and less confusing terms that can be used to guide accurate predictions of colours in perceptual-logical terms.
In conclusion, it is offered that AI can provide normative models which account for these aspects of colour vision, using techniques such as fbzzy logic and artificial neural networks.
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8. References
Barsalou, L. W. (1987) The instability of graded structure: implications for the nature of' concepts. In: Neisser, U. (ed), Concepts and conceptual development: ecologicar' and intellectual factors in categorization, Cambridge: Cambridge University Press.
Berlin, B. & Kay, P. (1969) Basic Color terms. US: UCP Berkeley.
Benoit, E., Foulloy, L., Galichet, S . & Mauris, G (1994) Fuzzy sensor for the perception of colour, Proc. of 3rd IEEE Int. Con$ on Fuzzy Systems, Orlando, US, 94, 2008- 2013.
Chen, Y.S. & Hsu, Y.C. (1995) Computer Vision on a Color-Blindness Plate, Image and Vision Computing, 13, (6), 463-478.
Fagin, R. (1999) Combining Fuzzy Information from Multiple Systems, Journal of Computer and System Sciences, 58, 83-99.
Gammack J. & Denby, E. (1999) The True Hue Of Grue: Investigating A Psychological Curiosity. Dialogues in Psychology: A Journal of Theory and Metatheory, 9, 11- 25. Electronic Journal archived at: http://hubcap.clemson.edu/psych/Dialogue s/9 .O. html
Mrd, A. (1969). Qualitative Aspects of Colour Perception. In: Colour 69, Proc of the 1st AIC Congress, Stockholm, 1-26.
HArd, A. (1982). Man's Evaluation of Colour Combinations. AIC Conference on Colour Dynamics, Budapest.
H&d, A. & Hkd, T. (1991). NCS, A method for determining perceived colours of objects in environment observed under various external conditions. Proc. of AIC- Colour and Light '91, Sydney.
Ishihara, S . (1962) Tests for Colour Blindness. Tokyo: Kanehara Shuppan Co. Ltd. http://www.umds.ac.uk/phvsiolomddaveb/ braindav/colourblindness/cblind. htm [Accessed April 30* 19991
Connectionist Model for Color-Blindness Plate Recognition. Proc ICNN'96, Washington, June.
Maruyama, M. (1993) Mindscapes in Management. Aldershot: Dartmouth.
Murphy, GL & Medin DL (1985) The role of theories in conceptual coherence, Psychological Review, 92,289-3 16.
Rosch, E. &, Mervis, C. B. (1975) Family resemblances: studes in the internal structure of categories, Cognitive Psychoiogy, 7,573-605.
Siminoff, R. (1997) Color Perception of Aperture Colors Using a Computational Model of the Human Visual System, Real-Time Imaging, 3, 17-35.
Siminoff, R. (1998) A Computational Model of the Human Visual System for Color-coding: Results with Adaptation and Colored Surrounds, Real-Time Imaging, 4, 101- 112.
Skottun, B. (1998) A Note on the Possibility of Explaining why a Colour Cannot be Both Red and Green, Brain and Cognition, 38, 254-260.
Wilson, D. (1997) WebDesign. http://www. lava.net/--dewilson/web/color. html [Accessed April 30" 19991.
Website: Human-Computer Interaction (HCI) Resources. httix//www.hcibib.org/ [Accessed September 29' 19991.
Two widely used colour order systems are the Natural Colour System (NCS) and the Munsell Colour Order System. Of the two, the NCS is evidently the most elegant and easier to understand. The Munsell system uses a blend of concepts, both physical and perceptual, which tend to confuse the issue of perceptual ordering. For example, the use of the terms Hue, Value and Chroma (H V/C) to designate a colour, do not clearly describe colour attributes’ in the perceptual sense. Also, the numbering system for hues (in the circle) is totally arbitrary, not to mention the dubious use of “purple” as an elementary reference colour. Therefore, in this paper we will be mainly concerned with the NCS as the preferred model for psychologically related research.
Perceptual colour order systems
Perceptual colour order systems are not a recent development but most people are unaware of its nomothetic implications nor its use in every-day evaluation of colour interactions. The Natural Colour System (NCS) claims to be a perceptual colour order system that provides colour mappings for theoretically infinite number of colours, based on natural/normal human ability to recognise distinct colour sensations. It was founded on the work of Ewald Hering back in 1874, but its basic postulates2 that every conceivable colour can be described in terms of the six elementary colour sensations remain, viz. white, black, yellow, red, blue and green.
In the next section we will discuss in some detail the essence of the NCS model and the issues raised by the its main founder and proponent.
