Colour Categorisation Without Verbal Labelssapience.dec.ens.fr/cogmaster/www/doc/MEMOIRES/2017_CHABANI_Emma.pdf · which was created by Katarzyna Siuda-Krzywicka). Dr Paolo Bartolomeo

ENS, PARIS V, EHESS

MASTER’S THESIS

Colour Categorisation Without VerbalLabels

Author:Emma CHABANI

Supervisors:Dr. Paolo BARTOLOMEO

Katarzyna

SIUDA-KRZYWICKA

A master’s thesis for the degree of Cogmaster

at the

PIC NIC lab

Institut du Cerveau et de la Moelle Epinière

June 6, 2017

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Declaration of Originality

I, Emma CHABANI, declare that this thesis titled, “Colour Categorisation Without VerbalLabels” and the work presented in it are my own. I confirm that:

• This work was done wholly or mainly while in candidature for a research degree atthis University.

• I have acknowledged all main sources of help.

• Where this thesis contains work done by myself jointly with others, I will make clearexactly what was done by others and what I have contributed myself..

What makes this work original is to bring a new method to resolve the current de-bate about the origin of colour categorisation as either coming from language or beingderived from innate perceptual categories. In the following paper, we propose a way todisentangle naming and categorisation using a colour categorisation task based on thecomparison of two pairs of colours from same and different colour categories. If the taskcan be done efficiently in both healthy subjects with normal but perturbed naming abili-ties (due to verbal interferences compared to non verbal one) and patients with impairednaming abilities, this could present strong evidence to support the idea that verbal labelsare not necessary for colour categorisation.

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Declaration of Contribution

Dr Paolo Bartolomeo, Katarzyna Siuda-Krzywicka and myself identified and clarifiedthe specific research question of this work as well as share relevant literature essentialto understand better the question research. Dr Paolo Bartolomeo and Katarzyna Siuda-Krzywicka, Christoph Witzel and their previous Master’s student Myriam Taga designedthe colour matching name experiment (2.1) used for our protocol. Dr Paolo Bartolomeo,Katarzyna Siuda-Krzywicka, and myself with the precious insight of Christoph Witzel,chose the methodology , design of the task, and interferences. Christoph Witzel created alarge set of stimuli from which Katarzyna Siuda-Krzywicka and myself selected ones thatwe used. I programmed the Pre Pilot study, Experiment 1, Experiment 2, and the Casestudy myself. Dr Paolo Bartolomeo and Katarzyna Siuda-Krzywicka as well as Dr. PaoloBartolomeo’s post-doc Tal Seidel, gave me critical feedback about how to improve the de-sign throughout the process. I recruited all the 34 subjects, and organised and took careof the testing process, including data collection. Katarzyna Siuda-Krzywicka and myselfcarried out the analysis together. Dr Paolo Bartolomeo, Katarzyna Siuda-Krzywicka andmyself derived the theoretical interpretations and I personally wrote the manuscript andcreated all figures and materiel (except for the explicative figure of the patient lesion, RDS,which was created by Katarzyna Siuda-Krzywicka). Dr Paolo Bartolomeo and KatarzynaSiuda-Krzywicka provided me with much feedback on this master thesis, including the-oretical notes and analysis notes, and I am very grateful for all of their contributions.

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ENS, Paris V, EHESS

AbstractDépartement des sciences cognitives

Institut du Cerveau et de la Moelle Epinière

Cogmaster

Colour Categorisation Without Verbal Labels

by Emma CHABANI

Colours, which are a continuous perceptual attribute, are gathered together by humansinto discrete categories associated with verbal labels. But do we need colour labels togroups hues together? The role of language in categorisation is part of an ongoing de-bate. Two opposing views can be found in the literature about the origin of colour cat-egories. According to some theorists, colour categories are the result of verbal colournaming. On the other hand, evidence is accumulating in favour of the hypothesis thatcolour terms might simply map onto perceptual colour categories. Nowadays, there is nosimple method for testing colour categorisation without explicit colour names. Thereforeit is difficult to disentangle the two underlying mechanisms. In this project we proposea novel approach to verify if colour categorisation can occur without the need of verballabels. We developed a colour categorisation task to be used in healthy subjects (withor without interfering with verbal function), as well as in a patient with left occipito-temporal damage resulting in colour anomia (RDS). In healthy participant, verbal andnonverbal interferences affected the accuracy in colour categorisation to the same extend,suggesting that the process might not rely on verbal labelling. Furthermore, We observeda dissociation between naming and categorisation performance in RDS, giving strong ev-idence that our task can disentangle colour categorisation from colour naming. Thus, wedemonstrated that verbal colour labelling is not necessary for categorisation of coloursin patient RDS, where a damaged ventral cortical visual stream in the left hemisphereimpaired colour naming but not colour categorisation. This finding suggests that colournaming and colour categorisation are separate cognitive functions, using different neuralmechanisms.

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Acknowledgements

First of all, I would like to thank Professor Bartolomeo and Kasia Siuda-Krzywickafor including me in the PICNIC lab team for my Master’s internship. They have not onlyguided me throughout my research project but have taught me numerous valuable skillsand vastly improved my knowledge. I would like to thanks Christoph Witzel for hisprecious knowledge in the field of colour perception, the creation of stimuli, and for hisfeedback on stimulus selection. I would also like to thank Professor Laurent Cohen andfor his invaluable input and collaboration on this project. Next, I would like to thank theentire PICNIC lab and especially Tal Seidel, for being so welcoming, as well as for theirvalued insight whenever I needed help. The atmosphere in this lab was kind and incredi-bly supportive. Lab meetings were always interesting and pleasant and the feedback thatthey gave me was very helpful and served great purpose. I would also like to thank thepatient, RDS, for his cooperative and enthusiastic participation in the study. I wish to ex-press my gratitude to everyone who participated in my experiments, especially friends.Finally, I would like to thank my classmates of Cogmaster program who went throughthis journey by my side and who helped me through the joyful and stressful moments ofthis degree. Lastly I would like to thank my parents and sister who have supported me ineverything that I have undertaken since I was born in the building in front of ICM, withspecial thanks to my father who participated to the study.

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Contents

Abstract 7

Acknowledgements 9

1 Introduction 11.1 The origin of colour categorisation - an ongoing debate . . . . . . . . . . . . 11.2 Aims of study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Colour categorisation on healthy and abnormal brain 72.1 Colour name matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2 Pre-pilot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.3 Experiment 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.4 Experiment 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.5 Case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3 General discussion and conclusion 253.1 General discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

A Figures annexed 27

Bibliography 47

1

1 Introduction

Colours, a continuous perceptual at-tribute, are gathered together by humansinto discrete colour categories associatedwith verbal labels. But do we needcolours label to groups hues together?The role of language in categorisation ispart of an ongoing debate. Two opposingviews can be found in the literature aboutthe origin of colour categories. Accord-ing to some theorists, colour categoriesare the result of verbal labels. An alter-native hypothesis states that colour termsmight simply map on pre-existing percep-tual colour categories. This manuscriptwill begin by briefly review the ongoingdebate about the bases of colour categori-sation in order to introduce our work.

