Linguistic Relativity and Its PotentialImplications in the Field of Second Language

Acquisition

Richard KingB.A. (Mod.) Computer Science, Linguistics, and a Language (French)

Final Year Project, April 2011Supervisor: Dr. David Singleton

Declaration

I hereby declare that this thesis is entirely my own work and that it has not beensubmitted as an exercise for a degree at any other university.

April 5, 2011Richard King

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Permission to Lend

I agree that the Library and other agents of the College may lend or copy thisthesis upon request.

April 5, 2011Richard King

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Acknowledgements

First and foremost, I would like to thank Professor David Singleton, my super-visor, for his wisdom and guidance throughout this project. Without his input,it would simply not have been possible to do this project.

I would like to thank Dr. Carl Vogel, not only for his input over the courseof this project, but for all of the assistance he has given over the last 4 years.

To my classmates: your friendship, encouragement and assistance have beeninvaluable.

My thanks also go to Anders Moefelt for open-sourcing his facemash-alike script,upon which the website used to collect the data for the empirical research waslargely based.

I would also like to thank my family for putting up with me, and being therefor me over the last four years, and indeed, the 18 before that. And particularlymy mother for being my proof reader.

Last but not least, I’d like to thank all of my friends, wherever they may be,for teaching me the stuff books just can’t.

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Quotes

“The marvellous thing is that even in studying linguistics, we find that the universeas a whole is patterned, ordered, and to some degree intelligible to us.”– Kenneth L. Pike

“The opposite of a correct statement is a false statement. But the opposite of a pro-found truth may well be another profound truth."– Niels Bohr

“Pour examiner la vérité, il est besoin, une fois dans sa vie, de mettre toutes chosesen doute autant qu’il se peut.1”– René Descartes

“One is always a long way from solving a problem until one actually has the an-swer."– Stephen Hawking

“Tutto il nostro sapere ha origine dalle nostre percezioni.2”– Leonardo da Vinci

1“To examine truth, one must, once in their life, question everything so far as is possible.”(French)

2“Everything we know has its origin in our perceptions.” (Italian)

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Contents

Preamble iDeclaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iPermission to Lend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiQuotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivContents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

1 Introduction 11.1 Aims and Overview . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Linguistic Relativity 42.1 Ancient Roots of the Hypothesis . . . . . . . . . . . . . . . . . . . 52.2 19th and Early 20th Centuries - The Beginnings of Modern Think-

ing on Linguistic Relativity . . . . . . . . . . . . . . . . . . . . . . 62.2.1 Benjamin Lee Whorf . . . . . . . . . . . . . . . . . . . . . 6

2.3 Current Writers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3.1 Lucy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3.2 Boroditsky . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3.3 Everett . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.3.4 Slobin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.4 Linguistic Relativity in Modern Culture . . . . . . . . . . . . . . 102.4.1 Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.4.2 Artificial Languages . . . . . . . . . . . . . . . . . . . . . . 112.4.3 Programming Languages . . . . . . . . . . . . . . . . . . . 12

2.5 Evidence in Support of the Hypothesis . . . . . . . . . . . . . . . 122.6 Criticisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

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3 The Study of Second Language Acquisition 153.1 Language Transfer & Interlanguage . . . . . . . . . . . . . . . . . 163.2 Process of Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . 163.3 Fossilisation and The Critical Period . . . . . . . . . . . . . . . . 17

4 Potential Implications of Linguistic Relativity in Second LanguageAcquisition 194.1 Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.1.1 Teresa Cadierno . . . . . . . . . . . . . . . . . . . . . . . . 204.1.2 Kenny R. Coventry, et al. . . . . . . . . . . . . . . . . . . . 214.1.3 Panos Athanasopoulos . . . . . . . . . . . . . . . . . . . . 214.1.4 Gale Stam . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5 Empirical Research 245.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255.2 What is a Colour? . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.2.1 The Nature of Light . . . . . . . . . . . . . . . . . . . . . . 285.2.2 The Eye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.3 How Best to Go About It? . . . . . . . . . . . . . . . . . . . . . . . 295.3.1 How the Website Worked . . . . . . . . . . . . . . . . . . . 30

5.4 Discussion & Analysis of Results . . . . . . . . . . . . . . . . . . 325.4.1 Win Propensity in Comparison with a Colour’s ‘Greenness’ 335.4.2 Score in Comparison with a Colour’s ‘Greenness’ . . . . . 33

6 Conclusions 406.1 Conclusions from Review of Literature . . . . . . . . . . . . . . . 416.2 Conclusions from Review of own Work . . . . . . . . . . . . . . . 42

References 44

Index 50

A Website Code IA.1 Database Structure . . . . . . . . . . . . . . . . . . . . . . . . . . I

A.1.1 Battles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IA.1.2 Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . II

A.2 PHP classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IIA.2.1 db-gen.php . . . . . . . . . . . . . . . . . . . . . . . . . . . IIIA.2.2 index.php . . . . . . . . . . . . . . . . . . . . . . . . . . . IIIA.2.3 session.php . . . . . . . . . . . . . . . . . . . . . . . . . . VIA.2.4 compare.php . . . . . . . . . . . . . . . . . . . . . . . . . . VIA.2.5 rate.php . . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII

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A.2.6 timeup.php . . . . . . . . . . . . . . . . . . . . . . . . . . X

B Abbreviations XII

C Comprehensive Results of the Study XIIIC.1 Comparison of Dividing Points . . . . . . . . . . . . . . . . . . . XIIIC.2 Comparison of Extremes . . . . . . . . . . . . . . . . . . . . . . . XIVC.3 Language Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . XV

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List of Figures

5.1 English - Colour vs. Propensity to be Greener . . . . . . . . . . . 345.2 Dutch - Colour vs. Propensity to be Greener . . . . . . . . . . . . 345.3 French - Colour vs. Propensity to be Greener . . . . . . . . . . . 355.4 Italian - Colour vs. Propensity to be Greener . . . . . . . . . . . . 355.5 Irish - Colour vs. Propensity to be Greener . . . . . . . . . . . . . 365.6 English - Colour vs. Score . . . . . . . . . . . . . . . . . . . . . . 375.7 Afrikaans - Colour vs. Score . . . . . . . . . . . . . . . . . . . . . 375.8 French - Colour vs. Score . . . . . . . . . . . . . . . . . . . . . . . 385.9 Spanish - Colour vs. Score . . . . . . . . . . . . . . . . . . . . . . 385.10 Italian - Colour vs. Score . . . . . . . . . . . . . . . . . . . . . . . 395.11 Portuguese - Colour vs. Score . . . . . . . . . . . . . . . . . . . . 39

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List of Tables

C.1 The colour in the middle for English . . . . . . . . . . . . . . . . XIIIC.2 The colour in the middle for French . . . . . . . . . . . . . . . . . XIIIC.3 The colour in the middle for Italian . . . . . . . . . . . . . . . . . XIVC.4 The colour in the middle for Spanish . . . . . . . . . . . . . . . . XIVC.5 The colour in the middle for Portuguese . . . . . . . . . . . . . . XIVC.6 The colour in the middle for Dutch . . . . . . . . . . . . . . . . . XIVC.7 The colour in the middle for Afrikaans . . . . . . . . . . . . . . . XIVC.8 The colour in the middle for Irish . . . . . . . . . . . . . . . . . . XIVC.9 The colour extremes for English . . . . . . . . . . . . . . . . . . . XIVC.10 The colour extremes for French . . . . . . . . . . . . . . . . . . . XVC.11 The colour extremes for Italian . . . . . . . . . . . . . . . . . . . XVC.12 The colour extremes for Spanish . . . . . . . . . . . . . . . . . . . XVC.13 The colour extremes for Portuguese . . . . . . . . . . . . . . . . . XVC.14 The colour extremes for Dutch . . . . . . . . . . . . . . . . . . . . XVC.15 The colour extremes for Afrikaans . . . . . . . . . . . . . . . . . . XVC.16 The colour extremes for Irish . . . . . . . . . . . . . . . . . . . . . XVC.17 All data gathered for English . . . . . . . . . . . . . . . . . . . . . XVIC.18 All data gathered for Afrikaans . . . . . . . . . . . . . . . . . . . XVIIIC.19 All data gathered for Dutch . . . . . . . . . . . . . . . . . . . . . XXC.20 All data gathered for French . . . . . . . . . . . . . . . . . . . . . XXIIIC.21 All data gathered for Italian . . . . . . . . . . . . . . . . . . . . . XXVC.22 All data gathered for Spanish . . . . . . . . . . . . . . . . . . . . XXVIIIC.23 All data gathered for Portuguese . . . . . . . . . . . . . . . . . . . XXXC.24 All data gathered for Irish . . . . . . . . . . . . . . . . . . . . . . XXXII

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Abstract

As anyone who has ever studied a language will know, translation is not merelya case of directly swapping words in one language for their equivalents in an-other. It is, (un)fortunately, somewhat more complicated than that. There existsa myriad of different manners in which to express an idea or notion, and differ-ent manners are employed by different languages.

