1

Linguistic categorization of BLUE in Standard Italian

Accepted for publication in Proceedings of the 3rd Progress in Colour

Studies Conference in Glasgow, 14–17 July, 2012. Carole Biggam et al.,

eds. Amsterdam: John Benjamins.

Mari Uusküla

Institute of the Estonian Language, Tallinn, Estonia

1. Background and objectives

This chapter describes a study of the linguistic categorization of BLUE in

Standard Italian.1 It was initiated within the framework of Berlin and Kay’s

Basic Colour Term theory (1969), now revised, updated and usually referred

to as the “UE model” (standing for “Universals” and “Evolution”). The

research of Berlin, Kay and their colleagues has shown that the majority of

languages have between two and eleven basic colour categories (BCCs)

which are labelled with basic colour terms (BCTs). Moreover, it was found

that most societies have developed their basic categories over time in a

broadly similar order, which is shown in the current diachronic sequence of

the UE model (Kay, Berlin, Maffi, Merrifield and Cook 2009, 11). In

addition, the concept of “linguistic categorization” has been applied in this

study according to the approach of John R. Taylor (2003) which is built on

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theoretical cognitive linguistics. The first step in the study was to establish

the basic colour inventory in Italian, but the scope of the full research goes

beyond that. For this present chapter, discussion will be concentrated on

BLUE.

Blue, being prototypically the colour of the sky and the sea, seems to

be so important for several languages and cultures of the world that some of

them have developed multiple salient categories to convey its meaning, and

they may denote different types of blue with different basic terms. One of

these languages is Italian, which, according to numerous studies, possesses

two (or even three) separate salient words for BLUE: blu, azzurro and celeste

(Giacalone Ramat 1967, 198–200; Grossmann 1988; Paggetti, Bartoli, and

Menegaz 2011; Paramei and Menegaz 2013; Philip 2006; Sandford 2011).

The best known example of a similar distinction occurs in Russian, which

has been shown to have twelve BCTs (one more than the usual maximum)

as there are two for the blue category: sinij “(dark) blue” and goluboj “light

blue” (Davies and Corbett 1994, Paramei 2005). Interestingly, Italian is not

the only language in the Mediterranean area that distinguishes between two

different types of blue with both functioning as autonomous linguistic

categories. The differentiation between two blues has also been reported for

Greek (Athanasopoulos 2009; Thierry, Athanasopoulos, Wiggett, Dering,

and Kuipers 2009), Maltese (Borg 2011), Catalan (Davies, Corbett, and

Margalef 1995), Turkish (Özgen and Davies 1998) and other languages in

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the circum-Mediterranean linguistic area (as defined by Cristofaro and

Putzu 2000). There seem to be considerable similarities in the cognitive

categorization of colour across various societies but there are, however,

differences in their linguistic encoding.

The aim of this study is to investigate how both inter- and intra-

linguistic features contribute to the linguistic encoding of BLUE in Italian. A

series of field experiments have been employed, examining both the

context-free and context-restricted usage of blue colour terms. The results

affirm that Italian has at least two different categories for BLUE, one

denoting dark shades of the hue and another term (or terms) referring to

lighter shades. It is made clear from the experiments that blu, azzurro and

celeste are all salient colour terms used with consistency and with relatively

high consensus among native speakers. They function only as partial (not

full) synonyms and consequently cannot be substituted for another blue term

in a particular context.

The interviews were carried out exclusively in Standard Italian rather

than any specific Italian dialect, although speakers’ dialects were recorded

in the background questionnaire. As the field work was performed in

Florence and the surrounding area (for example, in Fiesole), the majority of

the participants had experienced at least some exposure to Florentine or

Tuscan dialects, regardless of whether they actually considered themselves

to be dialect speakers or not. Researchers often agree that Standard Italian

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should be defined as a language which has its roots in the medieval

Florentine vernacular. It is used in the media, the military and at school; and

is a language that, nowadays, is both spoken and understood all over the

Italian peninsula (Harris 1988, 19; Nocentini 2004, 227–228). Standard

Italian should not, in general, contain features belonging to any specific

Italian dialect. However, speakers of different dialects may often preserve a

phonological accent and dialectal vocabulary in their speech, in spite of

their efforts to speak the standardized variety (Vincent 1988, 279).