1.1 The origin of colour

categorisation - an on-

going debate

Linguistics basis of categorical percep-tion of colour For over half a centurylanguage has been described as affectingperception and thought, and is used tosegment nature. (Carroll, 1956) Indeed,the human ability to create discrete cat-egories for continuous space raises com-plex problems that appear to be solved

only by looking at language. This is welldescribed by Davidoff (2001) in the fol-lowing quote :

Take two colors that areperceptually indistinguishable(say the two colours 630 nmand 629.9 nm that we wouldcall red). Suppose the 629.9nm color has its wavelengthaltered to 629.8 nm. As thiscolor is perceptually indistin-guishable from the previous, itwould also have to be calledred. There is soon a paradoxbecause, if we continue withthe procedure, eventually wewill have reached the oppo-site end of the spectrum andhave to agree that these (blue)colors should also be calledred. Thus, in order to resolvethe paradox, we need someadditional and non-perceptualmechanism such as colour la-bels to mark a distinction be-tween two colours.

Moreover, as we can see with the cat-egorical colour perception phenomenon,categories modify perception. This phe-nomenon results in human being faster

2 Chapter 1. Introduction

and more accurate when discriminatingtwo colours from different categories thantwo colours from the same category, evenwith equal perceptual colour distances(ie. chromatic separation). (Winaweret al., 2007; Bornstein and Korda, 1984;Holmes et al., 2009; Daoutis, Pilling, andDavies, 2006). Ensue from the follow-ing two assumptions; that (i) only a non-perceptual mechanism such as languagecan result in discrete categories and that,(ii) categories modify perception, onecould argue that language modifies per-ception. This is actually a well supportedview, as argued by Thierry et al. (2009)who said that "It is now established that na-tive language affects one’s perception of theworld". These authors demonstrated an"implicit effect of language-specific terminol-ogy on human color perception". It is there-fore of crucial importance to understandwhether language and categorisation relyon the same mechanisms. Numerousstudies have focused on the role of lan-guage in colour perception. This was firstdone in behavioural studies. Gilbert et al.(2006) showed that in colour discrimina-tion tasks, response times for colours fromdifferent categories are faster for stimulithat are presented in the right visual field,than for left-presented stimuli. They con-cluded that "people view the right (but notthe left) half of their visual world throughthe lens of their native language", and thatcolour categorisation is indeed a linguisticphenomenon. Evidence from neuroimag-ing studies confirmed these behaviouralfindings (Siok et al., 2009; Tan et al., 2008;

Thierry et al., 2009). For example, Tan etal. (2008) showed activation of the brainlanguage areas when participants judgewhether two colours were same or dif-ferent. Also, Siok et al. (2009) demon-strated that colour discrimination fromdifferent lexical categories in the right vi-sual field engaged left-sided language ar-eas, when compared with colour discrim-ination from the same lexical category inthe right visual field. Franklin et al. (2008)demonstrated that colour perception islateralized to the right hemisphere in in-fants, with a shift to the left hemisphereoccurring upon learning the words thatdistinguish the relevant category bound-ary. Kwok et al. (2011) showed that learn-ing new colour names leads to an increaseof grey matter in the left hemisphere V2-V3.If colours categories are indeed relying onlanguage, they should differ across cul-tures. Evidence supporting this claim wasfound by Winawer et al. (2007). Theyfound that Russian and English partici-pants perceive hues pertaining to the bluecategory differently, and that this effectcan be disrupted by verbal interference.Thus, there is much evidence to suggestthat verbal interference negatively inter-feres with colour categorisation, and cat-egories are subject to cross-cultural dif-ferences. Davidoff (2001) concluded thattheir "own cross-cultural research, backed upby neuropsychological data and interferencestudies, indicates that perceptual categoriesare derived from the words in the speaker’slanguage. The new data support a rather

1.1. The origin of colour categorisation - an ongoing debate 3

strong version of the Whorfian view that per-ceptual categories are organized by the lin-guistic systems of our mind."

Challenges to the position of linguisticrelativism Other studies have reacheddifferent conclusions. For example,Witzel and Gegenfurtner (2011) found alack of lateralization of psychophysical ef-fects, which "appeared to the same extent inboth visual fields." In another study, mod-ified perception by langage was put intoperspective by Forder et al. (2014) andHe et al. (2014) whoes showed that" thatcolor categories affect post-perceptual process-ing, but do not affect the perceptual represen-tation of color". Also, according to Witzeland Gegenfurtner (2013), categorical per-ception only occurs for colours at the cen-tre of categories. They concluded that"These findings seriously challenge the ideathat color naming forms the basis for the cat-egorical perception of colors. With respectto previous studies that concentrated on thegreen-blue boundary, our results highlight theimportance of controlling perceptual distancesand examining the full set of categories wheninvestigating category effects on color percep-tion." Altogether, these studies challengethe assumptions that (i) colour categoriesare linguistic, (ii) colour categories influ-ence perceptual representation of colours.

Perceptual colour categorisation aspreceding verbal label Alternatively,

colour terms might simply map on pre-existing perceptual colour categories, in-dependent of verbal labelling. Consis-tent with this hypothesis, it is possibleto extract coherent structures of colourcategories across cultures. (Lindsey andBrown, 2009). This is mainly shown forfocal colours (ie. the shade of a givencolour category that represents the bestexample of this category) as describedby Regier, Kay, and Cook (2005). Af-ter examining colour names in over 100cultures, they concluded that "(i) best-example choices for color terms in these lan-guages cluster near the prototypes for Englishwhite, black, red, green, yellow, and blue, and(ii) best-example choices cluster more tightlyacross languages than do the centres of cat-egory extensions, suggesting that universalbest examples (foci) may be the source of uni-versal tendencies in color naming. Addi-tionally, Roberson et al. (2004) exploredchildren’s category acquisition, and re-ported that children from a culture withonly 5 colour terms showed similar pat-terns of term acquisition as English chil-dren. Interestingly, they observed thatchildren "knowing no colour terms maderecognition errors based on perceptual dis-tance, and the influence of naming on mem-ory increased with age. An initial percep-tually driven colour continuum appears tobe progressively organized into sets appropri-ate to each culture and language". The exis-tence of such a universal structure impliesthat humans share similar organisationfor colour categories, independent of cul-tural factors. These conclusions would be

4 Chapter 1. Introduction

consistent with a strong biological basis ofcolour categorisation, with a perceptualbasis. Supporting evidence for this claimcomes fromYang et al. (2016)’s study us-ing NIRS in prelinguistic infants, whichdemonstrated the following : “We mea-sured the neural correlates of categorical colorperception in prelinguistic infants. We foundincreased brain activities to colors in differ-ent categories, but not to colors in the samecategory. These results indicated that differ-ent color categories are differently representedin the visual cortex of prelinguistic infants,which implies that color categories may de-velop in the visual system before languageacquisition."

