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Perceptual and categorical judgements of colour
similarity
a
a
a
a
Jules Davidoff , Julie Goldstein , Ian Tharp , Elley Wakui & Joel Fagot
b
a
Psychology Department, Goldsmiths, University of London, London, UK
b
Laboratoire de Psychologie Cognitive, University of Provence, Marseille, France
Version of record first published: 24 Jul 2012
To cite this article: Jules Davidoff, Julie Goldstein, Ian Tharp, Elley Wakui & Joel Fagot (2012): Perceptual and
categorical judgements of colour similarity, Journal of Cognitive Psychology, DOI:10.1080/20445911.2012.706603
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JOURNAL OF COGNITIVE PSYCHOLOGY, 2012, iFirst, 122
Perceptual and categorical judgements of colour
similarity
Jules Davidoff1, Julie Goldstein1, Ian Tharp1, Elley Wakui1, and Joel Fagot2
1
Psychology Department, Goldsmiths, University of London, London, UK
Laboratoire de Psychologie Cognitive, University of Provence, Marseille, France
Downloaded by [fagot joel] at 10:54 30 July 2012
2
Consideration is given to the tasks that make judgements of colour similarity based on perceptual
similarity rather than categorical similarity. Irrespective of whether colour categories are taken to be
universal (Berlin & Kay, 1969) or language induced (Davidoff, Davies, & Roberson, 1999), it is widely
assumed that colour boundaries, and hence categorical similarity, would be used when categorising
colours. However, we argue that categorical similarity is more reliably used in implicit than in explicit
categorisation. Thus, in Experiment 1, we found that category boundaries may be overridden in the
explicit task of matching-to-sample: There was a similar strong tendency to ignore colour boundaries
and to divide the range of coloured stimuli into two equal groups in both Westerners and in a remote
population (Himba). In Experiment 2, we showed that a distinctive stimulus (focal colour) in the range
affected the equal division in a matching-to-sample task. Experiment 3 tested the stability of a category
boundary in an implicit task (visual search) that assessed categorical perception; only for this task was
categorisation largely immune to range effects and largely based on categorical similarity. It is concluded
that, even after colour categories are acquired, perceptual rather than categorical similarity is commonly
used in judgements of colour similarity.
Keywords: Colour similarity; Categorical similarity; Perceptual similarity; Colour naming.
Two ways have been proposed for judging colour
similarity (Goldstein, 1948; Kay & Kempton,
1984; Roberson, Davidoff, & Braisby, 1999;
Roberson, Hanley, & Pak, 2009); ways that we
will call perceptual similarity and categorical
similarity. Perceptual similarity as defined here
is dictated by the physiology of colour vision and
will be identical for observers with normal visual
sensory function. Judgements by perceptual similarity produce some discontinuities in colour
space but, by themselves, (see Webster & Kay,
2011) would not produce even the basic colour
categories of Berlin and Kay (1969). We cannot
rule out the possibility that primary colour
prototypes (foci) have a physiological basis (Kay
& Regier, 2006; Philipona & O’Regan, 2006) but
it is also clear that what counts as a focal colour
for one language does not do so in other
(Roberson, Davidoff, Davies, & Shapiro, 2004).
A more complex account must therefore be given
for the wide range of colour names (categories)
and hence categorical similarity judgements used
by speakers of the world’s languages.
One recent approach to explain colour categories, popular for computer simulations, is to
argue that categories optimize the communication
benefits of a solution for any division of colour
space (Komarova, Jameson, & Narens, 2007;
Correspondence should be addressed to Jules Davidoff, Room 117 Ben Pimlott Building, Psychology Department, Goldsmiths,
University of London, New Cross, London SE14 6NW, UK. E-mail: J.Davidoff@gold.ac.uk
This work was supported by EC 6th Framework grant 012984 Stages in the Evolution and Development of Sign Use (SEDSU).
JG was supported by an ESRC studentship. We would like to acknowledge Alan D. Pickering for his advice on the decision bound
modelling. We are grateful to K. Jakurama for helping in the field.
# 2012 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business
http://www.psypress.com/ecp
http://dx.doi.org/10.1080/20445911.2012.706603
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2
DAVIDOFF ET AL.
Regier, Kay, & Khetarpal, 2007; Steels & Belpaeme,
2005). The underlying principle in these models
can be traced back to Jameson and D’Andrade
(1997) and in turn to ideas of Garner (1974) that
an optimal solution would minimise withincategory similarity and maximise between-category similarity. Some formulations for the optimal
partition have favoured a solution based on the
Berlin and Kay basic colours (Dowman, 2007;
Regier et al., 2007), but others have only weakly
(Griffin, 2006) or have no special place for them
(Komarova et al., 2007). However, these partitions are only going to explain part of the data on
colour categories. Their first weakness is that they
have no mechanism whereby observers may
switch from categorical to perceptual judgements;
a switch that is argued to rely on the use, or
not, of language networks (Davidoff & Roberson,
2004; Kay & Kempton, 1984; Roberson et al.,
1999). A second weakness common to these
models (Komarova et al., 2007; Regier et al.,
2007) is that they do not explain how different
boundaries arise between languages. For example,
two 5-term colour languages from very different
cultures and environments studied in Roberson,
Davidoff, Davies, and Shapiro (2005) appear to
divide colour space in similar ways but the foci
and boundaries were specific to each language
with specific behavioural consequences for learning and memory.
An alternative approach to colour categories is
to link them to colour names. More than that, the
claim is that the linking to a name may cause a
change in perception*colours with different
names (from different categories) look more
different, and colours from the same category
look more similar, than would be predicted from
our physiology (Kay & Kempton, 1984). Thus,
categorical colour similarity, as for the categorisation of continua in the auditory domain, implies
the property of Categorical Perception (CP;
Harnad, 1987). In practice, CP is assessed by
performance with a target being more accurate/
faster when foils are from different categories
than when they are from the same category
1
It has been argued that infants show colour CP (e.g.,
Franklin & Davies, 2004) and, in that case, language would not
be a prerequisite. The arguments pro and con infant colour CP
are given in Davidoff and Fagot (2010). However, as it is
agreed that these infant colour categories are not necessarily
present in the adult, they are not essential for consideration of
the present data.
despite all foils being at an equal separation
from the target as judged by perceptual similarity.
The cross-category advantage (CP) was linked
to language by Davidoff et al. (1999); indeed,
subsequent neuroimaging research has shown
activity in language areas during colour similarity
judgements (Siok et al., 2009; Tan et al., 2008).1
One particularly strong line of supporting evidence is that a concurrent verbal task removes CP
(Gilbert, Regier, Kay, & Ivry, 2006; Roberson &
Davidoff, 2000; Winawer, Witthoft, Frank, Wu, &
Boroditsky, 2007). Importantly, the interference
task to remove CP does not require rehearsing
colour names (Roberson & Davidoff, 2000). Even
though colour CP derives from a language’s
colour terms (e.g., Roberson et al., 2004), the
change in appearance of colours has become
linked to a neural network that involves frontal
areas, language areas but not those that when
damaged result in colour anomia, and the visual
cortex (Tan et al., 2008)
Not all researchers go along with a CP account
of categorical similarity. One early account regarded colour names as irrelevant (Rosch Heider
& Olivier, 1972). They proposed that the Berlin
and Kay (1969) colour categories are universal
and that differences in colour names between
cultures are unimportant to the underlying cognitive structure; this was denied by subsequent
evidence that colour categories were closely
linked to the colour terms in the speaker’s
language (Davidoff et al., 1999; Kay & Regier,
2006; Roberson et al., 2000; Winawer et al., 2007).
However, many of the early experiments on CP
(Bornstein & Korda, 1984; Roberson et al., 2000)
asked for an identity match in memory. It could
be that observers were merely matching the
names of colours and that would be sufficient to
account for what appeared to be colour CP
(Brown, Lindsay & Guckes, 2011; Munnich &
Landau, 2003). One clear denial that naming by
itself can provide an explanation for CP comes
from a patient who could not name colours yet
still showed CP (Roberson et al., 1999). Furthermore, recent research does not involve memory
and/or avoids overt naming, For example,
Winawer et al. (2007) asked for an identity match
with the target and samples present, and Daoutis,
Pilling, and Davies (2006) introduced a visual
search paradigm with rapid presentations, now
commonly in use, that asks the observer simply to
find the odd-one-out colour.