2. Methodology

In order to understand how the linguistic categorization of BLUE functions in

Standard Italian, different techniques and measures were used. The data

were mainly obtained through field research, and later compared with the

information on blue terms in dictionaries and corpora. Several examples

from these published sources were also used to compile a collocation-

association questionnaire.

In the first experiment, the classic list and colour-naming tasks (as

described in Davies and Corbett 1994) were used to identify the basic colour

term inventory, and to verify the status of each potentially basic colour term

for blue in Italian, namely, blu, azzurro and celeste. In addition to the list

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task frequency and mean position, the cognitive salience index (Sutrop

2001) was also calculated for each individual colour term. Sixty-five colour

tiles were used as stimuli in the colour-naming task (for the selection

criteria, see Davies, MacDermid, Corbett, McGurk, Jerrett, Jerrett, and

Sowden 1992). These were 5x5 cm rigid wooden tiles, each covered with

matt-finish coloured paper from the Color-aid Corporation’s 220-sample set,

presented in natural daylight on a grey cloth one at a time in a random

sequence. Color-aid uses a modification of Ostwald’s colour system, in

which every colour can be described by three characteristics: colour tone or

hue, tint (the content of white) and shade (the content of black). The 220-set

contains twenty-four chromatic colours, including these six: yellow (Y);

orange (O); red (R); violet (V); blue (B); and green (G); as well as their

transitional tones, such as blue-green (BG) and blue-green-blue (BGB).

Every chromatic colour can be blended with either white (T1–T4) or black

(S1–S3). The higher the number, the greater degree of tint (white) or shade

(black) is present. There are also eight greys, on a range from very pale

(whitish grey) to very dark (almost black). The City University Colour

Vision Test (Fletcher 1980) was used to ascertain whether all subjects had

normal colour vision. Lighting (natural daylight) and presentation

conditions were closely comparable for all subjects throughout the

experiment.

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The second experiment involved showing a selection of fifty-five

colour stimuli to participants, taken from the Color-aid matt-finish 314-set.

The participants were then instructed to sort the colours into groups

according to the perceived resemblance of the tiles. After the sorting task

they were asked to name each group. Use of the 314-stimuli set makes it

possible to distinguish between cool and warm colours, therefore adding a

“temperature” dimension to the array. Every chromatic colour (presented as

B HUE) is additionally divided into a cool section (marked with the letter c,

so Bc indicates the cool tonalities of blue) and a warm section (marked with

the letter w, so Bw indicates tonalities of blue inclined towards violet). The

314-set also contains a separate selection of dark shades (marked with DS)

and light tints (LT). The selection of stimuli used in the experiment was

strongly inclined towards blue, and included forty-five colour patches from

different tonalities of blue. Some greens and purples (at the category

boundaries) and yellows were added to the selection in order to make the

task less transparent to subjects. A group of fifty-four people participated in

the experiment, and the normality of their colour vision was verified with

the City University test (Fletcher 1980).

The same fifty-four subjects also performed the best-example task in

which they were asked to indicate the best example of the range of each

salient Italian blue term: blu, azzurro and celeste. All fifty-five tiles were

displayed at the same time by laying them on a table in front of the subject.

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Each subject’s choices were recorded. If participants were unhappy with

their first groupings, they were allowed to regroup the tiles. In such cases,

only the final choice was considered.

The next experiment, the collocation-association task, consisting of

eighteen nouns and adjectives, was carried out with a group of thirty-seven

subjects who had not taken part in either of the two previously described

experiments. The subjects had to say which of the three words for blue, blu,

azzurro or celeste, they would associate with a presented noun or adjective

to form a correct or suitable expression in Italian. The nouns and adjectives

were always presented in a fixed order. The subjects were allowed to pick

more than one option or to indicate that none of the three words would be

acceptable. Introduced nouns and adjectives, among them cielo “sky”,

maglia “T-shirt”, mare “sea”, penna “pen”, paradiso “paradise”, occhi

“eyes”, matita “pencil”, chiaro “light”, sangue “blood”, principe “prince”,

scuro “dark”, cobalto “cobalt” and so on, were extracted from dictionaries

and general reference corpora. All subjects were encouraged to complete the

list of words associated with blu, azzurro or celeste by adding any nouns or

adjectives which they felt were missing. The experiment aimed at observing

contextual impact on the linguistic categorization of BLUE. Subsequently,

the same subjects who participated in the collocation-association task were

requested to describe the colours of blu, azzurro and celeste.