Benjamin Lee Whorf proposed that “[w]e dissect nature along lines laid downby our native language,” and that “[l]anguage is not simply a reporting de-vice for experience but a defining framework for it.” This is the fundamentalprinciple of Linguistic Relativity, a hypothesis the corner-stone of which is theposition which holds, essentially, that the phraseology of an idea or object inan individual’s language influences how that individual perceives that idea orconcept.

This paper aims to establish any potential issues that may arise in the teach-ing and learning of a second or subsequent language as a result of this phe-nomenon, as well as to examine linguistic relativity itself in terms of any effectit may have on the perception of the colour spectrum.

Chapter 1

Introduction

“Motivation is what gets you started. Habit is what keeps you going.”– Jim Rohn, 1982

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1.1 Aims and Overview

The purpose of this paper is to examine Linguistic Relativity, a hypothesiswhich claims that there is a direct link between cognitive perception of one’senvironment and the language(s) one knows, and how it may possibly pose is-sues for people attempting to learn a foreign language.

I will first begin with an analysis of the hypothesis, examining what exactlyit means, followed by a brief history lesson detailing the history of thoughton this topic, from ancient times up to the 19th century. I shall then delve intomore recent academic work, from both sides of the argument, from writers suchas Benjamin Lee Whorf (after whom the hypothesis is often called), and StevenPinker.

After this, I will discuss the field of Second Language Acquisition with refer-ence to current research in the field that is, at least to some extent, relevant tothe study of Linguistic Relativity.

Then it remains to discuss various papers detailing research into the manifes-tations of Linguistic Relativity in non native speakers of a language with re-spect to their native language(s). In this section I will focus mainly on Han andCadierno (2010), but by no means do I exclude other papers and research.

Finally, I will present the results of the empirical research I carried out, investi-gating variations in the perception of the green-blue spectrum across languages,specifically analysing and comparing the point on the continuum at which anygiven language separates green from blue, looking for both differences in lan-guages concepts of individual colours, as well as looking for any variation in theactual perception of the colour spectrum, which would hint at a certain degreeof Linguistic Determinism.

1.2 Motivation

While much research has been done into Linguistic Relativity, it has mainlybeen focussed on the comparison of the cognitive processes of speakers of dif-ferent languages rather than how it manifests itself in multilinguals. As a mul-tilingual, albeit with only one L1, any implication of learning the languages Ihave learnt, or of learning any subsequent languages on my cognition is of realinterest to me.

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For me, at least, the study of human cognition and behaviour is fascinating.The subtle patterns and the intricate ways through which they are explainedare awesome facets of the natural world.

Finally, linguistic relativity is one of the oldest and most hotly debated areasof linguistics. From ancient Greek philosophers to modern academics, it hasbeen a topic of much debate, and tracking the hypothesis’ evolution over thecenturies is something I much enjoyed.

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Chapter 2

Linguistic Relativity

“Language is the dress of thought.”– Samuel Johnson, 1752

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Linguistic Relativity (LR)1 is a hypothesis which claims, as Wilhem vonHumboldt put it, that “[t]he diversity of languages is not a diversity of signsand sounds but a diversity of views of the world” (Humboldt, 1820). In essence,it is the idea that the language(s) we speak can influence our thought patterns,non-linguistic behaviour and even our perception of everything that occurs andexists around us, by teaching us, or even forcing us, to think in a logic in keep-ing with the grammar of the language. Swoyer (2003) points out that whenanalysing LR, one must avoid an all-or-none way of thinking, i.e. they keyquestion is “whether there are interesting and defensible versions of linguisticrelativism between those that are trivially true (the Babylonians didn’t have acounterpart of the word ‘telephone’, so they didn’t think about telephones) andthose that are dramatic but almost certainly false (those who speak differentlanguages see the world in completely different ways)” (ibid.).

2.1 Ancient Roots of the Hypothesis

According to Allan (2004), the ancient Greek philosopher Aristotle thought oflanguage as a symbolic system which represents our experiences as the mindrepresents them, and that all humans have the ability to have the same experi-ences. In Plato’s dialogue “Cratylus” (Sedley, 2003), Socrates is asked by Her-mogenes and Cratylus as to whether the names of things are derived by nature,or “physis”(‘φυσις’), (i.e. that there is an intrinsic link between the soundsmade an the objects the words represent) or by some human convention, or“nomos”(‘ηoµoς’), (i.e. that words are just an arbitrary collection of letters/-sounds). Aristotle’s position, that language is used to represent experiences isright at the core of the idea of LR, and shows us that the general concept ofconsidering the relationship between thought and language has existed sincethe very early days of study into language. Socrates’ statements in “Cratylus”,which, in fact, lean towards the idea that names are derived by nature2, formthe basis for the later work by de Saussure (among others, e.g Klinkenberg andHjelmslev) into the relationships between signifier and signified, the generalfield of semiotics.

1Often called the Sapir-Whorf hypothesis, named after the linguists who first seriously de-veloped the idea.

2In fact, at its most basic level, language may have originated from non-arbitrary namingof objects, see Ramachandran and Hubbard (2001) for an insight into the “kiki” and “bouba”shapes - which hint at a slight inherent degree of synesthæsia in an overwhelming majority ofhumans.

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2.2 19th and Early 20th Centuries - The Beginningsof Modern Thinking on Linguistic Relativity

Saussure (1916) is a compilation of the lectures given by the influential Swisslinguist Ferdinand de Saussure (1857-1913). In it is detailed Saussure’s propo-sition of a dyadic model of a sign, which is made up of the signifier and thesignified. It is a psychological model which has the signifier as an “acousticimage” and the signified as a “concept”. This was really the beginning of mod-ern thinking into the relationship between language and thought. However, theGerman philosopher Wilhelm von Humboldt (1767-1835) proposed a ‘Weltan-sicht’ (world-view) hypothesis, in which he was among the first to emphasizethe differences different languages reveal between the different cultures whichspeak them (O’Donnell, 2008). This hypothesis held that speakers of differ-ent languages have varying ways of viewing the world, stating in “Essai sur leslangues du Nouveau Continent” (1812) that the language we speak “transplantsus” into the world we live in (ibid.). For von Humboldt, language and thoughtwere one and the same. He even went so far as to call language a “mirror ofthe mind” (ibid.). Edward Sapir (1884-1936) of Columbia University developedthese ideas further, holding the opposite opinion to von Humboldt, in that hedid not believe that language and thought are one and the same, but rather thatthey are closely related, and very similar, describing thought as “a refined inter-pretation of [language’s] content” (Sapir and Mandelbaum, 1949). Lucy (1992)states that Sapir supported the LR principle, and believed that culture had acertain degree of influence over language, stating that “[n]o two languages areever sufficiently similar to be considered as representing the same social real-ity”, and that the worlds of different cultures are distinct and “not merely thesame world with different labels attached” (ibid). He did not fully accept lin-guistic determinism as promoted by von Humboldt3.

2.2.1 Benjamin Lee Whorf

Benjamin Lee Whorf (1897-1941) originally qualified as a chemical engineer,however he is better remembered as a linguist, and to him is attributed the“Sapir-Whorf Hypothesis” (Edward Sapir was his mentor at Yale), often calledsimply the “Whorf Hypothesis”, one of the foremost interpretations of LR. Heheld that language had an effect on thought, and that the structures of languagehad implications for cognition. He originally worked as a fire inspector, and

3Linguistic Determinism is a position held by some, notably in Wittgenstein (1961), thatlanguage limits or determines thought, whereas LR holds that one’s perception of the world isrelated to one’s language.