1.2 Aims of study

Research question The relationship be-tween colour categorisation and verbalcolour naming is a debate that is hardto resolve as no simple task exists thatwould allow for measuring colour cate-gorisation without explicit colour names.To disentangle colour categorisation andcolour naming we aimed to create acolour categorisation task that removedthe need to use explicit verbal colournames. Also, studies which discardcolour categorisation as a non verbal pro-cess in using verbal and non verbal inter-ferences did not test the effect of those in-terferences on an explicit colour namingtask. Indeed, they argue that as colour

categorisation was impaired by verbal in-terference it was consequently a linguis-tic process. (Thierry et al., 2009; David-off, 2001) But they did not compare thisresult to a pure naming task to see to whatextent verbal interference would have im-paired colour naming. To be sure that ver-bal naming is not required to perform thistask, we will compare the effect of verbaland non verbal interferences on the per-formance of healthy subjects on our taskto their performance in a explicit namingtask. We will use a colour-word namematching task we cannot explicitly ask toname colours due to our use of verbal au-ditory suppression interference.Also, a patient, RDS, with colour anomiaafter a left occipitotemporal and splenialischemic brain lesion, appears to show adissociation between categorisation andcolour naming. RDS’s stroke deprived theleft occipito-temporal lobe from direct vi-sual input, which consequently impairedcolour naming. However, sometimes RDSis able to use compensatory strategies torecover colour names. For example, whenpresented with a navy blue patch he said:: "this is the colour of the sky, the sky is bluetherefore this is blue". Navy blue and skyblue are not similar but come from thesame colour category (blue). Thus, RDS’sstrategy seems to rely on colour categori-sation. He appears to still be able to cat-egorise colours despite impaired colournaming. Comparison of his performancewith that of age- and education-matchedcontrols will allow us to test the hypoth-esis that colour naming is not required

1.2. Aims of study 5

to categorise colours, and to provide ev-idence for a dissociation for colour cate-gorisation and naming in this patient.

Hypothesis and predictions If colourcategorisation does not require colournaming, then verbal interference shouldnot impact the performance in a colourcategorisation task more than a non ver-bal interference task of similar difficulty.Moreover, RDS should present a disso-ciation between his performance on thecolour categorisation and colour namingtasks. Therefore, if RDS is able to performthe current colour categorisation task butis unable to perform as well as controlson the naming task, we can concludethat colour naming is not necessary forsuccessful judgement of categorical rela-tionships between colours. On the otherhand, verbal interference should have anegative impact on colour naming. This

impact should be more substantial thanthe influence of non verbal interference.Interaction of interferences and task typecould therefore show that colour categori-sation does not necessarily rely on nam-ing. Here we propose a colour categori-sation task which requires comparison ofthe categorical relationship of 2 pairs ofcolours, one lying within a category andthe other one crossing category bound-aries, while perceptual distances betweencolours in each pair do not significantlydiffer. If verbal labels are necessary forcolour categorisation, then verbal inter-ference should substantially impair taskperformance. If, on the other hand, verbalinterference has no more effect on colourcategorisation than non verbal interfer-ences, then, we can conclude that verballabelling is not essential for colour cate-gorisation.

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2 Colour categorisation on healthy andabnormal brain

The aim of this work is to disentanglethe process of colour categorisation fromthat of colour naming. In order to ad-dress this, we designed a task in whichsubjects have to categorize colours with-out the explicit need of colour labels. Firstof all we had to select hues that peoplecould label with the same colour namewith high consistency, both internally andbetween testing sessions (i.e. intra andintersubject similarity between name cho-sen for the same hue) (2.1). Then, an in-vestigation of the effects of verbal or non-verbal interference on this task was per-formed on healthy subjects. A colour-name matching task was also run with thesame interferences to look for a possibleinteraction between the task and interfer-ence type (either no interference, nonver-bal and verbal) on performance (2.3 and2.4). In addition, we investigated pos-sible dissociations in performance in ourpatient RDS, with left occipitotemporalbrain damage and colour anomia (2.5).

2.1 Colour name matching

The aim of this task was to confirm thatthe colour probes that we used were in-deed reliably assigned to given colour cat-egories.

Method

Participants Seventeen Frenchspeakers, 12 women and 6 men (aged21-40 years = 25,125±4.40) with normalor corrected-to-normal visual acuity andnormal colour vision tested with IshihiaraColor Blindness test performed the task.

Materials We built a set of 320 probecolours. Hues were chosen along aline drawn in the CIELUV colour spacebetween two prototypical from elevencolours categories i.e. black, blue, brown,green, grey, orange, pink, purple, red, yel-low and white. CIELUV stands for CIEL*u*v* (International Commission on Illu-mination L*u*v*) colour space which aimsto define an encoding with uniformityin the perceptibility of colour differences(from now on abbreviated CIELUV). Theprototypes were chosen so that all colours

8 Chapter 2. Colour categorisation on healthy and abnormal brain

along the line connecting them lay in themonitor gamut (i.e. can be shown on themonitor). Colours were sampled with∆ELUV steps of 5 (i.e. The euclidean dis-tance between two colours in the CIELUVspace is equal to 5) along the line.(see Fig-ure 2.1 A).13 sets of colour probes between twocolours were created. Figure 2.1 B. showsan example from one set of colour probeslying between a prototypical red and aprototypical blue.We presented probes of colours as300x300 pixels patches, and presentedthem on a CRT Monitor (resolution1024x758 pixels, viewing distance, 60 cm).The experiments were controlled by Psy-chtoolbox.

Figure 2.1.A. Illustration of the process of creation ofcolour probes. Hues were chosen alonga line drawn in the CIELUV colour spacebetween prototypical blue and prototypical

red.B. Part of naming data.

Each bar corresponds to a colour probe. Thecolour of each bar is that of the colour la-bel chosen most often for the probes repre-sented by this bar (i.e. more than 50% of an-swers). Stars point out high consistency (i.e.hue named for more than 90% of trials as a

given colour)

Procedure Patches containing huepairs were visually presented at the centreof the screen on a grey background. Eachof the 320 probe colours were presented3 times. The total number of trials was960. On each trial, subjects had to matcha colour label (black, blue, brown, green,grey, orange, pink, purple, red, yellow orwhite) with the patch presented. To do so,they had to press a key corresponding toone of the eleven colour names in French.The colour names were alphabetically or-dered as presented in Figure A.2. Time toanswer was not limited and the next trial

2.2. Pre-pilot 9

appeared after a given colour name waschosen.

Results An overview of the results ofthis task can be seen in Figure A.1. Ineach of the colour continuum, we identi-fies hues that were labelled with a givencolour name with at least 90% consistency(corresponding to stars in naming data,example on Figures 2.1B.) Interestingly,there were no hues judged as red morethan 90% of the time, as shown in thenaming data about red hereunder. Thismeans that there was not red hues thatparticipants would agree on to say "that’sred"

Figure 2.2.Naming data for red. All red bars are underthe red line (standing for high consistency inassigning red name to hues, i.e. 90% of trials)

Moreover, only three probes were re-liably assigned to the colours black andthree to white as shown in Figure 2.3 be-low.