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All interviews were carried out orally by a non-native, but fluent

Italian speaker (the present author) in the period 2006–2009. One individual

was tested at a time.

3. A description of the subjects

A total of 185 adult subjects with normal colour vision took part in the

study. Additionally, six schoolchildren between 11 and 15 years of age

participated in the list and colour-naming tasks, and two schoolchildren

aged 15 and 17 were involved in the free-sorting task. The subjects can be

divided into three groups: 102 participants who performed the list and

colour-naming task (consisting of 56 females and 46 males with a mean age

of 38.6 years); 54 participants who performed the free-sorting and best-

example task (consisting of 30 females and 24 males with a mean age of

39.5); and 37 participants who performed the collocation-association task

(consisting of 24 females and 13 males with a mean age of 40.7).

All the test subjects participated voluntarily. The aim was to recruit

volunteers with different backgrounds: interviewees were born and grew up

in different places all over Italy and spoke various dialects besides Standard

Italian (for details, see Uusküla forthcoming), which was the language of the

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interviews. All subjects filled out a biographical questionnaire either prior to

or after the tasks they performed.

4. Results and discussion

The main findings of the three experiments are in accordance with recent

studies (Menegaz and Paramei 2013; Paggetti et al. 2011; Philip 2006;

Sandford 2011). Detailed results of the list, colour-naming and collocation-

association tasks will be published in Uusküla (forthcoming).

4.1. The list and colour-naming tasks

In the elicitation task, 90% of subjects listed blu, while 76% named azzurro

and 62% celeste. For a comparison, it is important to underline that 99.9%

listed giallo “yellow” and 96% rosso “red”. According to the naming

frequency, blu ranked sixth after giallo “yellow”, verde “green”, rosso

“red”, bianco “white” and nero “black” (the latter two were named with

equal frequency), while we find azzurro in tenth place and celeste in

thirteenth place. On average, subjects remembered blu before celeste and

azzurro, their mean position values were respectively 6.15, 9.02 and 9.41.

The cognitive salience index that takes into account both naming frequency

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and the mean position (Sutrop 2001), puts blu in fifth, azzurro in ninth and

celeste in twelfth place among Italian colour terms. The results of the list

task suggest that both blu and azzurro appear to be psychologically salient

and, therefore, potential BCTs according to Berlin and Kay’s guidelines on

basicness (Berlin and Kay 1969, 6).

The colour-naming task provided similar results to the elicitation task.

All three terms for blue achieved significantly higher total frequency rates,

especially in comparison with obvious non-basic colour terms such as lilla

“lilac”, fucsia “fuchsia”, and compound terms such as verde scuro “dark

green”. However, what is striking is that there was no general convention in

naming azzurro. This may have occurred due to the absence of an azzurro

tile in the selected set of stimuli, or reveal that azzurro may be only rarely

used in reference to colour patches.2 In fact, azzurro seems to be

semantically more abstract than blu or celeste. That might also be one of the

reasons why azzurro has been used in technical descriptions of the blue hue

or in phrases expressing type modification, for example, volpe azzurra.

Both blu and celeste achieved dominance frequency, which can only

be reached when at least fifty per cent of the subjects consistently name one

colour tile with the same colour term. The highest agreement percentage for

blu was 54%, used with reference to the colour tile BVB HUE, while for

celeste it was 57% for the tile BGB T3. The results can be interpreted as

follows: blu signifies a darker blue with a slightly purplish overtone, while

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celeste is most certainly a light colour (T3 indicates that the hue is blended

with a large amount of white; for a description of the Color-aid system, see

Section 2). The specificity index is calculated using two parameters, total

frequency and dominance frequency. It shows the precision of a term in

reference to certain colours (Davies and Corbett 1994). Intriguingly,

specificity index values tend to be relatively low for all blue terms, along

with that for the colour term verde “green”. This finding is in line with

Taylor’s hypothesis that, in a natural environment, green and blue colours

lack referential stability as they are predominantly used for entities without

a fixed colour, such as the sky and the sea, and therefore, they have a

somewhat different status among other colour terms (Taylor 2003, 13).

Thus, the list and colour-naming tasks provide somewhat

controversial results. While azzurro appears to belong to the mental lexicon

of most native speakers of Italian, its application as a colour term remains

tenuous in a context-free colour-naming task. The other BCT candidate, for

light blue, celeste, displays an opposing trend. There seem to be certain

naming conventions among subjects in the usage of celeste, but in a

spontaneous elicitation task fewer participants list celeste than blu or

azzurro.