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some of his early thoughts on language arose from inspections of fire damagedworkplaces, where linguistic confusion was thought to be a contributing factor.It became apparent to him that “the meaning of [a] situation” to people pre-scribed how they behaved and the level of caution they exercised. He furtheremphasised that it was the linguistic meaning associated with the situation thatdictated behaviour. In one instance, he was examining a fire caused by the ex-plosion of spent ‘gasoline drums’, in which workers at the plant were far morecautious when handling the drums when they were full of the highly flammableliquid than when the drums were ‘empty’. However, the drums that were emptydid not house a vacuum, nor did they house standard atmospheric air. Theyheld gasoline vapour, which in this case is far more dangerous than the liquidfuel. The workers at the plant were casual enough when handling the emptydrums as to throw cigarette butts around. He says : “Physically, the situationis hazardous, but the linguistic analysis according to regular analogy must em-ploy the word ’empty,’ which inevitably suggests a lack of hazard. The word’empty’ is used in two linguistic patterns: (1) as a virtual synonym for ’null andvoid, negative, inert,’ (2) applied in analysis of physical situations without re-gard to, for example, vapour, liquid vestiges, or stray rubbish, in the container.”(Whorf, Carroll, and Chase, 1956). The workers at the plant assumed meaning(1), when, in fact, meaning (2) was the case.

Sapir and Whorf

When Whorf went to Yale to pursue his academic interests, Sapir was teachingin the Department of Anthropology. Sapir, who was continuing the work of hismentor, Franz Boas (Columbia University), into the relationship between lan-guage and culture4, mentored Whorf. Sapir believed, primarily, that culture in-fluenced language (at a lexical rather than morpho-syntactic level) (O’Donnell,2008). Whorf continued in his own work, largely but not entirely indepen-dently, crafting the theory as it is known today. The term “Sapir-Whorf hypoth-esis” attributes the work to them both and was first used in (Hoijer, 1954, p.92-105).

2.3 Current Writers

In the last 25 years or so, there has been a remarkable resurgence in interest inthe topic, and as such there is plenty of contemporary research and literature tobe found pertaining to LR. Some of the more prominent modern proponents ofthe principle include John Lucy at the University of Chicago, Lera Boroditsky

4It was Boas who originally gave the famous example of the Eskimo (Inuit) words for snow.

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at Stanford, Daniel Everett at Bentley, and Dan Slobin at UC Berkeley. In thissection I will summarise some of each of their contributions.

2.3.1 Lucy

One of the best known neo-Whorfian writers of today, John Lucy’s work tendsto focus on the potential implication structural, that is syntactic, differences be-tween languages may have for cognition (O’Donnell, 2008). He has spent a con-siderable amount of time studying and comparing English and Yucatec Maya,and in Lucy (2004) (from O’Donnell (2008)), he notes that the speakers of thelanguages are constrained in different ways when using nouns in the plural - inEnglish, plural is marked only for count nouns (discrete, discernable and count-able objects) and not mass nouns (objects which have no defined shape or size);whereas in Yucatec plural marking is never required. When, in English, we wishto enumerate a mass noun, we are obligated to name the unit by which we countcount the thing, e.g. two locks of hair. However, in Yucatec, all forms with a nu-meral must take a numeral classifier, which, according to Lucy, “reflects the factthat all nouns in Yucatec are semantically unspecified as to quantificational unit- almost as if they referred to unformed substances.” In relation to this finding,he conducted a study (Lucy and Gaskins, 2001), in which he investigated ob-jects that would retain their shapes over time (for which English presupposesshape as the quantificational unit, and Yucatec the substance), predicting thatwhen presented with 3 objects (hypothetically: a rubber cube, a rubber ball,and a plastic ball), one of them a pivot (the rubber ball), and asked which twoare most alike, speakers of English would chose the two with the same shape,and Yucatec speakers would choose the two made from the same material. Theresults of the experiment confirmed his prediction. He then sought to knowhow speakers of each language would react when the object was more mal-leable in nature, a situation which forces English speakers to consider more thematerial of the object. He predicted that speakers of both languages would tendto choose material over shape. And again his predictions were largely correctin terms of the results derived from the experiment. Interestingly, propensityto chose by material is low for younger speakers of both languages, increasingwith age, and more dramatically in the case of Yucatec Maya.

2.3.2 Boroditsky

In Boroditsky (2003), Lera Boroditsky points out that if one were to repeat a sen-tence describing a scene in different languages, it could be necessary to knowvarious small details about the scene in one language which wouldn’t necessar-ily be encoded. By taking her example phrase “the elephant ate the peanuts”,

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and comparing it to how it would be repeated in different languages we cansee that many things we as English speakers do not even register are funda-mental to the sentence structure in other languages, and, indeed, vice versa. InMandarin we have:

(1) DàAdult

xiàngelephant

chıeat

hashengpeanut

The elephant ate the peanuts

Note the lack of tensing in the verb ‘eat’. Conversely, French encodes infor-mation that we do not in English, and as such the original sentence could betranslated, depending on whether all or just some of the peanuts were eaten, ineither of the following two ways:

(2) L’The

éléphantelephant

aAUX

mangéate

lesthe.DEF

cacahuètes.peanuts

The elephant ate the peanuts

(3) L’The

éléphantelephant

aAUX

mangéate

desthe.PART

cacahuètes.peanuts

The elephant ate (some of) the peanuts

French requires that whether it was some or all of the peanuts that wereeaten be explicitly encoded into the sentence. Other languages can require theencoding of particular information that I’m certain most English speakers neverseriously consider, for example, Turkish requires that the verb be marked toshow whether the knowledge is first-hand or not. She states that these differ-ences may have some impact on cognition. Personally, I feel it would be in-teresting to see if language had an effect on long term memory, that is, wouldFrench speakers be more likely to remember correctly whether all or some ofthe peanuts were eaten? Does one remember best those details that are linguis-tically more salient in one’s own language?Boroditsky has also done some research into how languages conceptualise time,citing differences between the English horizontal model and the Chinese verti-cal model as evidence for LR. She found (Boroditsky, 2001) that English speak-ers answered “purely temporal questions” more quickly after horizontal primesthan vertical primes, with the opposite being the case for speakers of Mandarin.Another interesting aspect of time models to explore would be the indigenousSouth American language of Aymara, in which speakers gesture behind themwhen speaking of the future, and in front of them when speaking of the past(Núñez and Sweetser, 2006), the polar opposite of the gesticulations that wouldbe expected of a speaker of English, and many other European languges.

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2.3.3 Everett

Daniel Everett is best known for his work with the Pirahã people of the Brazil-ian Amazon in Everett et al. (2005). Their language is an anomaly, lackingfundamental structures and concepts present in most other known languages.The Pirahã language lacks words (and seemingly concepts) for numbers, andis referred to as a “one, two, many” language, in that it distinguishes betweenthe one and two items, but any larger number of items is just referred to usingnon-specific quantifiers. The tribe felt that as a result they were being cheatedin trade and asked if he could teach them numeracy. After 8 months, not asingle member of the community could count to ten, or do basic addition, andthe project was abandoned. Everett performed an experiment wherein Pirahãspeakers were tested to see if they could remember how many objects they wereshown. The could, most of the time, remember accurately up to four objects, butafter that the accuracy fell away dramatically. Everett claims that the culturalideology of the Pirahã is the reason that traits of language thought universal arenot present in Pirahã.

2.3.4 Slobin

Dan Slobin’s most influential work to date is Slobin (1996), in which he pro-poses his idea of “thinking for speaking” as a reformulation of the relation-ship between language and thought. It is his opinion that in order to becomea competent speaker of another language one must learn a mode of thinkingspecific to that language. He is also interested in the field of language acquisi-tion, making his research particularly pertinent to this thesis. One of the moreinteresting works he has conducted is the “Frog Story Project”. In Berman andSlobin (1994), he, along with Ruth Berman, developed a children’s story bookcomprising 24 pictures and no words. It allows for narratives that have similarcontent, but will vary by language and the age of the children who ‘read’ them.The study compared English, Spanish, German, Turkish, and Hebrew - thoughdata exists for tens of languages.

2.4 Linguistic Relativity in Modern Culture

LR is a phenomenon that has cropped up a substantial number of times inmodern literature. The genre of book in which it most commonly occurs isScience Fiction. There also exist artificial languages that have been developedto take advantage of Whorfian interpretations of cognition, some deliberately,and some by chance. In the field of computer programming, there has also been

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some research done into the role LR may play in coding languages.