Figure 2.3.Naming data for black and white. 3 blackbars and 3 white ones are above the red line

(90% consistency).

Summary The experiment 1 resulted ina set of colour patches reliably named bythe participants. We used them to createstimuli for the colour categorisation task.

2.2 Pre-pilot

The stimuli used in the task that wedesigned are pairs of colours pre-sented inside circles, separated horizon-tally in the middle as in Figure 2.4.

Figure 2.4.Task design

Pairs are eitherfrom the samecategory (within-category stimuli,presented in thebottom part of Fig-ure 2.4) or fromdifferent categories(cross-boundarystimuli, on the up-per part).The aim of the pre-pilot was to test thesestimuli. We wanted to make sure that


people were able to complete the task,and also to check whether our stimuliwere ambiguous. Indeed, even if we hadcontrolled for the fact that the colours weused were predominantly judged as be-longing to a given category in 2.1, pre-senting a colour next to another couldchange a person’s judgement of this par-ticular colour. Categorisation in our taskis performed in two ways : either by look-ing for the pair of colours which are fromthe same category or the pair that arefrom different colour categories. The la-bel "same" was assigned to the followinginstruction: "Find pairs of colours that be-long to the same category"; and the label"different" was assigned to the following :"Find pairs of colours that belong to dif-ferent categories". One could expect thatbecause the definition of colour categoriesis subject to linguistic variation, lookingfor cross-boundary stimuli would be lessbased on perceptual strategy than look-ing for within-category one. (Regier, Kay,and Cook, 2005) In this pilot experimentwe also wanted to verify if instructionscould trigger either linguistic or percep-tual strategies, and to explore possibledifferences in performance and subjectiveexperience.

Methods

Participants Eleven native French (5females, mean 25 years ± 3.09, range 22-31) with normal or corrected-to-normalvisual acuity and normal colour visionperformed the task.

Materials Stimuli were presentedvertically aligned. This disposition wasused to match the task performed by con-trols to the task performed by our pa-tient RDS, who has right homonymoushemianopia. Bi-bipartite circles were pre-sented on the same screen as the colourname matching experiment (section 2.1).Responses were entered by keyboardpresses. Experiments were con- trolledby the Matlab utility Psychtoolbox.

Within Stimuli CreationColour pairs were built with the mainconstraint of being as perceptually distantas possible within the same category. Anillustration of the process is shown in theFigure 2.5.

2.2. Pre-pilot 11

Figure 2.5. Creation of Within StimuliFigure 2.5 A. shows a bar plot containinga subsection of the naming data - the datafrom colour patches going from blue (0) tored (52). Stimuli created here must containa pair of colours from the same category (inthis case, pink). Therefore, the most distantpatches that were predominantly judged aspink were chosen to create our stimulus.Patch 33 and patch 42 (pointed by red ar-rows) are the best candidate for the withinpink stimulus. Distance in terms of CIELUVcolour space between this two colours is 45(9 probe hues between them times ∆ELUV =

5).

Cross Stimuli CreationFor cross-category stimuli, we choosecolours as perceptually similar as possiblein CIELUV spaces.

Figure 2.6. Creation of Cross StimuliHere, 7 and 16 (13 was too ambiguous) arethe best candidates. Theses two colours have

a distance of 45 in the CIELUV space

Distances

To be informative about the architec-ture of colour categories, the task shouldnot be able to be completed by usingpurely perceptual strategies. Conse-quently, euclidean CIELUV distances inwithin-category pairs) had to be equal toor lower than distances between cross-category colour pairs. Descriptive statis-tics of distances can be found in the FigureA.4. As required, within category meanperceptual distance was 42.24 ∆ELuv (±14.24) whereas cross-category mean dis-tance was 38.24 ∆ELuv (± 8.32). As testedwith a Two-sample Kolmogorov-Smirnovtest similarity of distribution of distancesamong trial for within-category stimuli


and cross-boundary stimuli was signifi-cant p=0.0317.

The final set of stimuli was composedof 11 cross and 19 within stimuli as pre-sented (see fig. A.5).

Procedure Participants saw twopairs of colours from either the same ordifferent categories. Six subjects had tochoose different category pair, five thesame category pair. Each subject receivedtraining with appropriate feedback. Thetraining used different stimuli from thosepresented in the experimental session.Once the task was understood (i.e. morethan 5 good answers in succession), par-ticipants passed 3 blocks of the task. Eachblock contained one of the possible 156pair combinations. Cross and withinstimuli containing the same category ofcolour were never presented together.The order of trials was randomised aswell as position of within and cross stim-uli on the screen and their directions. Wecontrolled for the fact that the same stim-uli could not be presented one after an-other. People had to press 8 (up arrow)to choose stimuli from the upper part ofthe screen and 2 (down arrow) to choosethe one from bottom part. Subjects wereasked to respond as quickly and as accu-rately as possible. For each trial, a fixationcross was presented at the centre of screenfor 500 ms. Afterwards, a pair of colourpairs was displayed until a response wasgiven.

Results

Data and Statistical analysis Twosubjects who had received the same re-sponse instructions had to be discardedfrom analysis, one because he did not un-derstand the task, the other for data cor-ruption. Thus, we could analyse datafrom 3 subjects with same response and 6participants with different response. Per-formance was analysed in terms of accu-racy (% correct responses) and responsetime. Responses were considered as cor-rect when same category pairs were cho-sen under the same instruction, or crosscategory pairs were chosen under differ-ent instruction. We analysed the meanresponse accuracy for each trial for eachinstruction - same (S) and different (D)-first separately. We merged the dataof two conditions afterwards to have anoverview of the categorisation of ourstimuli.

Accuracy and reaction time Partici-pants were generally accurate, with an av-erage of 81.1% correct responses (± 38.66)This rate is significantly different fromchance (binomial test p<.001, see FigureA.8 Table C.). The average response timewas 1.79ms ( ± 1.29). (See Figure A.8 A.)Accuracy and response time were signif-icantly inversely correlated (Spearman’srho = -0.539, p <.001,see Figure A.7), how-ever we did not find a speed/accuracytrade off: the less accurate participantswere, the faster they were, which can onlymean that some trials were harder than

2.2. Pre-pilot 13

others. One can see the distribution of re-sponse times and the binomial describedplot in Figure A.8 B. and C.

Couples of stimuli 21% of coupleswere not above chance level (p>0.05 in thebinomial test) as seen in table below:

Table 2.1.Table of Couples that are not significantly

above chance level

Some of the stimuli were ambiguousas show plot of accuracy for couples (seeFigure2.7). Participant categorise poorlyred/pink, Purple/pink. Also, withinstimuli as pink and purple were difficultto categorise for participants.

Figure 2.7.Accuracy matrix for all presented combina-tions. Dark blue indicates that this couplewas not presented because within and cross-category contained colours from the same

category

Distances There was no correlation(rho=0.026, p=0.726) of maximum per-ceptual distances with accuracy and re-sponse time for different instruction (seeA.9 A.). Maximal distances were com-puted from the widest value of distanceCIELUV colour space inside one trial (i.e.∆ELuv that is the largest between ∆ELuv

of within category pairs of colours andcross boundary pairs). We observed nocorrelation either for minimum distancesin a trial with reaction time in same in-structions (rho=0.022 p=0.722). (see Fig-ure A.9).