The results may be influenced by the fact that interviews were carried

out in Florence and the neighbouring area because, as speakers of the

Tuscan dialect, the subjects were likely to treat celeste, rather than azzurro,

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as the appropriate word to describe lighter shades of blue (as compared to

blu which denotes darker shades). They therefore regarded azzurro as being

of minor significance, and as belonging to the literary register, as stated by

many of the subjects after the tasks had been carried out. It should be noted,

however, that Paramei and Menegaz (2013) whose psycholinguistic study

was conducted in Alghero, Sardinia, and Sandford (2011) who recruited her

subjects in Perugia, Umbria, came to a different conclusion, namely, that

azzurro was a second BCT for BLUE, with celeste having only minor

significance. Vincent (1988, 279) has claimed that Italian speakers

sometimes “betray their geographical origin” by their vocabulary choices,

even when trying to speak the standard variety of the language.

Consequently, the blue lexicons of Italian dialects differ: Tuscan speakers

tend to use blu and celeste, but azzurro only very rarely, while speakers of

Sardinian dialects commonly use blu and azzurro, but very rarely celeste.

This hypothesis, however, needs to be tested further.

4.2. Free-sorting and best-example tasks

The subjects grouped fifty-five presented colour tiles into different

categories according to their resemblances. The greatest number of groups

made by a subject was fifteen, while the smallest was three. On average,

fifty-four subjects made 6.2 groups. If the fifty-five tiles were divided into

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four groups by a subject, they consisted of the following: giallo “yellow”;

verde “green”; blu “blue”; and viola “purple”. On average, about thirty tiles

were classified as blu. A total of 35% of the respondents sorted all the tiles

they considered to be blue together, naming them blu, while a total of 16%

of the subjects made two categories: blu and celeste. A total of 11% of the

subjects sorted different shades of blue into two groups: blu and azzurro,

while another 11% made three groups: blu, azzurro, and celeste. Only 1% of

the subjects named all the blue tiles as azzurro. A total of 26% of the

subjects used other combinations, such as other monolexemic words for

blue, or various modifications of simple terms, such as blu scuro “dark blu”,

celeste chiaro “light celeste”. The results of the free-sorting task show that

blu is used as a general name for the BLUE category, equivalent to English

blue. There were only a very few subjects who sorted blue into three

separate categories (not counting those who created blu scuro, celeste

chiaro and other groups). A more detailed free-sorting task analysis with a

multidimensional scaling solution is be provided in a separate article

(Bimler and Uusküla, 2014).

The best-example task followed the free-sorting task. It is crucial to

point out that the consensus rate among subjects in this task was extremely

low, as they were allowed to pick any of the fifty-five stimuli as “the best”

example of blu, azzurro and celeste. The colour term blu had the highest

agreement (30%) of all three colour terms. Subjects did not agree on the

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representation of azzurro (the highest agreement was only 11%). Some

subjects were unable to indicate the best example of each category: fifty

subjects picked their best example of blu, forty-eight subjects picked their

best example of celeste, and forty selected their best azzurro. The best-

example task results are represented in Table 1 (for comparison, see

Giacalone Ramat 1967, 199; Paramei and Menegaz 2013).

Table 1. Representation of the blue region in Italian according to the results

of the best-example task

Colour category Examples of colour tiles by their

Color-aid code

Description

BLU

B S1, B S2, B DS

(dark) blue

AZZURRO

Bc T2, B T2, B T1, Bc T3, B

HUE

mid blue

CELESTE

B T4, Bc T3, B T3, C LT

light blue

It becomes apparent from the best-example task results that it is

important to take into account different criteria in the linguistic

categorization of colour terms. Besides hue and lightness, there might be

other features that play a critical role in the conceptualization process of

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blue in Italian. According to the results, both the lighter shades, azzurro and

celeste, seem to represent cool varieties of blue (for comparison with

Russian goluboj, see Paramei 2005).

4.3. Collocation-association task and reference to objects

The most frequent collocation that was offered by all subjects was principe

azzurro, literally “Blue Prince” (Prince Charming in English). These two

words seem to represent an ideal idiomatic expression or, in other words,

the closest syntagmatic relationship between two words. None of the thirty-

seven participants gave a second thought to which of the three possible

words to collocate when they heard the noun principe. A Google search for

principe azzurro provides an image of a typical Prince Charming of the

Walt Disney Company, habitually dressed in a light blue garment (there is

more on the metonymical meaning of the expression in Philip 2006, 85).