2.4.1 Literature

George Orwell’s 1949 classic “Nineteen Eighty-Four” deals with life in a totalitar-ian state, with a dictator called “Big Brother”. The protagonist is an employee inthe “Ministry of Truth” and is charged with altering historical records in orderto support the regime’s propaganda. One of the ways that the regime maintainscontrol is through modifying the language used by the citizens into what theycall “Newspeak”, allowing only certain thoughts and concepts to be present inthe the minds of the populous.In 1966 Samuel R. Delany published “Babel-17”, a book that deals with an on-going war in space, in which a language (with many interesting features, in-cluding lacking a 1st person singular pronoun) has been developed to be usedas a weapon. Learning it turns you into a traitor, but attracts you to learn it asdoing so can heighten your other senses and abilities.

2.4.2 Artificial Languages

Ithkuil (Quijada, J., 2004), a language created by John Quijada, was designedto be “capable of high levels of conciseness and semantic detail while overtlyreflecting a deep level of cognitive conceptualization, more so than in naturallanguages”. Kozlowski, S. (2004) examines the language in terms of the LRhypothesis and its concise nature, arguing that the speed of human cognitioncould be greatly increased (he suggests five- or six-fold increases in speed) byspeaking such a streamlined language. Unfortunately, there are no known flu-ent speakers (much less native speakers) of the language to test out this idea.The “Newspeak” of Orwell’s aforementioned work was used by the totalitarianregime to influence the population’s thoughts and general perception of real-ity. Newspeak sought the radical reduction in vocabulary, ultimately reducingmeaning to simple contrasts (good vs. bad). Another way the language wasused to control the population is evidenced by the lack of a word for ‘science’.Newspeak itself is a modified version of English, with a much more aggluti-native structure than contemporary English. Words like ‘bad’ are rendered as‘ungood’5, and irregularities and complexities are removed, for example ‘good-gooder-goodest’ has replaced ‘good-better-best’.Brown (1966) sets out the stall for “Loglan”, a language based on first-order log-ical predicates, designed to test the LR hypothesis. He aimed for it to be so dif-ferent from natural languages that anyone who learnt it would need to think in

5Note the similarity to the Esperanto prefix ‘mal-’ in big-granda vs. small-malgranda

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a different way. Riner, R.D. (1990) says “As far as we can yet know, LOGLAN canaccommodate precisely and unambiguously the native ways of saying thingsin any natural language. In fact, because it is logically rigorous, LOGLANforces the speaker to make the metaphysical (cultural, worldview) premisesin and of the natural language explicit in rendering the thought into (disam-biguated) LOGLAN. Those assumptions, made explicit, become propositionsthat are open for critical review and amendment - so not only can the Sapir-Whorf hypothesis be tested, but its details can be investigated with LOGLAN”.

2.4.3 Programming Languages

The myriad of programming languages that exist in the world can be divided upaccording to what they see as the best way of solving problems. When applyingLR to programming languages, one would be hypothesising that the languagesknown to the programmer influence him in his decision as to how best to solve aproblem. All programming is the search for the optimum solution to a problem,and a good programmer would know the relative strengths of the languages inhis toolset when it comes to that particular area of problem-solving, and wouldknow which one was strongest. However, the hypothesis would hold that as heis unaware that his strongest language may be weaker than languages he doesnot know, he may not actually find the optimum solution to the problem athand. Graham (2004) deals with this subject, and suggests that because writingin one language means thinking in that language, programmers will be happywith whatever language they use as it is the language that dictates the programthey will write.

2.5 Evidence in Support of the Hypothesis

In the 1950s and 1960s, the hypothesis6 came under rigorous scrutiny. Brownand Lenneberg (1954) conducted experiments which they designed in orderto discover whether or not the perception of the colour spectrum varied amongspeakers of different languages. Berlin and Kay (1991) appeared to discredit theentire principle when they claimed that universal linguistic constraints came into play in relation to colour nomenclature, and interest in the principle waned.Since the 1980s, however, there has been a resurgence in interest in the topic.Pütz and Verspoor (2000) say that recent studies and experiments provide agreat deal of support for weak versions of the hypothesis (i.e. that , as Roger

6The term “Sapir-Whorf” hypothesis, introduced by a student of Sapir’s, is commonly usedsynonymously with the term “Linguistic Relativity principle”. It is, in fact, a misnomer, as theprinciple, as defined by Whorf (alone) is not a hypothesis in the scientific sense.

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Brown put it, “structural differences between language systems will, in gen-eral, be paralleled by nonlinguistic cognitive differences, of an unspecified sort,in the native speakers of the language”), particularly when it comes to spatialrelations, but also in analysis of the colour spectrum. Drivonikou et al. (2007)conducts some very interesting research in comparing the presence of Whorfianeffects on concepts in the left and right hemispheres of the brain, and concludesthat concepts analysed in the left hemisphere (the one more closely linked torational, logical and objective thinking) are more prone to Whorfian influence.Among the concepts analysed in the left hemisphere of the brain is the colourspectrum, which lends some credence to the idea that LR affects perception ofthe colour spectrum.Niemeier and Dirven (2000) is a collection of articles espousing support forthe idea of LR. An article there-contained by Gábor Győri examines the rela-tionship between language and cognition. Győri defines human knowledge interms of genetic, neural and symbolic knowledge. Humans are the only speciesto possess symbolic knowledge, and it is this type of knowledge that is by somedistance the most important. The larger part of human cognition is symbolic,and is mediated to us by language. This is evidenced by the fact that we canknow a great deal about some things that we have not, and possibly will never,experience. As such, certain types of human knowledge rely on language. Győrithen says that LR is a logical conclusion of this fact, stating that “the types ofknowledge that must by their nature be mediated to us through language willalso be processed in our minds in that medium”. In this case, whether or notdifferences exist between how languages interpret reality is entirely irrelevant,but what is clear is that cognition (the definition of which he takes to be “theacquisition, organization and application of knowledge”) is undoubtedly influ-enced by language. Language is thus not just an instrument of communication,but also of cognition.

2.6 Criticisms

Alford (1981) gives a good synopsis of the major criticisms waged against Whor-fianism. He notes that though many and varied, the majority are fallacies basedon the misrepresentation of Whorfianism and only disprove extreme statementswhich nobody ever made.Many of the criticisms made of LR seem to project claims of linguistic deter-minism on anyone who mentions linguistic relativity. Olshewsky (1969) claimsthat “[t]he thrust of the Sapir-Whorf Hypothesis is that thought and culture notonly reflect the linguistic forms and categories with which they operate but aredetermined by them”, which is completely false. Anyone assuming this to be

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the position of a relativist, and from there seeking to discredit them is makinga grave error. There is much, often quite valid, criticism of determinism whichis wholly inapplicable to LR. Pinker (2000) speaks of identicality, determinismand relativity as if they were all as absurd as each other. Identicality7 is thor-oughly torn apart, without even bringing in his notion of mentalese, that is tosay that all humans perform cognitive processing in the same mental language,and merely translate our ideas while speaking. He also demonstrates the fal-sity of determinism, before moving on to LR, which he conveniently side-steps.He doesn’t discuss any research into the relationship between thought and lan-guage, but gives a “clinching experiment” which appears to show that physi-ology, rather than language, is the dominant influence on the learning of newcolour words.In his preface to Schaff et al. (1973), Noam Chomsky claims that “Whorf arguesthat the structure of language plays a role in determining a world-view”, whichagain, shows a confusion as to what LR actually is. He goes on to point outa valid flaw in Whorf’s work, in that the hypothesis he puts forward is basedsolely on his research into Hopi, with particular reference to the model of timein the Hopi language. He validly points out that Whorf’s representation of theEnglish system is incorrect and that the argument made is tentative at best(Lenneberg (1953) claims that the fact that Whorf described the Hopi modelof time in English is evidence which disproves LR as Whorf could translate theidea. However, Whorf’s point was not that things in one language are not trans-latable into another, but rather that, in this case, an English speaker is able tounderstand how a Hopi speaker thinks without thinking that way themselves).However, even if Whorf’s original ideas on LR may have had false seeds, todiscount an entire philosophical position as a result, ignoring the fruits of anysubsequent research, is very naïve.

7The belief that language precisely, in a word-for-word fashion, determines thought.

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Chapter 3

The Study of Second LanguageAcquisition

“I wish life was not so short,” he thought. “Languages take such a time, and so do allthe things one wants to know about.”– J.R.R. Tolkien, The Lost Road, c. 1936

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Second language acquisition (SLA) is the process whereby a person learnsa second1 language. The focus of research into SLA is on the learner and whatprocess he undertakes, rather than what role is played, or what influence is held,by a teacher. It is relevant to point out that the term ‘bilingualism’ is often usedin certain fields to describe multilingualism on a whole, but true bilingualism(be it simultaneous or not) is not considered to be within the field of study ofSLA, but rather it is the goal of a second language learner, or the state attainedon having learnt a second language.