Discussion Our results suggest thathealthy participants were able to performthe task with high accuracy, but also thatsome of the stimuli had to be improved.The present findings also demonstratethat the task could not be performed ona purely perceptual basis. Indeed, therewas no correlation between perceptualdistances and participants’ accuracy. Be-cause there was no noticeable differencein the use of perceptual strategies in thesame or different instructions, for example,distances and performance should be cor-related. There was no noticeable differ-ence in the use of perceptual strategy insame or different instructions. In furthertesting we used the same instruction ongrounds of simplicity, and because cate-gorical judgement is better at the centreof category. Indeed, stimuli at the cen-tre category are closer to the prototypewhich make them easier to categorise.(Witzel, Maule, and Franklin, 2013) Also,


participants performed very accurately intrials where black and white were pre-sented. They reported to have learnedvery quickly that when the colours blackor white were presented they were partof a cross boundary stimuli. This effect isdue to the fact that white and black wereonly presented on cross category stimuli.As a reminder, we made this choice be-cause it was impossible to have withinblack and white stimuli with big distancesas only 3 probes for each were identifiedas respectively black and white. In fur-ther testing we chose to avoid this effectby having all of each of the eleven coloursin both groups of stimuli (cross as well aswithin).

Summary In experiment 1 we foundthat verbal and non verbal interferenceimpair colour categorisation in the sameextend. However interferences did notwork on colour-word matching task.Therefore are willing to improve interfer-ences and colour word matching task. Asurprising effect of correlation betweenthe perceptual distances between thecolour pairs and the colour-name match-ing performance, which was not presentin colour categorisation, highlights a dif-ferent strategy in both tasks, going towardour hypothesis that naming and categori-sation relies on two different mechanisms.

2.3 Experiment 1

In this experiment we used verbal a non-verbal interference to assess their impact

on our colour categorisation task and on acolour naming task. We explored possibleinteractions of interference effects (noninterference, verbal and nonverbal inter-ference) with tasks (colour categorisationand colour naming). If colour categori-sation depends on verbal labelling, thenverbal interference should impair perfor-mance on both tasks to a similar extent. Ifit is not the case, and verbal interferenceimpairs naming more than another simul-taneous task but has a different effect oncategorisation, one could conclude thatcolour naming is not necessary for colourcategorisation. Moreover, the non ver-bal task could underline the fact that ver-bal labelling is detrimental to fast colourcategorisation if it is affecting colour cat-egorisation performance (especially reac-tion time) more than verbal one.

Methods

Participants Twenty right-handed,native French speakers (ten females;mean age 28.53 ± 5.94 years, range 19-39), with normal or corrected-to-normalvisual acuity and normal colour vision,consented to participate.

Materials The monitor was the sameas in the pre-pilot task. We modified thestimuli in order to improve the ambigu-ous ones. We used a new method to selectwithin-category stimuli (see Figure 2.8).Also, we made sure to use all of the elevencategories both in within and cross groupof stimuli. To do so we created within

2.3. Experiment 1 15

black and within white with only a ∆ELuv

of five between the two colours that com-posed them. We also created a within redstimuli even if red is an ambiguous hue,rarely named as red when presented onthe monitor that we use. We used theseproblematic categories only to minimize(as much as possible) the effect of learn-ing describe above. We were willing todiscard the black and white categories inorder to analyse the data. We also equatenumber of stimuli in each group to con-trol for representation of each stimuli on157 trials. The new set of stimuli whichcan be seen in Figure A.6 resulted in 157combinations.

Figure 2.8.Creation of new within stimuli. Coloursfrom the same category were taken from dif-ferent sets of probes (as a reminder one set ofprobes correspond to one plot in A.1. In B.are shown samples of probes between pro-totypical orange and yellow (OY) as wellas prototypical yellow and green (YG) are

shown in the bottom of this figure.)

Our verbal interference task was an ar-ticulatory suppression task. Subject hearda sequence of rhythms in 4/4 time and oc-curring at the rate of 80 beats/min (i.e.,moderately fast). Each time they heard asound, they had to either press a pedalwith their non dominant foot (nonver-bal interference) or say ’ABC’ out loud(verbal interference) (Baldo et al., 2005).Sounds were presented through head-phones. We chose those interferences be-cause they require the same amount ofconstant engagement (tapping or saying’ABC’ aloud) and were proven to have asimilar level of difficulty. ()


Procedure Subjects started with atraining section for colour categorisation.First a presentation of the task was per-formed with the same support and wasscripted to avoid any differences in the in-structions given to each participant. Theywere instructed to look for colours fromthe same categories. We explained whatcolour categorisation is by showing a con-tinuous space of colour containing alleleven categories used in the experimentfrom which we have created discrete cate-gories. We then asked participants to citethe main colours categories while look-ing at this image. Thanks to this exer-cise we controlled for the fact that elevencolours were cited by each subject. If cat-egories were forget we remind them tosubject. This was done because it was im-portant that all participants had the samecategories in mind to be sure that theyperformed the task according to the cate-gories that we chose to work with. For ex-ample, in the pre-pilot we had to discardone subject (the one who did not under-stand the instructions) because he judgedblack, grey and white as the same cate-gories : the achromatic one. Therefore,to avoid subjective difference in what onecan call the ’main colour categories’, wechose to make them explicit for each par-ticipant. Then 3 couples of within andcross stimuli were presented with increas-ing levels of difficulty. We made this taskdifficult by showing within stimuli withcolours looking different from one an-other and cross stimuli with colours look-ing similar. Next, we wanted to ensure

that participants were familiar with theinterferences. They heard a metronomesound and and had time to practice the in-terfering tasks. First they had to try thepedal (on-line checking system in orderto allow us to verify that they press it inrhythm). Then, they tried to say ’ABC’after each sound. Participants were in-formed that difficulty to breath could oc-cur with this verbal interference and thatthey had to breath whilst saying ’ABC’rather than stopping speaking in orderto catch their breath. Subjects first beginwith a short training session where we en-sured that they were familiar with the ba-sic task with no interferences. We thenpresented the verbal and nonverbal inter-ferences to the subjects and ensured thatthey were familiar with these before be-ginning the experiment. They did at least5 trials of each condition (they could redo5 trials if it was not comfortable or if theydid not understand the instructions) on adifferent set of stimuli than the one pre-sented during the experiment. The orderof conditions for each block was counter-balanced. Then, subjects were interruptedfor a keyboard change and a presentationof the colour-word matching task whichwas also done both with and without ver-bal and nonverbal interferences in a coun-terbalanced order. The colour-to-wordmatching task (which served as a controltask for the explicit use of colour names)consisted in giving the name of the colourpresented in a within stimulus. It wassimilar to the colour categorisation task inthat it was a two alternative forced task


(participants had to choose between twostimuli vertically aligned), but subjectshad to choose a colour label in order tocomplete this task. Subjects had to choosebetween the eleven colours names that weare used in our protocol, glued onto a key-board see Figure A.2. Participants werefirst habituated to the keyboard by press-ing colours label names presented on thescreen for twenty trials. They then per-formed 5 trials to habituate them to themain task before performing 3 blocks ofcolour categorisation under counterbal-anced interference conditions.