The Three Colour Principles
The NCS is based on the six elementary colours described above and claims that colour perception can be classified on the basis of the observer’s infenfionuliy3 as distinct from, purely physical determinants such as additive or subtractive colour models. H6rd (1 969) suggested three Principles for colour ordering based on intentionality:
1 ) perception of colour differences (hue differences)
2) perception of colour categories (colour names), and
3) analytical colour perception (colour quality)
Since these three notions form the basis for the NCS model we shall deal with each of these in turn.
Colour Differences
This is the ability to discriminate very small steps in hue change, a remarkable achievement of the human perceptual apparatus, probably very useful in adapting to the natural
’ For a brief outline of the Munsell colour order system see ht@://www.munsell.com
* The concepts were reformulated into a streamlined model by the Swedish Colour Centre Foundation headed by Professor Anders %d. The NCS was officially launch at the first Congress of the Association Zntemationale de la Couleur (AIC), Colour 69 (Hkd,1969).
elaborated by Pylyshyn (1 984) and others, is more applicable here. H M (1 969) calls it “intention”, but we feel Brentano’s concept of mental principles described as intentionality and further
environment. For example, colour pattern changes in animal skin may indicate different mood perceptions. The second, reduces colour name categories to a small set of about eleven, on which other variations of the same colour are based, for example, there are a lot of different browns. The third, refers to the different attributes or dimensions of colour, for which there are, arguably, only six, the degree of whiteness, blackness, yellowness, redness, blueness, and greenness.
Colour Categories
Colour category perception poses interesting questions since the mere fact that there are so many colour names may have a confounding effect on the way we procure research on colour perception. It produces a particular interesting problem or sets of problems, because it suggests more than the six archetypes described above. Colour name categories can however, be reduced to a small set of about eleven, viz. white, grey, black, yellow, orange, red, pink, brown, violet, blue, and green. For example, H&d (1969) points out that there are a lot of different colours all of which are called brown, and it appears the merging boundaries do not follow any psycho-physical rules. A yellow which is made a little greenish is considered green, irrespective of being more yellowish in colour: if however it was to be made reddish, it would still remain yellow up to the point when it became orange. When a vivid blue or green is made blackish or greyish, it would still be perceived as blue or green, but a red or a yellow made blackish is perceived as brown. A whitish red is a pink, but a whitish yellow is a yellow. The reason for this, according to HBrd (1969; 1982), is to do with the cultural functioning based on aspects of every day life, as in "the blue sky", the "pink complexion", "everything that grows is green", and so on.
It is important to note that a colour model which excludes colours like brown and pink from its reference points is not in any way deficient, because all colour sensations can be mapped using the six attributes of whiteness, redness ... etc., and these will appear in an appropriate location within the colour space, according to the NCS model. However, an artificial system design to model colour perception should necessarily be able to identify the 'different types of browns' for example, to be of intelligent use, and this is not the same as identifying RGB mixes or specifications.
Colour Qualifies
Colour qualities or attributes have claimed powerful research results with experiments showing that people can gauge with remarkable accuracy the similarity of a colour, i.e. able to identify its position, with respect to the six elementary colour attributes. In a Swedish study reported at the inaugural Colour 69 Congress (Hard, 1969), it was found that when observers in the experimental group were asked to estimate the absolute perception of a "mid-grey'' sample, they accurately judged it to be 50% black and 50% white with a *5% confidence interval (p < 0.05). Compared with a "control" group, who were asked to compare the colour sample with visual reference points, the accuracy was &3.5%, suggesting that the absolute judgments can be done with close enough accuracy.
This appears to offer further evidence that intentionalify plays a key role in the colour perception process, most specifically in maintaining object constancy. It can be argued also, that simultaneous colour contrast is a product of intentionality, because it does not reflect what is "out there" in the real world from a causal perspective.
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The NCS Model
The NCS Model is a colour solid that can be visualised as a double cone. The outer circle symbolizes all conceivable hues of pure chroma. The vertices symbolise the achromatic or the blackness/whiteness scale, and each slope towards the outer circle, represent scales from chromatic to achromatic points, or colour nuance, i.e. from pure hue to blackness and to whiteness. (See figures 1 & 2).
The above representations describe each hue (circle) and each nuance (triangle) with a
Figure 1. The Hue circle Figure 2. The Nuance triangle
particular notation to make each colour quantifiable in perceptual terms. For example, in the NCS notation S 2030-Y90R, S means black4, and 2030 indicates the nuance, 20% blackness (s) and 30% chromaticness (c). The hue Y90R indicates the percentage resemblance of the colour to two chromatic elementary colours, here Y and R. Y90R means yellow with 90% redness. Purely grey colours lack colour hue and are only given nuance notations followed by -N as neutral, e.g. 0500-N is white and 9000-N is black.
The letter S in this NCS notation (S 2030-Y90R) means that the sample is from the Second Edition (Ed 2). Illustrations 0 shareware from http://www.ncscolor.comlenrzelsk/ncs svs.htm