Another frequently named word pair in the task was the association of scuro

“dark” with blu, named by 97% of the subjects (some subjects also used the

syntagmatic expression blu scuro “dark blu”). The next most frequent words

offered in association with blu were penna “pen” (by 92% of the subjects)

and sangue “blood” (89%). Sangue blu “blue blood” is an idiomatic

expression that signifies royal blood or a royal bloodline.

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The first three most frequently offered words in association with

azzurro were, as already mentioned, principe (100% of the subjects), cielo

“sky” (76%) and occhi “eyes” (70%), while celeste was mostly collocated

with paradiso “paradise” (78%), maglia “T-shirt” (73%) and chiaro “light”

(70%). It should be emphasized that the primary meaning of celeste in

Standard Italian, as represented in dictionaries, is “heavenly, related to the

sky”. This aspect was already considered in the study design by adding

words, such as paradiso, in the presented list. The purpose was to examine

which semantic meaning dominates in the mental lexicon of a native Italian

speaker.

Some subjects slightly misunderstood the task, offering their

associations rather than syntagmatic collocations. For instance, they

described the colour of the sky as follows: “it can have all three colours, blu,

azzurro and celeste, depending on the circumstances, such as day or night

time”. Generally, both mare “sea” and cielo “sky” were offered in

association with blu, azzurro and celeste (similar to descriptions presented

in dictionaries), although the semantic naming motivation differed. Celeste

and azzurro usually evoked positive emotions (for example: il bel cielo

azzurro diurno “the beautiful blue daytime sky”; and il bel mare celeste

vicino alla costa “the beautiful blue sea close to the shore”). The subjective

senses of the blue words became evident in subjects’ descriptions of the

colours of the sea, as in the use of blu in ho paura dal mare blu “I’m afraid

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of the blue sea” (referring to the high seas). This example suggests that blu

has a link to something unsteady, even threatening. Similarly, Deacy and

Villing (2004, 85) observe that the Greek colour adjective glaukos, a kind of

blue, could involve instability and fear with reference to the sea. As Philip

has shown in her study based on the analysis of Italian corpora, blu was

associated with either black or purple in Italian culture, and, as a result of

this relationship, blu partially took over their connotations, including the

negative ones (2006, 84). However, azzurro, denotes the true blue of the sky

(Philip 2006; Sandford 2011) and is used in association with heaven and the

divine.

Keeping this in mind, we could, intriguingly, draw parallels with

colour naming and categorization patterns in Hungarian and Czech, in

which a light shade of red is associated with positive emotions, while a dark

red shade evokes negative connotations (Uusküla 2011, 151–152).

5. Discussion

The main findings are consistent with previous studies that highlight the

specific status of BLUE in Italian. It is generally believed that Italian

conceptualizes BLUE in two (or even three) different categories, including

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one darker blue and one or two lighter blue(s), represented by the lexemes

blu, azzurro and celeste (Paggetti et al. 2011; Philip 2006; Sandford 2011).

Blu is predominantly used with reference to dark and intensive

varieties of blue, and azzurro with reference to lighter shades. Celeste seems

to denote the lightest of all three shades, especially when the three terms are

used to describe the colour of cloth. However, blu in present-day Italian can

also be considered as an umbrella term capable of referring to all blue

shades. Interestingly, Giacalone Ramat also came to this conclusion as

early as 1967.

At the cognitive level, blu, azzurro and celeste seem to function as

autonomous categories, as shown by the cognitive salience index in the list

task. Moreover, blu, azzurro and celeste cannot be substituted for another

blue term in particular collocations such as in set phrases, also referred to as

paradigmatic collocations (see Lyons 1977, 230‒269; Uusküla 2011,

151‒152). Paradigmatic collocations tend to be conservative, well-preserved

over time and rarely interchangeable within a particular context. They are

rarely subject to contemporary language change, being rather reflections of

how language was used historically.