Results

Data and statistical analyse Twosubjects had to be excluded. One did notunderstand instructions and was choos-ing bipartite circles containing coloursfrom different categories. Another oneperformed poorly and quickly, and whencompared to other participants his per-formance was out of the range. Thosesubjects performances can be seen plot-ted among all subject performance in Fig-ure A.10 For response time we decided totransform our data, as too long responsetimes were only showing that participantdid not perform the task correctly. In-deed, we judged that when subjects tookmore than 5 seconds to answer they wereeither not doing the task or involvingother cognitive reasoning that was not

required here. 157 trials were presentedin succession and it is likely that subjects’attention was not sustained throughoutthe entire task. As performance in eachtrial was averaged both intra and theninter subject we could not discard a pre-sentation of trial without losing the datafrom the three presentation of this trialfor one subject. Therefore, we chose totransform the response time up by 2 SDpoints around the mean. We discardedtrials (presentation of a given couple ofstimuli) which were different from chancelevel according to a binomial test. Therewere 3 trials out of 157 which were notsignificantly different from chance level(see table 2.2). Those three trials containswithin red stimuli which, as we excepted,where the most ambiguous.

Table 2.2.Couple not significantly above chance level

according to binomial test

Trials where within black and withinwhite were presented were also discardeddescribed above. Within white andblack stimuli were both made with colourprobes with a ∆ELuv of 5. We thus dis-carded 21 trials where those two stimuliwere presented. Finally, 24 trials were dis-carded which left us with with 133 trials


for analyses.

Performance on this task was analysedin terms of accuracy (% correct responses)and response time. Participants had anaverage of 86,90% correct responses (±33.75) in colour categorisation and a meanresponse time of M=1.504s (± 0.913) . Forword-colour matching, the mean scorewas 87.17% of correct responses (± 33.44)with a mean response time of 1.988s (±0.916). Analyses were performed usingJASP. We performed a Repeated measureANOVA with 2 factors. The first factor,tasks, had two levels : either colour cate-gorisation (CC) or word-colour matching(CN), and the second factor, interferences,had 3 levels; no interference (NVI), verbalinterference (VI) and non verbal interfer-ence (NVI). Assumption of sphericity wasmet (see Table A.1) therefore the variancewas homogeneous across different levelsof the factors.

For response time we found a maineffect of the task (F=33.431, p<.001), butno main effect of interference (F=0.603,p=0.354). No task and interference typeinteraction was found (see Figure A.12)For accuracy there was no main effectof task (F:0.674, p=0.044) but a main ef-fect of interference (F:3.603, p=0.354). Notask and interference type interaction wasfound (see Figure A.12)

As can be seen in the descriptive tablein A.12 and A.11 participants were fasterwith interferences but also less accurate.We therefore investigated for an eventual

speed accuracy trade off induced by inter-ferences that we did not find. However aninverse significant correlation was foundbetween accuracy and response time withall interference types for both colour cat-egorisation and colour word matching.(see Figure A.2).

We ran two separate ANOVAs, oneon categorization and one on namingwith only the interference factor, resultare shown in A.15 A and B. These anal-yses showed that interference affectedonly colour categorisation accuracy butnot colour word matching within sub-ject (F=0.435 p=0.651). When look-ing at colour categorisation interferencessignificantly reduced accuracy (F=5.739p=0.007) but not reaction time (see FigureA.15 A.).Also, we looked at correlation of min-imal distances between within-categoryand cross boundary stimuli for one trial(i.e the pair of colour which look themore similar). Also we looked at within-category stimuli distances and cross-boundary stimuli distances, with accu-racy and response time in trials. Therewas no correlation in colour categori-sation in either of the groups for ac-curacy (0.01<rho<0.1, 0.9>p>0.06) or re-sponse time (-0.001<rho<0.08, 0.9>p>0.2)(for mor detailed statistical test see Fig-ure A.13) However, there was significantcorrelation in colour-word matching taskbetween minimum distance with accu-racy (rho=0.25 p<0.03) and response time


(rho=-0.28 p<.001) and also between dis-tance of within-category stimuli with ac-curacy (rho=0.24 p=0.004) and responsetime (rho=-0.48 p<.001). Participants weretherefore faster and more accurate whendistances in stimuli was bigger (see Fig-ure A.13)

Discussion We observed that interfer-ence had an effect on colour cate-gorisation accuracy but not on colour-word matching task. Participants werefaster and less accurate with added in-terference, whether verbal or nonver-bal. However interference didn’t causea speed/accuracy trade-off. This first re-sult shown that we succeed to impair cat-egorisation and that verbal and non ver-bal interference impair in the same extentcolour categorisation. However, we wasnot able to impair naming with those in-terference. We chose to always do colour-word matching after categorisation toavoid triggering a naming strategy forcolour categorisation (i.e. naming coloursin patches in order to choose which onesare labelled as the same colour). We pro-pose that the inferences might become au-tomatic throughout the task and wouldtherefore have less of an impact on colour-word matching task. This indicates thatwe have to change our interferences in or-der to reduce the possibility of automa-tion as we cannot do naming task beforecategorisation. This could also be dueto the fact that colour naming is moreautomatic than categorisation and couldbe, as a process, resistant to interference.

As there is strong evidence in the litera-ture to suggest that verbal interferencesimpair performance on verbal tasks, wehope that this is not the case and that newinterferences will impair performance onboth tasks. There was no main effect oftask on accuracy but there was an effect oftask on response time. We are also willingto change the colour-word matching taskto remove differences in the mapping ofanswer between both tasks and to avoidthe need to categorise stimuli first to re-solve the colour-word matching task then(i.e. (i) find the stimuli to name thanksto categorical judgement (ii) match thecolour name with the category). Finally,we observed no correlation with distancesin colour categorisation, however surpris-ingly we did in colour naming. Peoplewere thus more efficient in naming whenthe distances between colours in the stim-uli was bigger. This underline that peo-ple are not using the same strategy to re-solve both tasks surprisingly, the strategyin colour word matching task dependsmore on the perceptual characteristics ofthe stimuli.

Summary In this experiment we foundthat verbal and non verbal interferenceimpair colour categorisation in the sameextend. However interferences did notwork on colour-word match naming task.We propose that this could be due to anautomation effect that we want to removein revised both interference and namingtask.


2.4 Experiment 2

We plan to perform a revised experimentthat addresses the problems of the pre-vious one. This experiment, Experiment2, is currently in progress, so the resultsare not yet available. As we did not ob-serve the effect that we expected for in-terferences in the colour-word matchingtask, we decided to improve our experi-ment in two ways. First, by changing thecolour-word matching task into a two al-ternative forced choice task, then adapt-ing the interferences so that they cannotbe automatised easily. The interferencethat we chose showed effect on categori-sation and was used in Lupyan 2009. InExperiment 2 however, we will run a clas-sical visual task (n-back) to control thatthey are equated in level of difficulty andthat visual interference has more of a neg-ative impact on the visual task than verbalinterference task (as it should do).