3.1 Language Transfer & Interlanguage

Language transfer is a feature of what is known as ‘learner language’, wherebythe language learner’s L1 influences productions in the L2. The syntactic andlexico-semantic rules of their L1 manifest in their use of the L2 (Cook (2008)gives the example of comparing French and Spanish learners of English. Span-ish is a pro-drop language, which may cause the learner to say “is raining”,rather than “it is raining”, a mistake that a French speaker would be unlikelyto make). Learner language is of interest2 to those who study SLA as it gives aninsight into the mental representation of the language on the part of the learner.It became apparent that ‘learner language’ could not be adequately describedas a cross between the L2 and the learner’s L1. Sometimes learners come outwith sentences which are not grammatical in either language. The concept of‘interlanguage’(IL) was developed to explain this phenomenon. Interlanguageis that learner language which incorporates: language transfer, overgeneralisa-tion3, and simplification4.

3.2 Process of Acquisition

The input received by the learner appears to be the most important factor whenit comes to the learning of a language (Krashen, 1994, 1981). These paperspoint to studies showing the amount of time spent in an immersive environ-ment (e.g. living in a foreign country) is closely related to the rate at which thelearner learns. Krashen (2004) points to reading as being a key source of input

1The word second, in this instance, is a misnomer. The field, in fact, concerns itself with theacquisition of all languages which are not first languages (L1s).

2Ellis and Barkhuizen (2005) says that it is the primary source of data for those who studyin the field as it is not yet possible to analyse such mental representations with brain scans etc..

3The use of rules in ways that are not usually deemed correct. Essentially, not knowingexceptions to rules.

4At times, the language used can be very simple, bordering on a pidgin.

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for the language learner. The more foreign language literature consumed, thefaster the learner learns.Also important is the output produced by the learner. Practising speaking andwriting in the foreign language speeds up acquisition. Long (1996) holds thatoverall interaction (be it reading, writing, listening, or speaking), particularlywhen the learner has to guess at meaning when there is a communication break-down, helps in improving overall comprehensibility of the input.Long, (ibid.), also mentions that, for the best conditions for language acqui-sition, a learner must be engaged in the encoding and decoding of semanti-cally meaningful content. That is, to a certain degree, saying that learners learnbest when performing internal translations of the content in the environmentin which they are interacting.

3.3 Fossilisation and The Critical Period

A language learner’s learning is said to be fossilised when it stops improving.The learner’s language becomes solidified in a constant state of interlanguage.Corder and Corder (1986) describes IL as a learner-constructed grammar whichapproximates the grammar of the target language, gradually increasing in ac-curacy over time. Selinker (1972) claims that the approximation of the targetgrammar may be stopped at one or more points, calling the permanent stop inmovement towards the TL grammar “fossilisation”. It is very common for fos-silisation not to be overcome despite serious effort at learning. Selinker alsocomments that learners may become complacent once they attain a level ofthe language that allows them to communicate comfortably, and may, there-fore, even subconsciously, lack the required motivation or desire to maintainprogress towards native-like grammar and general linguistic dexterity.The idea that a language is not completely mastered, I feel, calls for men-tion of the Critical Period Hypothesis (CPH). This hypothesis (popularised byLenneberg (1967)), which is the subject of much debate in the field of linguis-tics, purports that one’s ability to learn a language decreases dramatically at apredetermined point, and after such a point, great effort and determination isrequired to successfully learn a language. Lenneberg (ibid.) claims that the win-dow for achieving native-like competence in a language closes with the onset ofpuberty, and that in order to gain full mastery in a language, it is necessary tostart learning it before this time.In terms of SLA, this hypothesis is widely rejected. It is widely accepted, amongscholars in this area, that the earlier one begins learning a language the better(Singleton and Lengyel, 1995). The same paper points out that of adult bilin-guals, 5% begin learning one of their languages when they are in adulthood.

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Any idea of an abrupt decline in one’s ability to perform a task would seem tofly in the face of the natural, gradual decline that humans experience with ag-ing. Adult learners of language do tend to retain an identifiably foreign accent,however this is not universal, and may be due to a lack of desire to sound like anative speaker.Thus, fossilisation would appear to be a limit imposed by constraints other thanbiological, such as the lack of motivation, lack of free time to dedicate to thestudy of the language, or the lack of desire to attain fully native-like profi-ciency.

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Chapter 4

Potential Implications of LinguisticRelativity in Second LanguageAcquisition

“My words fly up, my thoughts remain below:Words without thoughts never to heaven go.”– William Shakespeare, Hamlet, 1601

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As I mentioned previously in §2.3.4, Slobin (1996) claims that in order tofully gain mastery in an L2, the learner must learn to think like a native speaker.This, in essence, is where implications for SLA lie, should we accept LR as a realphenomenon. If one’s cognitive process is different from that of a native speakerof another language (ceteris paribus), then the learning of that other languagerequires not just the learning of its grammatical rules and vocabulary, but alsothe learning of the intricacies of the cognitive process of its native speakers.

4.1 Research

Problems for language learners occur when they project concepts1 from theirL1 into the L2 resulting in an L2 production that doesn’t fit that language’smodel. In this instance, it is important to note what is language transfer andwhat is not. Speakers of pro-drop languages leaving out the subject pronoun intheir non-pro-drop L22 are not showing a difference in cognitive process, norare speakers of non-plural-marking languages failing to pluralise nouns in situ-ations that a plural-marking language would require them to3. Cadierno (2010)discusses the relationship between LR and SLA in terms of motions involving aboundary crossing event, and how speakers of verb- and satellite-framed (Span-ish, German, and Russian)languages perform in a satellite-framed L2 (Danish).

4.1.1 Teresa Cadierno

In the study in Cadierno (2010), the subjects performed three tasks. Therewas a picture description task “which was designed to elicit the learners’ pre-ferred means of expression when describing motion scenes depicting boundary-crossing situations”, for three different types of boundary crossing (into a boundedspace, out of a bounded space, and over a plane). The second and third taskswere concerned with vocabulary production and recognition in order to guagethe range of options available to the subjects. In the second task, subjects wereasked to write down any motion verbs they could think of, and in the third task,subjects were asked to circle the verbs of motion in a list of which they fully

1Particularly the relations between objects.2An example of this would be a Spanish speaking learner of English producing the sentence

“Is good.” when speaking English. The standard equivalent in Spanish is “Es bueno” (‘is good’),but equally “Ello es bueno” is grammatically correct. This is language transfer as it is a simplemanifestation of a construction which is grammatical in the L1, and not in the L2, being usedin the L2.

3An example of this would be native speakers of Irish, who, if saying the equivalent of “threedogs, four dogs, five dogs”, produce in their own language a construction that would be trans-lated very literally as “three dog, four dog, five dog”.

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understood the meaning. When analysing the results, very little difference wasnoticed between German and Russian L1 subjects (both of which are satellite-framed languages), however, when these results were compared the results ob-tained from the Spanish L1 subjects (Spanish, like all Romance languages, is averb-framed language) there was a marked difference when it came to the typeof construction used in the picture description task, the proportion of manner-of-motion verbs (to run, to jump, etc.) used in both the picture description taskand the vocabulary production task, and the number of verbs produced andrecognised in the vocabulary test. This data seems to point towards the conclu-sion that the subjects4 whose L1 is Spanish/verb-framed have issues in Danishthat pose much less of a problem to speakers of satellite-framed languages.

4.1.2 Kenny R. Coventry, et al.

Coventry et al. (2010) seek to explore LR and how it relates to spatial relations,with the research presented in the paper dealing more specifically with contain-ment relations. On research into spatial relations, an example from Pedersonet al. (1998) is given (see §5.1 on page 26). The study documented in the pa-per was designed to test for conceptual differences in containment and supportrelations between L1 speakers of English and Spanish, on the basis that Span-ish has one lexical term for the two relations (‘en’) and English uses two terms(respectively: ‘in’, and ‘on’), and to see how this may interact with immediatememory.Very early on in the paper, it is confessed that the experiment failed to workas planned, as there appeared to be no difference in sensitivity levels to con-tainment and support relations between the two languages (when testing L1speakers) in question, “challenging more extreme versions of linguistic relativ-ity, and undermining the rationale for testing for these conceptual differencesfor these relations in second language learners”(Coventry et al., 2010). Withno difference being evident between the native speakers of the two languages,the second part of the test, which would have been to test L2 speakers of thelanguages to see to which pattern they would adhere, did not go ahead.