It has also been argued that CP is merely
apparent because it really derives from the metric
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COLOUR CATEGORISATION
used to produce colour stimuli. It is most usual
for psychologists to assess equal separation from
the Munsell system (Munsell, 1905; Newhall,
Nickerson, & Judd, 1943), which is a metric where
colours are intended to be equally spaced based
on human perceptual similarity judgements. The
generality of the claim for equal separation is
found justified from threshold discrimination
studies (Davidoff & Fagot, 2010; Roberson
et al., 2009). But, as has been pointed out by
Brown et al. (2011), the assumption about equal
spacing in the Munsell system falls down particularly for colours that cross the blue-green boundary.2 However, a defect of spacing within the
Munsell system should predict that speakers of
languages that use the same word for green and
blue should nevertheless show CP; they do not
(Roberson et al., 2000). Moreover, defects of
spacing within the Munsell system for blue-green
would not explain why CP is found for other
category boundaries (Goldstein, Davidoff, &
Roberson, 2009; Roberson, Pak, & Hanley,
2008), or for CP distinctions between blues
where languages make that distinction (Thierry,
Athanasopoulos, Wiggett, Dering, & Kuipers,
2009; Winawer et al., 2007) or for newly acquired
categories within our normal green category
(Ozgen & Davies, 2002). For the present experiments, the choice of metric is somewhat irrelevant
as our main point will be to show that the same
metric can produce both perceptual and categorical judgements of colour similarity.
After the acquisition of colour terms, not only
are judgements made by categorical similarity but
they are done so automatically (Clifford, Holmes,
Davies, & Franklin, 2010; Fonteneau & Davidoff,
2007; Holmes, Franklin, Clifford, & Davies, 2009;
Siok et al., 2009; Tan et al., 2008; Thierry et al.,
2009). For example, Thierry et al. (2009) showed
that the two Greek colour terms distinguishing
light and dark blue leads to greater and faster
perceptual discrimination of these colours 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
2
Brown et al. (2011), Fonteneau and Davidoff (2007),
Webster and Kay (2011), and Witzel and Gegenfurtner (2011)
were unable to replicate the many findings (e.g., Gilbert et al.,
2006) that show CP for green compared to blue only for
stimuli presented to the language hemisphere. They all found
the advantage across the boundary for both visual fields.
Brown et al. argue for a physiological account for categorical
similarity at that boundary.
3
stimuli during a colour oddball detection task in
English participants, but it was significantly larger
for blue than green deviant stimuli in native
speakers of Greek. The categorical response was
observed early in the ERP trace (i.e., P1 normally
associated with perceptual differences) as has also
been shown by Holmes et al. (2009).
All of the tasks commonly in use in CP
research might have been thought to emphasise
judgements of perceptual similarity but they
instead promote judgements of categorical similarity. One might also note that they rely on
implicit judgements to assess colour similarity.
The connection of categorical similarity to implicit processing is also drawn from a clinical case
study where an aphasic patient still made categorical errors in an identity match despite being
unable to arrive explicitly at colour categories
(Roberson et al., 1999). A contrary example is
found in Webster and Kay (2011), where very
little CP was found in the implicit task of
orientation judgement from colour segmentation;
nevertheless, there is a reasonable case to be
made that the default position in humans is to use,
when available, categorical rather than perceptual
similarity in tasks that ask for colour similarity
judgements.
In the field of concept development there is
much discussion on the relative roles of perceptual and categorical similarity (see Sloutsky,
2010). For colour categories, there is much less
discussion. However, the matter deserves some
attention and the present paper considers tasks
that promote a preference for making perceptual
rather than categorical judgements. Thus, the
main aim of the present paper was to find
conditions under which categorical similarity
would not be used in judgements of colour
similarity. Explicit tasks are more likely to be
susceptible to strategic variation and it is these we
examine first. For example, different groups
of observers were asked to sort a large number
of colours that covered most of colour space
(Roberson, Davies, Corbett, & Vandervyver,
2005). The colours included good examples of
Western colour categories and the outcome for
Western observers could be explained by sorting
by names (categorical similarity). However,
speakers of many African (including Himba*
see later) and Australasian (including Berinmo;
Davidoff et al., 1999) languages with fewer colour
names did not take this opportunity; they made
many more categories presumably based on
perceptual similarity. For our study, we will use
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4
DAVIDOFF ET AL.
the explicit task of matching-to-sample (MTS) as
there is a surface similarity to the implicit tasks
that produce CP. In colour MTS (Fagot, Goldstein, Davidoff, & Pickering, 2006), the observer
is given two sample colours drawn from different
colour categories (e.g., green or blue) and asked
to assign intermediary (target) colours to one of
the samples. In implicit versions of that task, the
use of categorical similarity appears to be mandatory but will it be so in MTS?
In Experiment 1, we alter the range of stimuli
that cross the greenblue boundary. We ask
whether the importance of an existing colour
category boundary might be so great that the
division of stimuli would still be categorical; that
is made at the boundary irrespective of where that
boundary was placed in the range of stimuli (see
Wright & Cumming, 1971, for an argument and
data supporting that outcome with respect to
colour matching in the pigeon). There are several
reasons to expect that result. First, we have seen
that categorical similarity appears to be the
default similarity mechanism. Second, categorical
matching might be favoured because our choice
of colours in the Munsell system might give a
discontinuity at the boundary (Brown et al.,
2011), and lastly the observer might implicitly or
explicitly name the colours. Alternatively, we ask
whether the division of stimuli could be determined by perceptual similarity and hence not
necessarily made at the colour boundary. In
particular, it could be made at the middle of the
range as Komarova et al. (2007) point to ‘‘the
existence of a universal human tendency to
organise stimulus domains using polar opposition’’ (p. 363); this line of argument based on the
tendency to make a binary division is one that we
develop here by altering the range of colours
presented to the observer.
Range effects are found for many judgements
(Poulton, 1989). For example, judgements of size
(small vs. large) are adjusted to the endpoints of the
range (Parducci, 1965) and there is the well-known
effect of central tendency where judgements of,
say, size tend towards some estimate of the middle
of the range (Helson, 1947; Hollingworth, 1910).
We ask these questions concerning range effects
for two very different populations: Westerners and
a population (Himba) from a remote savannah
region of Namibia. The Himba have only five
colour names (Roberson et al., 2004) and make
many more categories than Westerners in colour
sorting tasks (Roberson, Davies, et al., 2005).
With these two disparate populations, we could
determine if the use of perceptual similarity
(binary division) was widespread in the explicit
task of MTS despite their many differences not
least being a quite different colour vocabulary.
In Experiment 2, we ask about the role of focal
colours. Colour categories have been argued to
arise from representations based on focal colours
(Rosch, 1975) where colours are drawn towards
the prototype to which they belong*the magnet
effect of prototypes (Kuhl, 1991). However,
colour categories have also been argued to arise
from demarcation at their boundaries (Ashby &
Gott, 1988; Goldstone, 1994) and it is the norm to
find enhanced discrimination at the colour boundary rather than close to the focus (Bornstein &
Korda, 1984; Gilbert et al., 2006; Roberson et al.,
2000; Winawer et al., 2007; though see Roberson
et al., 2009). In Experiment 2, we included a focal
colour in the range of colours in a similar MTS
task to that of Experiment 1. In previous research
using a face MTS task, Angeli, Davidoff, and
Valentine (2008) found that a distinctive sample
face produced a significant deviation from the
equal division of the range with the psychological
midpoint of the range now shifted away from the
prototype (typical face). Here we ask whether a
focal colour stimulus positioned away from the
midpoint of the range would also affect the
division of stimuli.
In Experiment 3, we turn our investigation of
range effects to an implicit task typically used to
assess categorical similarity for colour. In Experiment 3, we assessed the availability of colour
categories in a visual search task where the range
of colours allowed the existing colour boundary to
fall at the midpoint of the range or was shifted
from it to the same extent as in Experiment 1.
EXPERIMENT 1: COLOUR MTS WITH
VARYING COLOUR RANGES
Colours provide some uncertainty of their allocation at category boundaries (Rosch, 1975) but
the boundary used in Fagot et al. (2006)*that
between blue and green*is agreed to be reliable
and fairly sharp (Bornstein & Korda, 1984;
Boynton & Gordon, 1965; Roberson et al.,
1999). In the present study, the importance of
the boundary colour in a MTS paradigm will be
assessed by changing the range of colours in the
matching task. The change (shift) would make
the range more blue by moving the dominant
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COLOUR CATEGORISATION
wavelength of the two samples in the shortwave
direction, or more green, if moved in the longwave direction. In both cases, and very important,
the midpoint of the new range would not normally be acceptable as the boundary colour
between green and blue. Thus, the question posed
is whether we would still maintain an equal
division of the colours, suggesting judgements of
perceptual similarity, or whether the observer
would make judgements based on categorical
similarity and thereby produce an unequal division
based on the existing colour boundary (Midpoint
Westerners: MW) between green and blue.