Methods

Participants This experiment is cur-rently in progress.

Materials The new verbal interfer-ence task will consist on remembering 9digits in saying them out loud during 10trials of the main task. Participants willhave to recall if a given number was pre-sented in the sequence of digit. Visuo-spatial interference (nonverbal) will con-sist in recalling a structure of 9 dots in agrid. The task will be to say if a point was

present at a particular place on this grid(see Figure A.14). We will first run a n-back task on 20 healthy controls to assessthe level of difficulty, and once we ensurethat the difficulty is equated for both in-terference tasks, we will run the revisedcolour categorization and colour colour-word matching tasks.

Figure 2.9

On the newcolour-word match-ing task people willhave to say whethera colour word pre-sented at the centre ofthe screen designatesthe colour shown inthe stimulus at the

bottom of the screen or in the stimulusin the upper part. This two-alternativeforced choice (2AFC) design can be seenin Figure 2.9. It will be done on thesame set of stimuli. Each couple is as-sociated with a colour which is presentwith a 50% chance within or cross stim-uli. Eight colours are presented 14 timesand 3 colours 15 times to avoid any biastowards detecting a given colour moreeasily. In the colour categorisation taskwe present hash signs to replace colournames in order to equate the amount ofvisual stimulation in both task. (see Fig-ure A.14 to visualise the design). Thenumber of hashes is the same as the num-ber of letters used on the label which ispresented for the colour-word matchingtask for a given trial as seen on the left.

2.5. Case study 21

Procedure The procedure will be ex-actly the same as in experiment 1 exceptthat there will be no habituation sessionbefore the block with interferences.

2.5 Case study

Methods

Patient RDS is a right-handed 53-year-old man who had an ischemic strokein the left occipito- temporal region inFebruary 2014. RDS is of Portuguese ori-gin but received all of his education inFrance. Before his stroke, he workedas a manager in the automotive after-market, where he interacted closely withtechnicians and car painters before hisstroke occurred. He used to work withcolours on a daily basis during car re-pairs. Specifically, he had to find thecorrect mixture of colours and to adjusthues to match the car colours in collab-oration with the painter. Therefore, hehad expertise in categorizing colour withan extensive colour knowledge and vo-cabulary. After his stroke, RDS presenteda right-sided hemianopia, alexia withoutagraphia and colour agnosia. His visualmental imagery for orthographic mate-rial and for object form and colour wasspared. RDS had no substantial problemsin tasks of colour discrimination, colourconstancy or colour contrast. His objectcolour knowledge was virtually intact. Incontrast, his colour naming was impaired.When he was first seen by a neurolo-gist, he could name only 3 patches out

of 11 basics colours. Today he is able toname all of them thanks to compensatorystrategies (see the introduction). A struc-tural magnetic resonance scan (Siemenssyngo MR B17) performed on the 30th ofMarch 2015 at the CENIR at the ICM inParis, revealed a lesion in the left occipito-temporal lobe. This lesion lies in theposterior cerebrum which was expandingfrom the posterior lingual gyrus, throughthe fusiform gyrus, reaching the parahyp-pocampal area. The splenium, whichis the posterior part of the corpus callo-sum, was also completely destroyed, re-sulting in an interhemispheric disconnec-tion between the two occipito- temporalregions. Investigation of the long-rangewhite matter tracts also shows a discon-nection between these brain regions andmore anterior regions in the left frontallobe. An inter-hemispheric disconnectionresults from the splenial lesion. Moreinformation about RDS’s lesion can befound in Figure A.16.

Controls Four males (mean50.25years±0.5, range 50-51) served ascontrol participants for comparison ofRDS’s performance.The controls had nor-mal or corrected-to-normal visual acuityand normal colour vision. They were ex-plicitly matched on professional experi-ence (RDS worked as a middle managerduring 18 years).

Materials The same stimuli wereused as in Experiment 1 but the material


used was from Experiment 2. Colour la-bels for the revisited colour-word match-ing task were presented orally throughoutspeakers due to RDS’s alexia.

Procedure Participants performedthe second version of the task i.e. colourcategorisation and revised 2AFC colour-word matching task. They only per-formed the control condition (ie. withoutany interference), and they performed ittwice for each task. Therefore 4 blockswere performed by each subject : 2 colourcategorisation and 2 colour naming.

Results

RDS Scores vs controls Perfor-mance on this task was analysed in termsof accuracy (% correct responses) and re-sponse time on one hand for RDS andon the other for controls. Mean accu-racy and response time for each trial (agiven couple) were calculated separatelyfor RDS and controls. Statistical analy-ses were performed using this data. RDShad a mean score of 81.8% of correct re-sponses (± 38.6) in colour categorisationand M=84.4% (± 36.4) in colour naming .Controls’ scores were M=88.9% (± 36.4)in colour categorisation and M=99.7%(± 05.6) in colour naming. It should benoted that the standard deviation scoresfor the control participants reflects bothbetween and within subject variability,whereas for the patient there is only thewithin subject variability. We did not dis-card any trials for the case study and did

not apply any modifications to the data.We did not focus on response time as wewere investigating difference in accuracyof the task. Indeed, patient and control re-sponse times are hard to compare due toother cognitive deficits resulting from thebrain lesion. For the statistical analysis weused modified Craw- ford and Howell t-test to test for dissociations in single-casestudies with a small control sample (hereN=4) (Crawford and Garthwaite, 2005).More specifically, we chose to used theRevised Standardised Difference Test totest whether there is a dissociation be-tween colour categorisation and naming,with the patient’s standardised z scorecompared with the differences measuredin the controls. According to this test thecase meets the criterion for a deficit oncolour word matching between RDS andthe control participants (t = -2.830, df = 3,p(one-tailed) = 0.033) (see Figure 2.10).

2.5. Case study 23

Figure 2.10.Main results in tcase study. Error bars rep-resenting 95% confidence intervals are only

shown only for controls.