4.1.3 Panos Athanasopoulos

Athanasopoulos (2006) sought to recreate the experiment conducted in Lucyand Gaskins (2001) as portrayed in §2.3.1, with Japanese L2 learners of En-glish, examining learners at various levels and comparing them to monoglot

4All subjects in the study were resident in Denmark for between 18 and 30 months prior tothe study, and had partaken in a Danish government language education course.

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native speakers of English, and monoglot native speakers of Japanese. Sub-jects would be presented with triads, as in the Lucy & Gaskins experiment,consisting of pictures with one card having the same type of item as the pivot,but a different number of them, and another card having the same number ofitems as the pivot, but with different items. His expectation was that Englishspeakers would group as if number were important, and that Japanese speakerswould group as if the type of item or substance mattered more. The gathereddata did indeed reflect this, but also noted that more advanced Japanese L1learners of English seemed to behave more like English speakers than eitherJapanese monoglots, or the other, less advanced, English learners. From this,he concludes that L2 acquisition may change cognitive processing which werepreviously influenced by the L1.

“Overall then the results show that the cognition of the two L2 groupscorrelates with their proficiency in the L2 and with their perfor-mance on the grammaticality judgement task. The more successfulthe L2 learners are in the QPT5 and in the grammaticality judgementof number marking and articles in English, the more they behavelike monolingual speakers of English in the cognitive task.” (ibid.)

4.1.4 Gale Stam

In her 2010 paper (Stam, 2010), Gale Stam (a professor of Psychology at National-Louis University, based in Chicago) asks whether or not an L2 speaker’s modelof thinking for speaking can change. She performed a study, analysing the pat-terns of speech of a native speaker of Spanish resident in the United States ofAmerica, and how they changed over a 9 year period (1997 - 2006). The sub-ject had begun learning English two years prior to the beginning of the study.Stam compared how the subject expressed path and manner in motion eventsin both the L1 and L2 at the beginning and at the end of the study, and com-pared them to each other, and to similar productions of native speakers. Thesubject was interviewed twice, in 1997 and 2006, and was asked to narrate thesame Sylvester and Tweety Bird comic strip on both occasions. Stam hypothe-sised that there would be a shift towards more native like productions in the L2as proficiency increased over time. The results largely supported this notion.Stam found that there was no change in the subjects use of Spanish, for eithermanner or path. In terms of path events in the L2, the subject in the initialinterview used a mixture of her L1 (verb-framed) construction and the correctL2 (satelite-framed) construction to narrate the comic strip. In the follow upinterview in 2006, it was found that the subject had, as hypothesised, drifted

5The Oxford Quick Placement Test, a language proficiency assessment test. My annotation.

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to a substantially more native like use of constructions (that is, a decreased useof gerund forms). However, the same results were not found in terms of man-ner, where the L1 construction persisted. Stam proposes several reasons as towhy this may be the case, among them that manner is seen as less importantthan path in motion events, or that there is a lack of exposure to manner verbsin a way that is similar to how children acquire them (nursery rhymes, games,etc.). But overall, the evidence from the change in the narration of path eventsin the L2 over time indicates that it is possible for language learners to changetheir model of thinking for speaking, albeit a gradual shift. The implicationhere is that although LR may pose problems in language learning, it is not aninsurmountable obstacle. It can be overcome with persistence, patience anddedication.

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Chapter 5

Empirical Research

“If we knew what it was we were doing, it would not be called research, would it?!”– Albert Einstein, c. 1920

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As part of this project, I decided to perform some research of my own intowhether or not one’s language affects perception. Having initially consideredan analysis of spatial relations, I settled on working on the colour spectrum. Inmore specific terms, I thought it would be interesting to gather data on how dif-ferent languages separate adjacent colours along the continuum of colours vis-ible to the human eye, with the view that significantly different division pointswould evidence a certain degree of influence on the part of language.

5.1 Background

According to Lucy (1997), there are 3 main strains of experimental research intoLinguistic Relativity. Firstly, there is the “structure centered” approach, wherea unique or rare structure in a language is analysed to see if and how it impactsupon the behaviour of speakers of that language (Lucy (1992), Lucy and Gask-ins (2001) and Lucy (2004) examine Yucatec1 along these lines).Secondly, there is the “behavior centered” approach, which is, to a certain ex-tent, the inverse of of the “structure centered” approach, in that it analysesthe behaviours of different language communities and searches for contrast-ing linguistic phenomena that might explain any differences between the be-haviours. Lucy (1997, pg. 303-304) discusses some very interesting researchdone by Strømnes into the differences between Finnish and Swedish. He wasinspired to do this research because of the frustration he felt in his seeminginability to learn Finnish. He deduced, from a series of novel experiments, that

“Swedish prepositions can be represented in terms of a vector ge-ometry in a three-dimensional space whereas Finnish cases can berepresented in terms of a topology in a two-dimensional space cou-pled with a third dimension of time (or duration)” (ibid.)

These differences are born out in many subtle ways. Strømnes noted differencesin the cinematography of Indo-European and Ural-Altaic language groups, not-ing that

“Indo-European (Swedish, Norwegian, English) productions formedcoherent temporal entities in which action could be followed frombeginning to end across scenes, whereas Ural-Altaic (Finnish, Hun-garian, Estonian) productions showed more emphasis on static set-tings with only transitory movement and formed coherent person-centered entities in which scenes were linked by the emotional Gestaltsof persons” (ibid.)

1A Mayan language spoken by the aboriginal peoples of Southern Mexico.

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From this, he focussed on why accidents were more common in Finnish speak-ing factories in Finland than those in which Swedish was spoken (in both Swe-den and Finland) and deduced that

“Finns organize the workplace in a way that favors the individualworker (person) over the temporal organization of the overall pro-duction process. Lack of attention to the overall temporal organi-zation of the process leads to frequent disruptions in production,haste, and, ultimately, accidents” (ibid.)

This is a prime example of behaviour centred research, as it seeks to explaindifferences in practical behaviour between groups by way of a known linguisticdifference.Finally, there is the category of research under which this study falls, whichLucy calls “domain centered”. In this type of study, a researcher will take a par-ticular semantic domain (in my case colour nomenclature, but other examplesinclude spatial relations) and analyse correlations within it across various lan-guages. Many researchers (among them Paul Kay and John Lucy) acknowledgean issue in the study of the colour spectrum as a result of the fact that humanperception of the colour spectrum depends to a certain extent on the hardwarethat the human has2 allowing them to perceive a limited field of colour. Interms of research into spatial relations, Pederson et al. (1998) documents anexperiment in which speakers of “absolute-frame” and “relative-frame” lan-guages3 were asked to perform a non-linguistic task in which they were firstpresented with a table with 4 cards on it(having the same pattern but differentorientations), and were then rotated 180 degrees around the table and asked topick the same card. The speakers of the relative languages(English and Dutch,in this case) picked the card with the same orientation relative to themselves,whereas the speakers of the absolute framed languages chose the card with thedots in the opposite orientation to themselves (relatively) but with the exact

2i.e. the eye and the neural networks to the brain3An absolute framed language refers to spatial relations in absolute terms, for example

“north of” or “downhill from” (an example of such a language is Tzeltal (Boroditsky, Schmidt,and Phillips, 2003)), whereas English is a relative framed language, allowing for the expres-sion of relations like “the boy is to the right of the girl”. In languages like Tzeltal, there is nopossible way to encode the meaning of the sentence “the boy is to the right of the girl”, in-stead requiring the speaker to encode it as something along the lines of “the girls is west ofthe boy” (clearly requiring a phenomenal cognitive ability in having a mental compass even inunfamiliar environs).