To examine the view that there might be a
universal tendency to make binary divisions in
perceptual continua in MTS, we also examined
the effects of range in a remote population. The
Himba of Namibia are an extremely remote
seminomadic population of animal herders estimated between 20,000/50,000 in what has been
described as the last wilderness in southern Africa
(Namibian Government Statistics, 2004). They do
not have words that map directly on to our terms
in their five colour term language but they
nevertheless have sharp boundaries between their
terms (Roberson et al., 2004). Given that the
Himba do not have a boundary between green
and blue, it might be thought unsurprising if range
effects were found for those colours. More interesting would be if range changes were found to
affect one of their own established colour boundaries. A comparable MTS experiment was therefore undertaken on a colour range that crosses the
boundary between two of the Himba colour
terms. Roberson et al. (2004) demonstrated a
sharp boundary for colours that Westerners would
call ‘‘green’’ on both sides of their boundary. To
the yellow side of that boundary colour, the
Himba use the term ‘‘dumbu’’ and to the blue
side ‘‘buru’’. The comparable experiment used
the range with their boundary colour in the
middle (Midpoint Himba: MH) and also used
shift conditions where the range of colours is
moved either towards the shortwave or longwave
end of the spectrum. The design allowed us to see
if the Himba divided the range at the midpoint
and then investigate the effect of the shifts to see
whether the allocation of colours to the sample
was made on the same basis as for Western
observers. For both Western and Himba observers
we used the modelling procedures in Fagot et al.
(2006) to establish where in the range observers
placed their boundary.
5
One further question we address is whether the
names given to the colours are affected by the
range. Naming is of course an explicit task but it
differs from MTS in that the categorical aspect of
colour similarity ought to be built into the
response. Indeed, the evidence to date suggests
that naming is unaltered after changing the range
of colours (Mitterer & de Ruiter, 2008).
Method
Participants. Different Western observers were
used for three colour ranges (MW [no-shift], greenshift, and blue-shift). The eight observers (and
data) for the MW (no-shift) range were taken from
Fagot et al. (2006). Four different French (three
women and one man; age 2429 years, mean 26.7
years) observers and eight English (four women
and four men; age 2434 years, mean 29.7 years)
observers saw the colours of the continuum shifted
to make them more green (green-shift). A further
different 12 observers from the same places and
with the same gender distribution (French: age 23
29 years, mean 26.5 years; English: age 2432
years, mean 28.3 years) saw the continuum shifted
to make the colours more blue (blue-shift). The
French observers were studying in Luminy, Marseille, France and the English were students at
Goldsmiths, University of London, UK. All were
screened for redgreen colour vision abnormalities
with the Ishihara colour vision test (Ishihara, 2001)
and paid for taking part in the experiment.
For Himba observers, there were three groups
of 12 participants. The first group who conducted
the study with their boundary in the middle of the
range (MH) contained four men and eight women,
varying in age from approximately 18 to 40 years,
mean 30 years. The second group (nine women
and three men; age range approximately 2060
years, mean 39 years) saw colours shifted towards
yellow (dumbu-shift). The third group (10 women
and two men; age range approximately 1745
years, mean 27.6 years) saw colours shifted
towards blue (buru-shift). All observers were
screened for colour vision abnormalities along
the redgreen confusion axis with the City Colour
Vision Test (Fletcher, 1980)*a test that does not
require number identification*and paid with
small quantities of maize flour.
Stimuli. All experiments used Munsell (1905)
colours at Value (Brightness) 5 and Chroma
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6
DAVIDOFF ET AL.
(Saturation) 6 with 2.5 Munsell hue units between
each adjacent colour. The MW group of Western
observers in Fagot et al. (2006) saw 12 colours
going from 2.5G to 10B centred around the
known boundary for green and blue (between
5BG and 7.5BG; Roberson et al., 1999). For the
shift condition, 11 stimuli were used to make only
one stimulus in the median position. The 11
stimuli used for the green-shift continuum for
Western observers were 10GY to 5B; i.e., the
range shifted 5 Munsell steps in a greenwards
direction. The 11 stimuli for the blue-shift continuum were 7.5G to 2.5PB; i.e., the range shifted
5 Munsell steps in a bluewards direction. The
colours 10G, 2.5BG, 5BG, 7.5BG, 10BG, and 2.5B
were common to all groups. Colours for the
Western observers were generated on a computer
screen using colour notation software (Roberson
& Davidoff, 2000) and were calibrated with a
CS100 colour gun so as to assure correct colour
appearance. Colour stimuli subtended 6.48 by 6.48
visual angle.
The Himba boundary between dumbu and
buru is between 7.5GY and 10GY (Roberson
et al., 2004). For the Himba, the MH group saw 12
Munsell colours with 11 equally spaced steps
going from 5Y to 2.5BG. The 11 stimuli used for
the dumbu-shifted continuum were 2.5Y to 7.5G;
the 11 stimuli for the buru-shifted continuum
were 10Y to 5BG. The colours 2.5GY, 5GY,
7.5GY, 10GY, 2.5G, and 5G were common to all
three groups. Stimuli were 1-inch square glossy
finish chips that were mounted on 2-inch square
pieces of white card.
Apparatus. Western observers were tested in a
darkened room facing an analogue joystick and a
14-inch colour monitor driven by a Pentium 4
personal computer (see Fagot et al., 2006). The
monitor and joystick were placed on a table so
that viewing distance remained approximately
equal to 49 cm. Control and randomisation of
conditions were achieved through purpose-made
programs written in E-Prime V 1.0 (Psychology
Software Tools, Inc.). The Himba observers were
tested manually under the shade of a tree and
colours were placed into a solar-powered lightbox
giving Illuminant C under which the Munsell
samples are standardised. The decision to use
the lightbox was not based on a concern that
computer screens could be too unfamiliar to the
Himba. They are also unfamiliar with printed
material but we have found, in examining their
shape perception, identical outcomes for material
when presented in that form or on a computer
screen (Davidoff, Fonteneau, & Goldstein, 2008;
De Fockert, Davidoff, Fagot, Parron, & Goldstein,
2007). Our reason for using the lightbox was that
colours are exactly reproduced; we could not
ensure that a computer screen maintained colour
calibration in the field. The two procedures
giveidentical outcomes for Western observers
(Roberson & Davidoff, 2000; Roberson et al.,
2000).
Procedure. To initiate the testing, Western
participants pressed the spacebar. A squareshaped sample stimulus then appeared with 4.58
of lateral eccentricity on the right or the left of the
screen followed 500 ms later by a cursor and two
patches of colour for comparison with the sample.
In balanced order, one colour square appeared on
the top, and the other on the bottom half of the
screen. Observers indicated which target was
more similar to the sample with a joystick. There
was no time limit for responding. Response
choices and latencies were recorded.
For the MW group, the training procedure used
only the 2.5G and 10B stimuli as sample and
target stimuli; the green-shift group used only
10GY and 5B and the blue-shift group 7.5G and
2.5PB. Training sessions for all groups was as in
Fagot et al. (2006) for the MW group. Sessions
were composed of 96 randomly ordered identity
matching trials, resulting from a completely
balanced stimulus identity by stimulus position
design. Observers were required to reach a
criterion level of 80% correct in training trials
before being tested; they were reinforced at this
stage receiving yes (oui)/no (non) feedback.
Experimental test sessions were composed of
identity and similarity MTS trials. In the identity
trials, the target was identical to one of the sample
stimuli. For the no-shift (MW) group (see Fagot
et al., 2006), in the similarity matching trials, the
target was randomly chosen from the 10 intermediary colours, going from 5G to 7.5B. For all
trials, the colour-comparison stimuli were 2.5G
and 10B. All possible combinations of stimulus
position were given equally often in a random
order. The two stimuli used on identity matching
trials were each shown 15 times, and the 10 stimuli
used on similarity matching trials were each
shown once, for each combination of sample
and comparison stimulus position, resulting in a
total of 160 trials per block. The proportion of
identity and similarity matching trials (3:1) was
chosen so as to maintain attention during the test.
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COLOUR CATEGORISATION
Ten test blocks were required for each observer
who was allowed breaks on every occasion after
completion of the first three test sessions. A
similar procedure was adopted for the green-shift
and blue-shift groups except that the target was
randomly chosen from the nine intermediate
colours, going from 2.5G to 2.5B (green-shift)
and 10G to 10B (blue-shift).