RDS Scores vs 18 young controlsand 4 age-matched controls When plot-ting RDS’ result in colour categorisationamong the four controls of the case studyand 18 subject from experiment 1, we cansee that he is within the distribution foraccuracy but an outlier for response time.We compared performances on block ofcolour categorisation without naming be-cause it was the same (the only differencewas the presentation of hashes in the cen-tre of the screen that was not presented inexperiment 1). (see Figure A.3)

Correlation of RDS performance intasks To check if RDS could use hisresidual naming abilities to resolve thecolour categorisation task, we looked for

the correlation between colour categorisa-tion and colour word match task. There isalso no correlation between performancein naming and colour categorisation forRDS - neither for accuracy (Spearman’srho = 0.015, p =0.852) nor for responsetimes (Spearman’s rho = 0.053, p =0.506)(see Table A.3)

Achromatic and Chromatic Cate-gories analysis RDS’s troubles were re-lated to colour naming, we wanted tocheck if there were be differences in hisbehaviour on trials containing achromaticstimuli (black white or grey), regard-less of the fact that their were from thewithin-category or cross-boundary. Un-fortunately we could not use the modifiedCrawford t-test Crawford and Garthwaite2005 for the accuracy in colour naming forachromatic stimuli to test for dissociationsin single-case studies. Indeed accuracy onachromatic stimuli was 100% for healthysubjects, and variance equal to 0 whichis not compatible with the test. How-ever, we could use it in chromatics trialsfor colour naming and we obtain flowingscores : t = -2.894, df = 3. p= 0.031. Never-theless, what can be seen is that if chro-matic and non chromatic performanceis really similar in both naming and ac-curacy for controls, is this difference inaccuracy for naming in chromatic andnon chromatic stimuli (χ2=10.75, p=0.001)when it does not exist on colour categori-sation (χ2=0.76, p=0.38). Results can beseen in Figure 2.11.


Figure 2.11.Descriptive plot of Case Study results with separation of chromatic and non chromatic stimuli.

Jitter on the x axis is for better visuealtation only and have no meaning

Discussion First we have shown thatthere is a dissociation in performance ofcolour naming and not in colour categori-sation between patient and age and ed-ucation matched control subjects. More-over, this accuracy is lying within thescores of subjects for colour categorisa-tion task. Therefore, RDS shows impairednaming in comparison to controls, as op-posed to categorising, where he behaveslike them. Beside, there was no correla-tion in RDS performance (both accuracyand response time) between the two tasks(colour categorisation and colour wordmatching) then, his pattern of responses

for naming and categorization was notcorrelated. Therefore even though he isable to name and categorise stimuli withsame accuracy despite his colour anomia,his ability to name patches was not facili-tating his categorisation. Finally, dissocia-tion between colour naming and categori-sation in RDS’s performance no longer ex-ist when naming and categorisation areperformed on achromatic patches. Thissuggests that he is able to categorise andname achromatic categories without anydifficulty but when it comes to colours heis impaired only for naming them.

25

3 General discussion and conclusion

3.1 General discussion

We built a new task in order to disen-tangle colour categorisation from colournaming. In experiment 1 we found thatverbal and non verbal interference impaircolour categorisation in the same extend.However interferences did not work oncolour-word matching task. This couldbe due to the following reasons: first,the colour-word matching task requiredcolour categorisation thus it is impossi-ble to disentangle the specific effect ofverbal interferences on the verbal compo-nent of the task. Second, the colour-wordmatching task always came second afterthe colour categorisation task, it is thuspossible that subjects were better trainedon interferences during this task whichresulted in flattening of the dual-task ef-fect. Third, it is also possible that theprocess of assigning the verbal label toa colour patch is so fast and automatic,that it could be resistant to interferences.Another, we were able to show a differ-ence in strategy when participants wereforced to name the categories whereas cat-egorisation and naming are thought torely on same mechanism. In fact, dis-tances in CIELUV colour space betweentwo hues composing within stimuli (our

target) and performance were correlatedin the colour-word matching task but notin the colour categorisation task. Fur-ther investigation into this surprising re-sult could lead to novel insight into roleof perceptual strategy in this task. Fur-thermore, results from the study of abrain-damaged patient showed a dissoci-ation between both performances of thepatient compared to controls as well asbetween patient performances on achro-matic compared to chromatic trials. Thisshows that despite his difficulty in nam-ing colours, the patient is still able to cat-egorise them. It also shows that his dis-sociation between categorising and nam-ing is linked only to chromaticity. Thisencouraging result pushed us to continuein this direction by revisiting interferencesand colour-word matching task in orderto be able to show that our colour cat-egorisation task can be also performedin healthy participants when interferingwith naming. If this task shows the ex-pected results, the next step would be touse this protocol in fMRI to obtain evi-dence on the neural mechanisms under-pinning colour categorisation without ex-plicit need of verbal labels.

26 Chapter 3. General discussion and conclusion

3.2 Conclusion

The dissociation between performance onthe naming and categorisation tasks in pa-tient RDS suggests that our task disen-tangles colour categorisation from colournaming. Evidence for differences in relia-bility on perceptual distances for namingand categorisation found in healthy sub-jects also supports this hypothesis. Ac-tually, we showed that the involvementof verbal colour labelling is not neces-sary for categorisation of colours in a pa-tient impaired in colour naming. More-over, verbal and nonverbal interferences

affected the accuracy in colour categorisa-tion to the same extend, suggesting thatthe process might not rely on verbal la-belling. This suggests that colour nam-ing and colour categorisation are sepa-rate cognitive functions subsumed by dif-ferent neural mechanisms. Indeed whilean impaired left ventral visual stream re-sulted in impaired colour naming, it didnot affect colour categorisation to a simi-lar extent.

27

A Figures annexed

Figure A.1.Naming data. Each plot is showing a sample of patches from two typical colours : RO from redto orange, OY from Orange to red etc. Each column represents a patch and each row represents atrial presented to a subject. The meeting of the two is coloured in the colour that was chosen bythe subject of this trial. Each colour was therefore seen 51 times (3 time each subject multipliedby 17 subjects). The black horizontal represent the most given colour name switch on the set ofprobes lying between two prototypical colours. For example, in the left side of the black line inRO, probes were named as red more than 50% of the time and in the right side, they were named

as orange more than 50% of the time.

28 Appendix A. Figures annexed

Figure A.2. Illustration of keyboard used forname matching experiment and in the nam-

ing task of Experiment 1.

Appendix A. Figures annexed 29

Figure A.3.Performance of RDS among controls of case

study and 18 subjects from experiment 1


Figure A.4. Distances of Within and Cross stimuli.


Figure A.5. Cross and Within Stimuli for the pre-pilot


Figure A.6. Cross and Within Stimuli, Second set


Figure A.7.Pre pilot results : Correlation of accuracy and reaction time


Figure A.8.Accuracy and Reaction time of Pre-Pilot

C. Binomial test was performed on analysis to asses if participants were performing the task sig-nificantly better than the chance level


Figure A.9.Correlation of distances and Accuracy and reaction time


Figure A.10. Outliers


Table A.1. Main results for reaction time


Figure A.11. Main results for reaction time. Error bar srepresent standard error


Figure A.12. Main results for accuracy. Error bar represent standard error


Table A.2.Matrix of correlation between Reaction Time (RT) and Accuracy (Acc) in Colour Categorisation(CC) and Colour Naming (CN) under different interferences : Non Verbal Interference (NVI),

Verbal Interference (VI), No Interference (NOI)


Figure A.13. Correlation between distances and performance


Figure A.14.Interferences - Verbal and non verbal


Figure A.15.Resuts of repeated measure ANOVA run separately for colour categorisation and colour naming

with interference type as a factor.


Figure A.16.Figure from Katarzyna Suida-Krzywicka et al. poster


Table A.3.Correlation matrix between Accuracy (Acc) and Reaction Time (RT) in Colour Categorisation (CC)

and Colour Naming (CC)

47

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