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same orientation in the absolute frame4.To date, the largest single aspect of LR to have been studied is colour terminol-ogy. Rather than serving as a deterrent to further study in this field, the factthat studies constantly contradict and challenge each other provides a ratherrich collection of reading materials from both sides, as well as being very opento more research. Many people think of colours as absolute entities, whereasthey are, in fact, arbitrary points along a continuum. To perform a comparisonon the various arbitrary points at which different languages choose to differen-tiate one colour from another could provide interesting results.As for why specifically the green-blue continuum was chosen, this comes downto several factors. Firstly, Berlin and Kay (1991) lay out the ascending scaleaccording to which languages acquire terminology for different colours. Onthis scale green and blue are acquired sequentially5, and as a result may havea less well defined boundary. I also chose to go with the blue-green continuumrather than any other aspect of the visible colour spectrum as it is the sub-section of visible light least susceptible to colourblindness6, thus reducing asmuch as possible the potential influence of defective colour perception due tothe test subjects physical ability. Finally, as a speaker of a Celtic language(Irish),I have always been aware of a certain degree of difference in the way that differ-ent languages define their colour “ideals”7. In Irish, the word “glas” is trans-lated as “green”. However, this word also covers silver (as in the colour of theblade of a sword) and grey8. In Welsh, a cousin of the Irish language, however,“glas” means “blue”. Etymologically, this is due to the fact that the root of the

4It is also worth noting that spatial relations are 2-ary or 3-ary predicates. English has 3-ary predicates, referencing both objects and the speaker, whereas an absolute framed languageonly takes into consideration the two objects for which the relation is being defined. Languagesthat employ 3-ary predicates can also access 2-ary predicates (“Dublin is south of Belfast” is aperfectly logical and grammatical sentence in English), but 2-ary relation employing languagescannot move up. Tepehua (a Totonacan language spoken in Central Mexico by fewer than10,000 people) is an example of an intrinsic framed language, which references immediaterelations (beside, on top of, etc.) rather than abstract ones (north, uphill, etc.). Intrinsic andAbsolute framed languages operate under the same constraints.

5If a language has 6 colour terms, then it has a term for blue, if it has only 5, it does not haveblue but has both of green and yellow (along with black, white, and red). If it has only fourterms for colours, it has black, white, red and then one of either green or yellow, so in strictterms, the acquisition of terminology for green and blue, although always sequential, may notbe one after the other, but may well be.

6According to http://web.archive.org/web/20061017164313/http://www.colorfield.com/ref/types.html(retrieved 22 Feb 2011) it occurs in 0.001% of males and 0.03% of females (thus roughly 0.016%of the world’s population).

7That is to say the frequency of light that native speakers picture upon hearing a specificcolour term.

8“capall glas” would be translated to English as “grey horse”.

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word “glas” in Proto-Celtic referred to a myriad of different colours and shades(among them: green, blue, grey, metallic colours, faded fabrics and the colourof ice and frost)9.

5.2 What is a Colour?

Quite key to this study is the concept of a colour. Colour is not a physicalphenomenon, there are not rays of blue, red, green, etc. shooting around theplace. Colour recognition is, fundamentally a psychological process, dependanton three factors - the physical properties of light, the working of the retina andpsychological analysis.

5.2.1 The Nature of Light

Malacara (2002), Ch. 1, discusses in some depth the physical properties of light.Light itself is a narrow range of electromagnetic waves which are detectableby the eye. It is the frequency of these individual waves that determines whatcolour we perceive. Shorter wavelengths have an increasingly violet appearance(ultra-violet light is that light with too short a wavelength for us to see), whereasthose waves with longer wavelengths have a more red appearance (infra-redlight is light with too long a wavelength to be seen). The actual sensation ofcolour occurs with the stimulation of cones on the retina (see 5.2.2). Coloursthat we see may be spectrally pure colours, i.e. those that consist of only wave,but are more often than not combinations of various different waves (Malacara(2002) gives the example of orange, which is either a spectrally pure colourcreated by a wave with a wavelength of 6×10−7 metres, or more likely a combi-nation of two or more waves (for example yellow and red, having wavelengthsof 5.8×10−7 and 7×10−7 metres, respectively)). As the eye is a synthesiser (i.e. itcannot divide a compound colour into it’s original constituents10), colours aregenerally assumed to be spectrally pure.However, this model of colour does not allow for pink, among other colours,providing us only with the “hue” of a colour therefore we need to go deeper. Innature, colours mix with white to become more dilute, or more properly: moreor less saturated. The degree of saturation is called a colour’s “chroma”. Thefinal parameter which must be considered is “luminance”, or the amount of

9The Proto-Celtic root for both green and blue is “*glasto-”. See: The University of Wales(2011) and Matasović (2009).

10In fact, this can only be done with a device called a spectroscope, or, in the case of “white”light, a prism (as demonstrated in Newton (1671)). Compare with the ear, which, when listeningto an orchestra, can pick out individual instruments playing, and is thus termed an analyser.

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black that is mixed into a colour. We can see this by comparing the appearanceof a spectrally pure red colour when it is in well illuminated surroundings, andwhen it is dimly lit.Important work into colour and its perception was done by Young (1802), whopostulated that all colours are composed of three elementary colours. Helmholtz(1852) went further and identified the three elementary colours as red, greenand blue. It wasn’t until Maxwell (1856) that this was proved conclusively.König (1891) first suggested that this may be down to having three colour re-ceptors in the eye.

5.2.2 The Eye

The inside of the human eye is lined with a light sensitive tissue called theretina. The retina is a complex structure which is comprised of many layers ofneurons. Of these layers, the only one we really need to take into considera-tion for the purposes of this paper is the “Outer nuclear layer” (Carter-Dawsonand Lavail, 1979), which contains the photoreceptor cells, which have two maintypes. There are rod-cells which function in dim light, giving black and whitevision. Colour receptors are called cone-cells, and these function in daytimelight (Björn, 2002). There are three variations of cone-cells, each being moresensitive to light waves of a particular frequency. They are called L (for long(red) wavelengths), M (for medium (green) wavelengths) and S (for short (blue)wavelengths)(Malacara, 2002). Osterberg (1935) claimed there were, on aver-age, six million cones in the eye, and this is the most commonly cited figure,however research by Curcio et al. (1990) may point to there being only 75% ofthat number. The peak wavelength for each type of cone, naturally, varies fromhuman to human, but the figure rarely varies by more than 10−6 metres eitherside of the averages. The S cone has an average peak at 4.3×10−7 metres, whilethe M cone has its average peak in the vicinity of 5.4 × 10−7 metres, and theL cone’s peak averages at about 5.7 × 10−7 metres. There is generally sensitiv-ity to colour when wavelengths are between 3.8 × 10−7 and 7.4 × 10−7 metres(Malacara, 2002).

5.3 How Best to Go About It?

At first I was unsure as to how exactly I would go about data collection. Forobvious reasons, the larger the data set was the better, and as such I decided thatthe data gathering process should be a computer based survey, and the mostefficient way (I felt) of gathering a wide sample (particularly when looking forspeakers of different languages) was to host this survey on the internet. From

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this point, various options were available to me. I could ask participants to rateany particular point in the green-blue continuum out of 10 for its greenness,I could ask them to select the point in the continuum that they felt was thedividing point between green and blue, I could have subjects watch a video ofthe transition between green and blue and ask them to click when they felt thecolour had changed from green to blue, or blue to green. The idea I settled on inthe end was to present the subjects with two shades from along the continuum,and ask them to select which was greener. Essentially, this functioned like asporting match-up, with the winner being the greener colour. The colour atthe end of the experiment with the highest percentage of wins was deemed thegreenest, and that with the lowest percentage was the bluest. Each shade on thecontinuum was also assigned an original score11, which was modified with eachresult as per the Elö ranking algorithm, which is used to rank chess players andinternational football teams. It was then a modified version of the Facemashwebsite created by Facebook founder Mark Zuckerberg.

5.3.1 How the Website Worked

Subjects were directed to the website and were informed of the aims of theexperiment, and what would be required of them. They then selected theirnative language and were directed to another page which would be used forthe testing. On this page, the subject was presented with 2 colours and belowthem, a display of where the dividing point was for their language based oncurrent data12. The page also contained the basic instruction “Click which oneyou think is greener”. Upon clicking which colour the subject perceived asgreener, the result was then analysed by the program. The program updatedthe database for the number of wins and losses incurred by the colour, and thenperformed an operation based on the Elo algorithm on the scores of the two‘competitors’.The colour with the score closest to the original value of 1500 was deemed asthe dividing point between both green and blue, as it had both a roughly similarpercentage of wins and a score that remained relatively neutral when the resultswere weighted.

The Elo Algorithm

Elo (1978) set out a method for ranking chess players. In it, any two chessplayers with a score can be ranked. It compares the two on several grounds. For

11I settled on the relatively arbitrary number of 1500.12This feature was unintentionally left over after testing, and may have played some role in

influencing subjects in their decision making.