A similar but manually controlled procedure
was carried out for Himba observers. The endpoints of the chosen range (MH [no-shift],
dumbu-shift, or buru-shift) were used as training stimuli. They were shown individually, one
at a time in random order and observers were
asked in Himba to look at the colour carefully.
For training, the colour was then removed and,
with an interval of a couple of seconds, followed
by the administration of the choice comprised
of the two endpoints (e.g., 5Y and 2.5BG). The
card was presented twice with one endpoint
uppermost and twice with the other endpoint
uppermost, in random order. The observer was
then asked, by an interpreter, which colour he/
she had seen. The observer was trained until he/
she got all the practice trials right four times
consecutively.
For the MH (no-shift) range, the test phase of
the MTS experiment used the 10 Munsell colours
(7.5Y to 10 BG) intermediate between the endpoints of the range. A shortened procedure was
carried out with the Himba in order to maximise
data collection from observers who might not be
able to return for testing. The test phase in the
MH and shift MTS tasks each consisted of 200
trials with every colour presented 20 times as a
target. The sample choices were now present
at the same time as the targets and placed on a
card that could be reversed to keep the colour of
the top and bottom sample randomised. A similar
procedure was adopted for the test trials in the
shift conditions. The instructions to the Himba
were to match the test stimulus to the target that
was more similar; this instruction induced no
strategic bias, i.e., it did not emphasise categorical
or perceptual similarity judgements.
After the MTS, both Himba and Western
observers were asked to name the colours presented during the experiment. Western observers
were asked to name the colours individually
shown on the screen in random order; this was
repeated five times. Himba observers named the
colours once in random order in the lightbox.
7
Results of MTS
Training
For Western observers, one session sufficed for
them to learn the MTS rule. The Himba observers
needed at most 16 identity matching trials to
reach criterion for the test.
Modelling the data
Following the same rationale as Fagot et al.
(2006), we modelled the data to determine the
most likely strategy used by the participants in the
MTS task. We fitted the two-parameter (human)
decision bound (DB) model from Fagot et al. to
the similarity matching data for each observer.
The DB model proposes that the observer sets a
linear boundary at a particular value (k) within
the range of the perceptual response values
created by the probe stimuli. For each probe trial,
the matching response to training stimuli (e.g.,
2.5G [Green] or 10B [Blue] for Western observers) is determined according to whether the
perceptual response value of the probe falls
above or below k. In the no-shift conditions,
10 probe stimuli were used and thus the value
of the boundary parameter can be represented by
a single value in the range 110; the nine probe
stimuli for the green-shift (dumbu-shift) and blueshift (buru-shift) conditions are represented by
the range 08 and 311, respectively. This boundary is employed for all probe stimuli but its
application is susceptible to noise (s2). The noise
parameter jointly accounts for both the error
associated with the placement (or application)
of the decision boundary and also the perceptual
representation of the probe values, across trials.
The two-parameter (k and s2) DB model was fit,
for each observer individually, to the probability
of making a particular matching response (i.e.,
‘‘green’’ or ‘‘dumbu’’ for Western and Himba
observers, respectively) for each of the probe
stimulus types (nine or 10 for the shift and noshift conditions, respectively). Maximum likelihood estimates of the two parameters were found
using a constrained iterative routine in MATLAB
(see Fagot et al., 2006). The fit of the twoparameter model was compared with a perfect
saturated model using Likelihood Ratio (LR) test
statistics (for a review of this procedure see
Maddox & Ashby, 1993). This saturated model
used nine or 10 parameters (the observed probabilities of ‘‘green’’ or ‘‘dumbu’’ responses for
8
DAVIDOFF ET AL.
20
No Shift (Mw)
Green Shift
Blue Shift
18
14
12
10
8
6
4
2
2.5PB
10B (11)
7.5B (10)
5B (9)
2.5B (8)
10BG (7)
7.5BG (6)
5BG (5)
2.5BG (4)
10G (3)
7.5G (2)
5G (1)
2.5G (0)
0
10GY
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Mean Number of times "Green" sample chosen
(Max=20)
16
Munsell Colour (k value)
Figure 1. Mean number of times ‘‘green’’ sample chosen (91 SE) for Western observers in the MTS task for no-shift (MW), greenshift, and blue-shift continua. The data for the no-shift group are taken from Fagot et al. (2006). Stimuli for all continua are Munsell
colours separated by 2.5 Munsell hue steps. The scale and range of the k parameter, 011, indexes the 12 equally spaced Munsell
colours used in the no-shift condition. The boundary between green and blue is normally found to be between 5BG and 7.5BG
(Roberson et al., 1999).
each of the probe stimulus types) for the shift and
no-shift conditions, respectively, and was (trivially) able to predict the observed probabilities
perfectly. When the LR test statistic is nonsignificant (i.e., below the 95th percentile of the x2
distribution, with degrees of freedom equal to the
difference in the numbers of parameters between
the two models, i.e., df7 or df 8 as appropriate), then the simpler two-parameter model is
not significantly worse than the perfect model,
and it can be deemed an extremely good fit.
The two-parameter DB model was fitted to
each individual’s data and yielded an excellent fit
in most cases. For 14 of the 20 Western and
Himba observers in the no-shift condition, the
two-parameter model did not yield a significantly poorer fit than the saturated model (LR
test values ranged from 3.81 to 14.1; 8 df;
.08 B pB.87). For the remaining six observers,
the deterioration of fit from the saturated model
was moderate (LR test values ranged from 17.33
to 21.95; 8 df; .005 Bp B.027). For the 24 Western
and 24 Himba observers in the shift conditions,
the two-parameter model yielded a significantly
poorer fit than the saturated model in only six
cases (LR test values ranged from 0.92 to 13.67; 7
df; .057 Bp B.996). For these six observers, the
deterioration of fit from the saturated model was
moderate (LR test values ranged from 14.73 to
19.06; 7 df; .008 Bp B.040). There was no significant difference in mean value of the noise
parameter across the shift conditions for either
Western observers, F(2, 29) 1.96, p.15,
h2p .12, or for the Himba, F(2, 33) B1, h2p .05.
Range effects
We examined the effect of shifting the range of
the probe stimuli on the modelled decision
boundary (k) parameter separately for the
greenblue and dumbuburu ranges.
Greenblue continuum. For Western observers,
the mean value of the boundary estimate (k) was
entered into a three level (group: no-shift [MW]
vs. green-shift vs. blue-shift) ANOVA and revealed a significant main effect of group,
F(2, 29) 51.28, p B.001, h2p .78; see Figure 1.
Planned contrasts showed that, relative to the noshift group, the mean estimated boundary values
for the green-shift (3.01, SD 0.77) and blue-shift
9
(3.01) was, in fact, lower than 3.65, t(11) 2.85,
pB.05, d 1.72 in a direction away from the
existing colour boundary (see Figure 1).
To examine whether the shift conditions resulted in a corresponding latency difference, the
six colours common to the three continua used for
Westerners were submitted to analysis. Latencies
of the green-shift and blue-shift data were prorated to average performance levels of the noshift (MW) group and are shown in Figure 2. A 3
(group: no-shift [MW] vs. green-shift vs. blueshift)6 (common probe colour: 10G, 2.5BG,
5BG, 7.5BG, 10BG, and 2.5B) ANOVA was
carried out on log latencies. The analysis revealed
group differences, F(2, 29) 7.32, p B.005,
h2p .336 in the context of a main effect of
common probe colour, F(5, 145) 4.86, p B.005,
h2p .144, and an interaction, F(10, 145) 20.63,
pB.0001, h2p .587. It is clear from Figure 2 that
the three groups have their peak latencies at
different colours (i.e., the middle of each range).When the continuum was shifted greenwards,
peak latencies were found to correspond to a shift
in that direction (p B.0001); when the continuum
was shifted bluewards, peak latencies were then
found to have shifted in that direction (pB.002).
(6.50, SD 1.05) groups were significantly
shifted, respectively, greenwards, t(29) 5.53,
p B.001, d 2.03, or bluewards, t(29) 3.47,
p B.004, d 1.29; both contrasts Bonferroni corrected for two comparisons.
In Figure 1 it can be seen that the boundaries
for all three groups were placed near the middle
of the ranges. For the no-shift condition, the
middle of the range coincided with the existing
colour boundary between green and blue. Thus,
perhaps not surprisingly, the observed value of
k did not differ from the predicted value of 5.5.
The mean value of the k parameter for the Western (MW) no-shift group was 5.15 (4.496.05,
SD 0.56) and gave t(7) 1.76, p .05, d 1.33.