In all the experiments the prevailing colour term was blu: it had a high

naming frequency, both in the elicitation and colour-naming tasks, and was

remembered and named before other terms for blue in the list task which

proves its psychological salience (this conclusion is consistent with the

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findings in Sandford (2011)). The results of the colour-naming task show

that blu has the strongest claims to BCT status, gaining high consensus and

specificity index rates in the tile naming task. Celeste was the principal term

for one colour tile, while azzurro failed to achieve consensus and specificity

rates, and therefore does not satisfy the crucial thresholds in order to claim

BCT status according to Davies and Corbett’s criteria (1994, 78–80). If we

try to interpret the results according to Berlin and Kay’s 1969 guidelines for

basicness, we can conclude that all three blue terms are monolexemic and

psychologically salient. But how can we explain their degree of restriction

to a particular context, or their degree of hyponymy (the extent to which

their meaning can be included within the meaning of another blue word)?

Can we argue that celeste is a type of blu, or that blu is a type of azzurro?

As explained above, the experimental results have supported Giacalone

Ramat’s earlier suggestion that blu can function as an umbrella term for all

shades of blue even though its principal role is in the darker regions of that

hue. The lighter region of blue is earmarked by either celeste or azzurro and

regional dialects seem to have a specific role in preferring one to the other.

Also the blue category in Italian may still be evolving as speakers of modern

Standard Italian appear to be witness to a state of flux in the blue colour

terms, and, moreover, are participating in the conscious or subconscious

decision as to which categories will develop further.

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The results of the free-sorting task confirm the strong status of blu as

a general name for the BLUE category. Nevertheless, a considerable number

of subjects sorted blue tiles into more than one group, labelling them mainly

as blu and celeste (for further discussion, see Bimler and Uusküla, 2014).

We could speculate that this occurred as a result of the stimuli selection, that

is, the large number of different blue shades used in the task but, in fact, the

distinction between light and dark blue only seems to emerge in societies

that classify blue into more than one category. Other languages with only

one blue category, such as English, do not make a significant distinction

between lighter and darker blues (for a comparison of Italian, English and

Russian blue categories, see Bimler and Uusküla, 2014).

The results of the best-example task revealed that the distinction

between light and dark blue might not be made solely on a lightness-

darkness scale but, additionally, on a “temperature” (warm versus cool)

dimension. Subjects indicated that both azzurro and celeste may, partly, be

regarded as cool varieties of blue. This suggests that there may be other

phenomena, such as a separation into warm and cool blues, involved in the

Italian cognition of this hue. Biggam points out that the meanings of colour

terms in various languages often include what an English speaker would

regard as non-colour features, such as surface texture, and even not

necessarily visible features, such as dryness (Biggam 2012, 5‒7), so it is

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possible that certain of these features, such as “temperature”, should be

taken into account in future researches into the Italian blue category.

The main outcome of this research is that Italian native speakers

habitually denote the blue region of the colour space with (at least) two

salient terms in their everyday speech. Depending on the dialectal

background of the speaker, the choice for designating a lighter variety of

blue lies between celeste and azzurro. However, it is not uncommon for a

native speaker to apply all three terms, including blu, to different denotata.

The above findings converge to suggest that the cognition of BLUE in

Italian is influenced by extra-linguistic phenomena, such as “temperature”

and emotional overtones, which have contributed to the categorization

process. We assume that the second (or third) BLUE only becomes

cognitively salient when there is a need to distinguish between shades of

different objects or referents, such as the colour of the sky at night, as

opposed to during a summer day, or the colour of sea water close to, as

opposed to farther away from the shore. These conditions, dependent on

paleness / darkness, and partly on emotions that a speaker wishes to

communicate, appear to play a crucial role for Italian speakers in the

categorization of BLUE.

Acknowledgements

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I am grateful to Galina V. Paramei, David Bimler, Alexander Borg, Irene

Ronga and other participants at the PICS12 conference, to anonymous

reviewers for their valuable suggestions and comments, and to Carole

Biggam for patience and devotion as an editor. The study was supported by

the Estonian Science Foundation Grant no. 8168 (awarded to present author)

and Estonian Ministry of Education and Research project SF0050037s10

(awarded to Urmas Sutrop).

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1 The purpose of this chapter is to present and discuss some of the results of the research

carried out in Italy. A fuller account of list, colour-naming and collocation-association task

data which has partially been analysed in this chapter will appear in the 2nd

edition of “The

Language and Color in the Mediterranean”edited by Alexander Borg.

2 I am grateful to Urmas Sutrop for pointing out this possibility to me.