The midpoints between the two training stimuli
used in the two shift conditions represented a shift
of 1.5 (green-shift) and 1.5 (blue-shift) units
(along the k scale) from the midpoint of the noshift condition. Based upon the observed midpoint of the no-shift group (5.15) one would
expect a mean k parameter values of 3.65 and
6.65 for the green-shift and blue-shift conditions,
respectively, if decisions were based on the
middle of the range. The mean of the blue-shift
group (6.50) was not significantly below 6.65,
t(11) B1. The mean of the green-shift group
3.1
No Shift (Mw)
Green Shift
Blue Shift
3.05
3
Mean log Latency (secs)
2.95
2.9
2.85
2.8
2.75
2.5PB
10B(11)
7.5B(10)
5B(9)
2.5B(8)
10BG(7)
7.5BG(6)
5BG(5)
2.5BG(4)
10G(3)
7.5G(2)
5G(1)
2.5G(0)
2.7
10GY
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COLOUR CATEGORISATION
Munsell Colour (k value)
Figure 2. Mean log latencies (91 SE) for Western observers in the no-shift (MW), green-shift, and blue-shift groups for all probe
colours used in the MTS task.
DAVIDOFF ET AL.
3.64 and 6.64, t(11) 2.03, p .05, d1.22, and
t(11) 1.96, p .05, d 1.81, respectively.
Dumbuburu continuum. For Himba observers,
the mean value of the boundary estimate (k) was
entered into a three-level (group: no-shift [MH]
vs. dumbu-shift vs. buru-shift) ANOVA and again
revealed a significant main effect of group (see
Figure 3) on the estimated decision boundary,
F(2, 33) 13.04, p B.001, h2p .44. Relative to the
no-shift group, the mean estimated boundary
value for the dumbu-shift group (4.13,
SD 0.83) was significantly shifted towards the
dumbu end of the colour continuum, t(33) 2.77,
pB.02, d 0.96. The degree of shift in the
boundary estimate towards the buru end of the
colour continuum in the buru-shift group (5.99,
SD 1.16) was also in the predicted direction,
t(33) 2.33, p B.06, d0.81, both contrasts Bonferroni corrected for two comparisons. Again, not
surprisingly, the mean value of the k parameter
for the Himba (MH) no-shift group (M 5.14,
range 4.036.03, SD 0.59) did not differ from
the middle of the range, t(11) 2.12, p.05,
d1.28. More critical, based upon this observed
midpoint of the no-shift group, the mean values of
the k parameter for the dumbu-shift group (4.13)
and buru-shift group (5.99) were not significantly
different from their respective range midpoints of
Mean Number of times "Dumbu" sample chosen (Max=20)
Discussion of MTS
On each MTS trial, the structure of task was to
present three stimuli of which two were from the
same colour category just as it is in tasks that
produce CP. Yet the MTS produces judgements
made on perceptual rather than categorical similarity. The two sets of human observers could
hardly be more different in terms of their literacy
and culture, but in a striking fashion they both
showed judgements based on perceptual similarity (binary division) in the MTS. Both Westerners
and Himba made relatively sharp equal divisions
in all ranges irrespective of the position of their
colour boundary. Observers did not know at the
outset of the test the exemplars for which judgements were required, yet, with relatively few trials
and with only one minor exception, both groups
apportioned the exemplars equally to the two
sample stimuli. Furthermore, the resulting middle
of the range boundary overrode a category
boundary that in other circumstances has
been found to be robust and well-established
No Shift (MH)
Dumbu Shift
Buru Shift
20
18
16
14
12
10
8
6
4
2
5BG
2.5BG (11)
10G (10)
7.5G (9)
5G (8)
2.5G (7)
10GY(6)
7.5GY(5)
5GY (4)
2.5GY(3)
10Y (2)
7.5Y (1)
5Y (0)
0
2.5Y
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10
Munsell Colour (k value)
Figure 3. Mean number of times ‘‘dumbu’’ sample chosen (91 SE) for Himba observers in the MTS task for no-shift (MH),
dumbu-shift, and buru-shift continua. The Himba boundary between dumbu and buru from colour naming was found to be between
7.5GY and 10GY (Roberson et al., 2004).
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COLOUR CATEGORISATION
11
example of each colour used in the respective
MTS tasks and were not restricted in their
possible colour naming response. To aid comparison across shift conditions, we represented the
colour naming boundaries according to the k
parameter used for the modelling. Thus, the
midpoints of both the Western and Himba colour
probe ranges, i.e., the established colour boundaries (greenblue: 5BG7.5BG, and dumbuburu:
7.5GY10GY, respectively), are represented by a
value of 5.5k across all shift conditions. In Table 1,
we give the naming boundaries for the Westerners
for the three ranges used in greenblue and, for
the Himba, the three ranges used for dumbu
buru. It is clear that the range made only the
most marginal difference to the names given to
the colours. For Westerners, there was no hint
of any significant difference between the colour
naming boundaries across the three groups,
F(2, 29) B1, h2p .057. The mean boundary value
of each group was close to a k-value of 5.5 (see
Table 1) and all lay within the usual boundary
range of greenblue; i.e., between 5 BG (5k) and
7.5BG (6k).
Colour naming is more variable in the Himba
(Roberson et al., 2004) with some observers
preferring to use cattle terms for the colours. On
that basis, eight observers were excluded from
analysis (one from the no-shift, four from the
dumbu-shift, and three from the buru-shift
groups). The resulting analysis gave no significant
difference between the colour naming boundaries
across the three groups, F(2, 25) B1, h2p .060.
The mean boundary values of the dumbu-shift
and buru-shift groups were both close to the
midpoint 5.5. While not significantly different
from the two shift conditions, the mean colour
(Bornstein & Korda, 1984; Roberson et al., 1999).
Admittedly, we tested the phenomenon with a
restricted range of colours, but, even so, the
effects were strong. The critical difference in the
MTS procedure could be that the observer is
presented with two constant target colours and a
third (sample) colour that takes many positions in
between. Some of those sample colours were very
similar to one of the targets and this may be the
critical factor that encourages the use of perceptual similarity.
It might seem obvious that, in the absence of
other biases, observers would make equal divisions of the continuum, but it is worth commenting upon. For example, it might explain why
colour categories are of roughly equal size (see
also the similar prediction in Komarova et al.,
2007) and have the extra benefit of being the most
efficient procedure for adding new colour terms
to a speaker’s language (Jameson, 2005). In a
more general context, the tendency to make
divisions in perceptual continua could also be
the basis of dichotomies seen in so much of
human cognition. Indeed, for some social theorists, dichotomies are at the core of human thought
(e.g., Lévi-Strauss, 1969) and even been argued to
be hard-wired into the brain (see Rose, 2005).
Results of naming. We examined the naming
data to see if range changes also affected the
names given to the colours. For Westerners, a
naming boundary was found for each observer by
determining the first point along the greenblue
colour continuum at which the number of green
responses dropped below chance levels (50%).
The same general procedure was applied to the
Himba but they were presented with only one
TABLE 1
The mean k-value (SD) and Munsell colour equivalent for the boundary found from colour naming
No-shift (MW)
Westerners green
vs. blue
Green-shift
k-value
Munsell colour
k-value
Munsell colour
k-value
Munsell colour
5.75 (1.39)
6.88BG
5.5 (0.60)
6.25BG
5.17 (0.98)
5.43BG
No-shift (MH)
k-value
Himba dumbu
vs. buru
Blue-shift
4.86 (0.92)
Munsell colour
4.62BG
Dumbu-shift
k-value
5.38 (1.36)
Munsell colour
5.95BG
Buru-shift
k-value
5.61 (1.76)
Munsell colour
6.53BG
For all ranges, a value for k of 5.5 represents the middle of the range. For Westerners, colour naming was obtained from the noshift (MW), green-shift, and blue-shift continua; for the Himba it was obtained from the no-shift (MH), dumbu-shift, and buru-shift
continua. No boundaries are reliably different from k5.5.
12
DAVIDOFF ET AL.
naming boundary for the no-shift group was
somewhat below 5.5.
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Discussion
In summary, when asked to name the colours, the
observers assign the colours in a way consistent
with categorical similarity. In MTS, observers
assign colours consistent with perceptual similarity. It is perhaps remarkable given the claim by
some authors of automatic colour naming as an
explanation of colour CP (Munnich & Landau,
2003) that observers do not turn to naming in the
MTS but clearly they do not. In the next two
experiments we explore the generality of our
results. In Experiment 2, we examined the effects
of the inclusion of a focal colour to rule out a
potential artefact in the MTS paradigm.
EXPERIMENT 2: COLOUR MTS WITH A
RANGE INCLUDING THE FOCAL
COLOUR
In Experiment 1, we assumed that the observer
was obeying the instruction to assess the similarity
of the sample colour to the targets. However, the
results of Experiment 1 could reflect neither a
perceptual nor a cognitive judgement but merely
a response bias to make equal responses to each
sample in MTS. We therefore wish to rule out this
alternative in Experiment 2. Evidence from MTS
tasks with other domains (Kuhl, 1991) suggests
that the division of stimuli might not always be
equal where there is the inclusion of focal
examples. Indeed, one might have explained the
one case of an unequal division in Experiment 1
in this way as the shift condition now included a
colour close to focal green.
Focal colours have a psychological importance.
They are earlier and more reliably established
than boundary colours (Mervis, Catlin, & Rosch,
1975), better recognised in short-term memory
(Rosch Heider, 1972), more effective in pairedassociate learning (Rosch Heider, 1972), and are
more efficiently activated by the colour word
(Rosch, 1975). It is uncertain whether Western
focal colours have a universal importance as
argued by Kay and Regier (2006) or whether
each language forms its own categories around its
own best examples (foci) of colours given the
same name (Roberson et al., 2000; Roberson,
Davies et al., 2005). However, irrespective of
the origin of focal colours, it seems important to
ask whether the equal divisions of the colour
ranges found in Experiment 1 would be maintained if a focal colour were included in the
similarity judgements.
In Experiment 2, we therefore repeated the
MTS experiment with a range of colours that
would all normally be called green but included
focal green (Berlin & Kay, 1969) positioned
towards one end of the range. A dominant view
(Kuhl, 1991) is that a typical stimulus (prototype)
as an endpoint will act like a magnet and draw
stimuli towards it thus causing the boundary to be
drawn further from the prototype. An alternative
view is that atypical stimuli have larger attractive
fields with the opposite prediction about boundary position (Tanaka, Giles, Kremen, & Simon,
1998).
If judgements of similarity were more likely to
be made to the focal (typical) sample (Angeli
et al., 2008; Kuhl, 1991) or to the atypical sample
(Tanaka et al., 1998), then clearly the midpoint of
the MTS judgement would be altered by the
inclusion of the focal colour. An asymmetric
response would also show that the results of
Experiment 1 reflected a perceptual judgement
and were not merely a response bias to make
equal responses to each sample.
Method
Participants. Twelve observers (seven women
and five men; age 1835 years, mean 23.30 years)
were recruited at Goldsmiths, University of London, UK. All had normal colour vision for the
red-green confusion axis (Ishihara, 2001) and
were paid for participating in the study.
Stimuli. The stimuli were derived from Munsell
colours and calibrated as in Experiment 1 but
with Value (Brightness) 2.5 and Chroma (Saturation) 6 and a linear spacing of 1.25 Munsell hue
units between each adjacent colour. Darker
stimuli were chosen to include an example of
focal green and smaller Munsell separation to
ensure all colours would be called green. Eleven
stimuli were generated ranging from 7.5GY to
10G and subtending 6.48 by 6.48 visual angle when
presented on a computer screen. The aim was to
use a range of colours including a focal colour
towards one end of the range. Therefore, we
COLOUR CATEGORISATION
100
13
3.0
Percentage 7.5GY Responses
90
3.0
2.7
20
2.6
10
2.6
0
2.5
3.75G
Mean Log Latency (ms)
30
10G
2.7
8.75G
40
7.5G
2.8
6.25G
50
5G
2.8
(midpoint
of range)
60
2.5G
2.9
1.25G
70
10GY
(focal green)
2.9
8.75GY
80
7.5GY
Percentage 7.5GY Responses
Mean Log Latency (ms)
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Munsell Colour
Figure 4. Primary y axis: Percentage of 7.5GY responses (91 SE) in the MTS task with a range including focal green. Secondary y
axis: mean log latencies (91 SE) for the MTS task with a range including focal green. Stimuli are plotted on the x-axis, and are
Munsell colours separated by 1.25 Munsell hue steps. The focal colour (10GY; Davidoff et al., 1999) and the midpoint of the range
(3.75G) are indicated on the x-axis label. All colours in the range would normally be called ‘‘green’’ (Roberson et al., 1999).
included focal green, which is at approximately
10GY (Davidoff et al., 1999) in our range of
greens. All colours in Experiment 2 would be
normally called green (Roberson et al., 1999).
Procedure. The procedure was identical to
Experiment 1 except for the use of a different
colour range and, instead of using a joystick,
observers simply pressed one of two buttons
arranged vertically to correspond with a cursor
movement of ‘‘up’’ or ‘‘down’’. The training
procedure used only the two endpoint colours of
the range 7.5GY and 10G as the sample and
target stimuli. For the similarity trials, the target
was one of the nine intermediary colours from
8.75GY to 8.75G. For all trials, the sample stimuli
were 7.5GY and 10G. The task was again to assess
which of the two sample colours was more similar
to the target.
Results
Again, all participants had learnt the MTS rule by
one session. We examined the effect of the range
of the probe stimuli on the modelled decision
boundary (k) parameter. The average value of k
was found to be at 2.19 G (SE 0.16). A onesample t-test indicated that the boundary was
statistically significantly different from the midpoint of the range (3.75G), t(11) 9.57, pB.001
(see Figure 4), shifted greenwards (i.e., towards
the end of the continuum containing focal green
at 10GY). Using log transformed latency data, a
one-factor ANOVA on target colour (11 levels)
indicated that there was a significant main effect
of target colour, F(10, 121) 7.97, p B.001.
Planned simple contrasts revealed that latencies
for 1.25G were not significantly longer than for
2.5G (p .1) but they were significantly longer
than for all the other colours (all ps5.01; see
Figure 4). These latencies reflect the choice data
where the average boundary was at 2.19G (i.e.,
between 1.25G and 2.5G).
Discussion
In Experiment 2, we found that the categorical
decision did not give a boundary at the middle of
the range. The asymmetric response indicates that
the results of Experiment 1 were not merely a
response bias to make equal responses to each
sample. The boundary in the MTS was drawn
closer to the focal colour and this would seem
counter to the argument in Kuhl (1991) that
similarity judgements were more likely to be
made to the typical (here focal) sample. It is
worthwhile noting the similar outcome of the
inclusion of a focal colour when observers are
asked for an explicit colour (naming) categorisation (Hampton, Estes, & Simmons, 2005). In their
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14
DAVIDOFF ET AL.
study, Hampton et al. (2005) found that observers
would more likely categorise a borderline blue
purple colour as purple if it were presented
alongside a typical blue. In effect, the category
boundary in their study was shifted towards the
focus as here in Experiment 2. Hampton et al.
interpret their data as showing that contrast is an
important determiner of (colour) categorisation
in simultaneous comparisons (see also Stewart &
Brown, 2005). In their categorisation tasks, the
observers in Hampton et al. had to decide
whether a colour matched a name. Their observers’ borderline judgements were considerably
affected by the presence of a focal colour by
making it less likely that borderline colours were
given the same name as the focal colour.
It is not our primary concern to investigate the
role of typical stimuli in the allocation of similarity judgements. Our primary concern is to examine tasks where the range may or may not
override existing colour boundaries and hence
produce judgements based on perceptual similarity. However, one implication that could be drawn
from Hampton et al. (2005) is that even colour
similarity assessed by naming might depend on
the range of stimuli. One of the aims of Experiment 3 was to examine whether that is the case. In
Experiment 3, we considered a colour CP task;
such tasks have been claimed to show an automatic activation of colour categories (Fonteneau
& Davidoff, 2007) and so might be immune from
range effects.3
EXPERIMENT 3: VISUAL SEARCH WITH
VARYING COLOUR RANGES
We used a visual search paradigm successfully
employed in several recent studies of colour CP
(Daoutis, Franklin, Riddett, Clifford, & Davies,
2006; Daoutis, Pilling, & Davies, 2006; Drivonikou
et al., 2007; Franklin et al., 2008; Gilbert et al.,
2006). These studies have found that search times
were quicker for an odd-one-out colour embedded within colours of a different category
(cross-category targets) than embedded within
3
Motivation for Experiment 3 came in part from a talk
given by Dr. Oliver Wright at the Progress in Colour Studies
2008 conference held in Glasgow, UK (Wright, 2011). He
noted that the midpoint of the range of blue/green colours
used in the visual search study of Gilbert et al. (2006) did not
correspond precisely to the usual boundary given for that
colour range.
colours from the same category (within-category
targets). If these effects were in some part due to a
division of the range of colours at the midpoint,
then some sort of categorical division*perhaps
even CP*should be obtained at the midpoint of
any range of colours. Experiment 3 contrasted a
range of colours with the midpoint at the green
blue boundary with another range of colours
where the midpoint was green-shifted to the
same extent as in Experiment 1. For the greenshift range, we compared a group who had prior
exposure of the range to another that had no prior
exposure. We included the former group in order
to maximise the green-shift participants’ exposure
to the green-shifted stimulus range, and therefore
the possibility of a range effect.
Method
Participants. The observers were tested on the
greenblue (no-shift) set or the green-shift set.
There were 14 observers (10 women, four men,
age 1938 years, mean 23.86 years) in the green
blue condition,14 observers (six women, eight
men, age 2030 years, mean 24.07 years) in the
green-shift condition with prior exposure to the
range of stimuli to be used in the visual search
task, and 14 observers (12 women, two men, age
1935 years, mean 23.93 years) without prior
exposure. All were recruited at Goldsmiths, University of London, had normal colour vision for
the redgreen confusion axis (Ishihara, 2001) and
were paid for participating in the study.
Stimuli. There were two sets of stimuli. The first
(greenblue set; see top half of Figure 5) contained both blue and green patches centred at the
greenblue boundary (6.25BG; Roberson et al.,
1999), and therefore used the same midpoint as
the MW range of Experiment 1. The second set
(green-shift; see bottom half of Figure 5) was
shifted five Munsell steps towards the green end
of the continuum, with a midpoint now at 1.25BG.
All stimulus presentations contained 12 colour
patches arranged as in a clock face but offset so
that no colours appeared on the vertical meridian;
11 of the colours were identical and one was an
odd-one-out. For both sets there were four types
of odd-one-out stimulus but always separated
from the identical stimuli by five Munsell steps.
For both sets of stimuli, two of the types were
drawn from the middle of the range (‘‘middle’’;
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COLOUR CATEGORISATION
15
Figure 5. The two sets of stimuli used in Experiment 3. The top half shows the Munsell values for the four pairs of stimuli used in
the green-blue (no shift) condition. The bottom half shows the Munsell values for the four pairs of stimuli used in the green shift
conditions. In each half, the upper section shows the two ‘‘end’’ pairs and the lower section the two ‘‘middle’’ pairs.
see lower pairs in each half of Figure 5) and the
other two were drawn from the ends of the range
(‘‘end’’; see upper pairs in each half of Figure 5)
For the greenblue (no-shift) set, the middle
types would normally be regarded as crosscategory; they used 2.5BG as the odd-one-out in
7.5BG (or vice versa) and likewise for 5BG and
10BG. The end types would normally be regarded
as within-category; they used 8.75G as the oddone-out in 3.75BG (or vice versa) and likewise for
8.75BG and 3.75B. The green-shifted stimulus set
were equivalent to the greenblue set but shifted
by five Munsell steps. The two middle types were
(7.5G2.5BG) and (10G5BG) and the two end
types were (3.75G8.75G) and (3.75BG8.75BG).
One may note that the end type colours 3.75BG
8.75BG in the green-shift range could be regarded
as cross-category given the standard allocation of
the greenblue boundary.
Apparatus. This was as for the Western observers in Experiment 1 except that the joystick
was replaced by a button box to record responses.
Procedure. Each observer completed three
tests: the main visual search task and two naming
tasks covering (1) just their task range of colours,
and (2) the entire range of colours across both the
no-shift and green-shift stimuli. The no-shift
group first completed the visual search task,
followed by the naming of their task range and
finally the naming of the entire stimulus range.
One set of green-shift observers carried out the
tasks in the same order. However, in order to
maximise the green-shift participants’ exposure to
the green-shifted stimulus range, and therefore
the possibility of a range effect, the other set of
green-shift observers were told that they would
see a range of colours that would cover all the
colours that might be used in the visual search
task. They were then asked to name their task
range which was then followed by the visual
search task and finally the naming of the entire
stimulus range.
Based on Gilbert et al. (2006, Exp. 1), each
trial of the visual search task began with a fixation
display for 1000 ms, followed by the stimulus
display consisting of 12 squares of colours. Only
one of these squares (target) was a different
colour from the others (distractors), and observers were asked to identify this odd-one-out and
respond to whether it was on the left or right side
of the screen by pressing the corresponding
button of a response box. In Gilbert et al., there
was an interest in the hemispheric locus of colour
categories; however, as that was not our concern,
we did not present stimuli at a rapid speed to
prevent eye movements but the display remained
on screen until a response was made. Following
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16
DAVIDOFF ET AL.
the response, a blank screen was displayed for 250
ms before the next trial.
Each stimulus colour served as both target and
distracter. Each target was presented equally
often at each of the 12 locations, and the order
of location and target stimulus was randomised.
Thus, each block was made up of 96 trials and
participants completed three blocks. Response
times and accuracies were collected. A practice
block of 12 trials using purple and orange stimuli
ensured observers understood the task.
For naming, the observers responded by pressing buttons labelled ‘‘blue’’ or ‘‘green’’. Each
colour was presented individually in a random
order on the computer screen in a block of trials.
Five blocks were given for the range used in their
visual search task. Observers who saw the green
blue (no-shift) set were presented with 17 colours
ranging from 8G to 4B in steps of one Munsell
unit; the green-shift participants saw 17 colours
ranging from 3G to 9BG. For the entire range of
colours, participants were asked to make similar
responses to 24 colours at one Munsell step from
1G to 4B. Again, these colours were presented at
random in five blocks.
the 3.75BG8.75BG colours without (p.63) or
with (p.62) prior exposure to the range.
Accuracy. The accuracies on both stimulus sets
were generally high and were therefore inverse
sine transformed prior to analysis. A similar
mixed ANOVA to that conducted on latencies
again indicated (see Figure 6, bottom) that effects
of categorical similarity are only found with the
no-shift set. There was a significant main effect of
category, F(1, 39) 7.31, p B.01, an interaction,
F(2, 39) 10.44, p B.001, but no effect of stimulus set, FB1.
Tukey HSD tests on the difference between
middle and end performance indicated (see
Figure 6, bottom) that the middle-end comparison for the no-shift was significantly different to
both the no exposure green-shift (p B.005) and
prior exposure green shift (pB.001) that did not
differ from each other (p .7). Again, Tukey
HSD tests of the pair types used in the greenshift conditions revealed no sign whatsoever of an
advantage for the 3.75BG8.75BG colours without (p .34) or with (p.27) prior exposure to
the range.
Naming
Results
Visual search
Latency. For the visual search, response times
were log transformed prior to analysis and
responses over 2 s were excluded. They were
then entered into a 2 (category: middle vs.
end)3 (stimulus set: greenblue [no-shift] vs.
prior exposure green-shift vs. no prior exposure
green-shift) mixed ANOVA with the withinparticipants factor of category, and the betweenparticipants factor of stimulus set. Figure 6 (top)
shows that effects of categorical similarity are
only found in the no-shift set. There were
significant main effects of category, F(1, 39) 24.96, pB.001, and interaction, F(2, 39) 9.29,
pB.001, but no effect of stimulus set, F(1,39) B 1.
Tukey HSD tests on the difference between
middle and end response times indicated that
this was significantly different for the no-shift set
compared to both the prior exposure green-shift
(p B.01) and the no prior exposure green shift
(p B.01) that did not differ from each other
(p .1). We note that Tukey HSD tests of the
pair types used in the green-shift conditions
revealed no sign whatsoever of an advantage for
Range covering observer’s stimulus set. We
examined where observers placed their green
blue naming boundary in the range of stimuli on
which they had been tested in the visual search
task. The naming boundary was found for each
observer as in Experiment 1. The location of the
boundary for each observer with respect to the
midpoint of their range was calculated in Munsell
units. On average, for the greenblue (no-shift)
observers, the naming boundary was at 6.50BG
(middle of range 6.25BG). For the no prior
exposure green-shift participants, it was at
1.71BG, and for the prior exposure green-shift
at 1.36BG (middle of range 1.25BG) (see
Figure 7). One-sample t-tests indicated that in
all conditions (no-shift, p.6, no prior exposure
green-shift, p.15, and prior exposure green
shift, p.7) the boundary was not significantly
different from the midpoint of the range.
Large range. In this naming task, all participants were tested on a large range of stimuli that
included colours close to focal examples of both
green and blue; the middle of the range was
2.5BG. The average naming boundary of this
large range was at 5.07BG for the greenblue
(no-shift) participants, 3.71BG for the no
17
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COLOUR CATEGORISATION
Figure 6. Top: Mean accuracies (91 SE) in each category condition for the visual search task in the no-shift (green-blue) and two
green-shift groups. For the no-shift condition, ‘‘middle’’ would correspond to a cross-category pair and ‘‘end’’ to a within-category
pair. Bottom: Mean latencies (91 SE) in each category condition in the visual search task for the no-shift (green-blue) and two
green-shift groups. For the no-shift (green-blue) condition, ‘‘middle’’ corresponded to a cross-category pair and ‘‘end’’ to a withincategory pair.
exposure green-shift participants, and 3.14BG for
the prior exposure green-shift participants, (see
Figure 8).
One-sample t-tests indicated that in the noshift condition, t(13) 4.66, p B.001, and no prior
exposure green-shift condition, t(13) 2.97,
p B.02, the boundaries were significantly different
from the midpoint of the range; however, the
prior exposure green-shift condition escaped significance, t(13) 1.80, p B.1. Tukey tests indicated that the no-shift boundary was not different
from the no prior exposure green-shift, p.05,
although it was to the prior exposure condition
(pB.05). The prior and no prior exposure greenshift boundaries did not differ (p.6).
The boundary of the no-shift condition did not
differ from the assumed greenblue boundary at
6.25BG, t(13) 2.14, p .05. However, although
not reliably different from the no-shift condition
boundary, the no exposure green-shift boundary
when separately tested against the assumed
greenblue boundary was found to be different
to 6.25BG, t(13) 6.21, p B.01, as was the prior
exposure green-shift boundary, t(13) 8.70, pB.001.
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18
DAVIDOFF ET AL.
Figure 7. Percentage of participants’ ‘‘green’’ responses (91 SE) in the naming task for the range covering only the participants’
visual search stimulus set for the no-shift (green-blue) and two green-shift groups.
Discussion
Experiment 3 showed two important different
outcomes to Experiment 1. The first difference
was that, with the visual search CP paradigm, we
found colour similarity based on categorical similarity. However, CP was found only for the green
blue set. The other set did not show CP whether or
not participants had prior exposure to the range.
An important difference between the paradigms
used in Experiments 1 and 3 is that there is an
explicit demand for a similarity match in MTS,
whereas in the odd-one-out task the category
judgement is implicit. Consideration of these issues
concerning when colour category boundaries are
immune from range effects will be reserved for the
General Discussion. The second and surprising
difference is that there are now ranges of stimuli
for which naming does not conform to what might
be expected from existing colour boundaries.
In Experiment 1, naming could be seen as a
reliable measure of categorical similarity by being
Figure 8. Percentage of participants’ ‘‘green’’ responses (91 SE) in the naming task for the entire range of stimuli used in the two
visual search conditions (no-shift [greenblue] and green-shift) for the no-shift (greenblue) and two green-shift groups. Dashed
lines indicate the midpoint of the range at 2.5BG and 50% level of response.
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COLOUR CATEGORISATION
immune from range effects; in varying degrees
this was not the case in Experiment 3. Whereas
for the no-shift condition, there was little difference to the names given to the colours, prior
exposure to the shift range produced a reliable
change in naming for the large range. Also, and
more dramatically, the naming changed with a
restricted range. When both green-shift groups
named the more restricted colour range used in
their visual search task, their naming boundary
was now clearly shifted to the middle of the range.
The range of colours used for the large range in
Experiment 3 was not as large as that in Experiment 1, but still contained good examples of
green and blue colours; this would appear necessary, though not sufficient, to use the greenblue
boundary as the naming boundary. It was not
sufficient because the prior exposure condition of
Experiment 3 showed that a bias could be
introduced that would affect the position of the
greenblue boundary even if good examples were
given in the naming task. It is also clear that given
a restricted range without clear examples (i.e.,
close to prototype) of both blue and green,
naming like MTS will tend to divide the stimuli
into two equal groups.
GENERAL DISCUSSION
Colour boundaries are assumed to be stable, and
hence one might assume that categorical similarity would be used for all tasks where colours need
to be distinguished. Our main finding is that,
despite the efficiency gained by allocating items
to a category (see Smith & Medin, 1981, for
discussion of this normal advantage of having
acquired categories), categorical similarity is not
used in a large number of conditions. We note
here a similar conclusion in the auditory domain
for phoneme categorisation (Gerrits & Schouten,
2004). It was only in the visual search task
(Experiment 3) where we found that performance
was based on existing colour boundaries.
What then is the mechanism underlying CP?
We propose that the default position in processing
colour similarity relies on a neuronal network that
automatically associates language areas and visual
cortex (Siok et al., 2009) with the corollary that
colour similarity will be categorical. The default
judgements about colour are a result of an underlying representation that has a symbolic structure
but with the important phenomenal consequence
that colours from different categories look more
19
different than would be predicted from our ability
to discriminate between them. Thus, we provide an
explanation for the everyday observation that
rainbows look striped even though the colour
change is continuous. The default position would
apply to most judgements concerning colour that
implicitly ask for an assessment of colour similarity. We note what might be a connected proposal
made recently by Filoteo, Lauritzen, and Maddox
(2010) for category learning. Filoteo et al. surprisingly found that it was in implicit, rather than
explicit, category learning that there was an
increased use of optimal strategies.
An alternative proposal for the origin of CP
(Roberson et al., 2009) would account for our
data by predicting a conflict between verbal and
perceptual codes. In their proposal, both verbal
and perceptual codes are automatically activated
and come into conflict only for within-category
trials when the verbal code gives a ‘‘same’’
response for the colours but the perceptual code
responds that they are ‘‘different’’. The Roberson
et al. (2009) account may have the added benefit
of explaining why the assumed cross-category pair
in Experiment 3 produced no advantage for the
green-shift groups. If observers are inclined,
through their recent experience, to call/see both
stimuli of the cross-category pair as ‘‘green’’, then
that pair would produce a similar response to all
the other pairs. However, the conflict between
automatic activation of both verbal and perceptual codes would not predict our MTS data.
We now turn to consider conditions where the
categorical judgement of colours in implicit tasks
can be disrupted (cf. the disruption of the automatic task of reading; Stolz & Besner, 1999). One
condition is the addition of a concurrent language
task (Gilbert et al., 2006; Roberson & Davidoff,
2000; Winawer et al., 2007). Indeed, in these
interference studies, observers only have to remember a single colour or detect a single oddone-out colour; so, the categorical similarity
procedure must be easy to disrupt. What the
present paper shows is that there are procedures
in explicit tasks, and ones that are not obviously
demanding of language resources, that can also
prevent the use of colour categories. Our data for
MTS certainly support that conclusion as presumably it is sufficient to promote perceptual
similarity by making the observer carry out some
near identity similarity judgements. The deactivation of the default colour category networks in an
explicit colour naming task might seem more
surprising. It would be reasonable to believe that
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20
DAVIDOFF ET AL.
being asked to name a colour patch green or blue
would inevitably activate the existing colour
boundary rather than do the exact opposite as it
did for the green-shift group of Experiment 3.
Instead, the data from Experiments 1 and 3 suggest
that the requirement for the use of colour boundaries in colour naming is the presence of good
examples of the colour in the samples to be named.
Once categorical similarity has been deactivated in MTS, responses will be affected by the
range (Experiment 1) and by the perceptual salience of a focal colour in the range (Experiment 2).
Thus, the types of cognitive processes activated are
those that one might expect to operate in any
categorisation judgement based on perceptual
similarity (for reviews see Hampton et al., 2005;
Murphy, 2002). In MTS, one in particular that
would appear relevant is the positioning of any
decision criterion in relation to the prevailing range
of stimuli by moving the criterion towards a running average of the range (Treisman & Williams,
1984). Nevertheless, one must ask why we should
want to be prevented access to categories already
mastered. Tanaka et al. (1998) addressed a similar
question of when a categorical or noncategorical
strategy is used in a face MTS task. Referring to
work on speech sounds, Tanaka et al. argued that
categorical judgements are used only when there is
an automatic use of category structure (Kuhl,
1991). With respect to CP, our data largely support
that argument.
In summary, several processes operate to make
a colour similarity judgement based on perceptual
similarity rather than the default categorical
similarity. For language impaired patients, as
Goldstein (1948) noted, perceptual similarity is
all that is available to make colour decisions. In
consequence, the outcomes of their colour sorting
are bizarre. Normal observers may also use
perceptual similarity if that is what instructions
demand (Kay & Kempton, 1984). However,
normal observers may apply other constraints to
override perceptual judgements. One obvious
constraint is to use colour names. Nevertheless,
in MTS, even with good examples of green and
blue as samples, colour names are not applied by
observers in our studies. Instead, what is brought
to bear is a decision criterion based on perceptual
similarity with the important consequence of
categories based on a binary division.
Original manuscript received August 2011
Revised manuscript received June 2012
First published online July 2012
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