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every match-up, there is an expected result. In the case where C1 and C2 arepitted against one another, the expected result for C1 (EC1) would be

11 + 10(ScoreC1)−(ScoreC2)/400

,

and similarly, the expected result for C2 (EC2) would be

11 + 10(ScoreC2)−(ScoreC1)/400

.

By those metrics, an absolutely certain win would have and expectation of 1,and an absolutely certain loss would have an expectation of 0.The equation for the calculation of a player’s new score is, thus:Score′C1 = ScoreC1 +K(R−EC1)where:

• Score′C1 is the new score for player C1,

• K is the ’K-coefficient’, which I will describe below, and

• R is the actual result (1 for a win, 0 for a loss).

The K coefficient is used differently in different implementations of the algo-rithm. In chess, it is assigned based on the players status (novice, grand master)and in football, it is used to give different values to different levels of competi-tion. In essence, it is a value that corresponds to the significance of the result.Were a chess grand master to win, because he would be expected to win hewould gain very few points, and this is multiplied by his K number. Howeverwere he to lose, he stands to lose a lot of points, and the coefficient magnifiesthis - so shock results have more of an impact.In my implementation everything was calculated as it is for chess. However,the K-coefficient was determined by comparing the proximity of the two pointson the spectrum that were being analysed. If two were extremely close to eachother, this may be difficult for the subject to discern, and thus the result wastreated as insignificant. If the colours were very far apart (for example: thegreen of the Italian flag and the blue of the French flag), any ‘shock’ resultwould surely be an error on the part of the subject, and the result would alsobe considered insignificant13. Between these two poles, I divided the remainingshades into two separate groups, leaving me with the insignificant pairs beingthose that are >90% the same or different, those that are between 50% and 90%the same as the most significant, and the remainder as somewhat significant.

13If this were to continue for any language, over time the insignificant results would becomesignificant

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5.4 Discussion & Analysis of Results

The data gathered provided me with specific values for the percentage of time acolour was chosen, and its score at the end of the study, as calculated on an on-going basis by the Elo Algorithm during the study. Every colour was assigneda number in the range 1-500, with the lower values being the bluer shades,and the higher values being greener14. From the study, useable data was gath-ered from 8 languages: English, Afrikaans, Dutch, French, Italian, Spanish, Por-tuguese and Irish15. Unfortunately, these 8 languages do not provide a partic-ularly wide sample of the world’s languages, as all of them are Indo-European,with English, Dutch and Afrikaans(which is itself barely more than a dialectof Dutch) all being Germanic in origin; French, Spanish, Portuguese and Ital-ian are all Romance (though there may be a greater level of similarity betweenSpanish and Portuguese than the others due to the fact that they both immedi-ately descended from Ibero-Romance); and Irish is a Goidelic Celtic language.The largest dataset collected was that of English, consisting of almost 7000 in-dividual clicks, going all the way down to Irish and Portuguese, with around400 clicks each. Data was collected for other languages, but there was insuffi-cient data upon which to perform useful analysis, for example, German had inthe region of 350 clicks and was unusable. The first interesting thing to note is,as visible in §C.3, how well sorted the colours actually are.I should also point out that a feature I put in during the testing phase of thedevelopment of the website, which displays the green-blue continuum at thebottom, was accidentally left in. On realising this, I decided not to remove it,as it would have led to an inconsistency in the data collected, as it may haveinfluenced the subject, in the sense that they may have read from the spectrum,rather than referencing their internal lexicon.The analysis I performed on the data was linear regression, comparing a colours‘greenness’ value to both its propensity to be selected as the greener colour, andalso to its ‘score’ when the results were weighted. As English was the largestdata set16, it’s R2 values, i.e. how accurately it could predict one value from

14As the colours were generated for the study using 24-bit Truecolor RGB values. With Redalways being zero, the range from blue to green are those colours that fall between 〈0,0,255〉and 〈0,255,0〉, thus 512 possible colours. In order to keep the number of colours manageable,the numbers for the parameters were incremented with values greater than 1.

15A caveat when using data like this pertaining to the Irish language is that it would be verydifficult to find a native Irish speaker without a high proficiency in, and exposure to English,given the pervasiveness of the English language in Ireland. The overwhelming majority ofnative Irish speakers will have English language proficiency indistinguishable from Englishmonoglots. Thus, unexpected similarities between the two may need to be taken with a pinchof salt.

16And, conveniently, my L1!

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another, was the highest on all fronts, thus I chose it as the standard againstwhich I will compare the data from the other languages.

5.4.1 Win Propensity in Comparison with a Colour’s ‘Green-ness’

To illustrate any points made, I will use the following 5 languages in discussingthis result: English (5.4.1), Dutch (5.4.1), Italian (5.4.1), French (5.4.1) and Irish(5.4.1).This result was striking in it’s uniformity. The plot for each language is of thecolour’s win propensity (on the Y-axis) and its values (on the X-axis). As such,the slope of the trend line correlates to how early a speaker of that languagebegins seeing green. English and Dutch in this case have R2 values greater than0.9, French has 0.89, and Irish and Italian have 0.77 and 0.79 respectively. Theresults therefore are reasonably reliable. The slope of each language is verygradual, but what is striking is that the slopes for English, Dutch, French andIrish are all exactly 0.19%. That would suggest that speakers of all of theselanguages do not perceive differently in any way the green-blue spectrum. Theslope for Italian is just 0.17%. Given the 21% error margin that must be af-forded to the data from the language as per its R2 value, the difference is quiteinsignificant. If this statistic was significant, it could point to the fact that Ital-ian, like Russian and several other languages, has 3 words to cover what wecall ‘blue’ and ‘green’, namely ‘blu’, ‘azzurro’ and ‘verde’. This difference is in-significant, and the fact that it is Italian that differs from the norm is seemingly,without any other evidence to the contrary, just a coincidence.This result is a bit of a thumbs down for the strong version of the LR hypothe-sis, i.e. Linguistic Determinism, in that there seems to be absolutely no effect oflanguage on the mental parsing of the colour spectrum.

5.4.2 Score in Comparison with a Colour’s ‘Greenness’

When the data being compared changes from the percentage of times which aparticular shade was deemed greener to the score associated with that point onthe spectrum (which gives data in a more ‘weighted’ fashion), there seemed, onfirst glance to be no difference. However, on closer inspection, an unexpectedtrend clearly visible in English (5.4.2) and Afrikaans (5.4.2) actually permeatesthroughout all of the languages. The fact that much more data was gathered forlanguages like English, Dutch and Afrikaans means that trends are more pro-nounced, and outliers are fewer. Were there more data for the other languages,I feel that the pattern of a deviation from the trend line, followed by a sharp

33

0 100 200 300 400 500

0

0.2

0.4

0.6

0.8

1

ColourValue

Gre

enPe

rcen

t

R2 = 0.981

ScoreTrend

Figure 5.1: English - Colour vs. Propensity to be Greener

0 100 200 300 400 500

0

0.2

0.4

0.6

0.8

1

ColourValue

Gre

enPe

rcen

t

R2 = 0.9363

ScoreTrend

Figure 5.2: Dutch - Colour vs. Propensity to be Greener

downward kick below the trend line and a gradual return to the trend as seenfor English would be more obvious in all of the languages.This blip would appear to represent where the languages separate blue fromgreen. Taking the point where the actual (blue) line crosses the trend line

34

0 100 200 300 400 500

0

0.2

0.4

0.6

0.8

1

ColourValue

Gre

enPe

rcen

t

R2 = 0.8926

ScoreTrend

Figure 5.3: French - Colour vs. Propensity to be Greener

0 100 200 300 400 5000

0.2

0.4

0.6

0.8

1

ColourValue

Gre

enPe

rcen

t

R2 = 0.7972

ScoreTrend

Figure 5.4: Italian - Colour vs. Propensity to be Greener

(red)17 we can read off the point of the spectrum where ultimate confusion

17Another possible reading of the graph is that the bottom of the trough immediately fol-lowing this crossing is the dividing point between the colours in the eyes of the language inquestion.

35

0 100 200 300 400 500

0

0.2

0.4

0.6

0.8

1

ColourValue

Gre

enPe

rcen

t

R2 = 0.7756

ScoreTrend

Figure 5.5: Irish - Colour vs. Propensity to be Greener

on the part of the subject between green and blue occurs. Admittedly, due tothe number of outlying results for some of the languages, I had to discern thecrossing point from looking at the numbers rather than the graph. Nonetheless,with our spectrum being metered as going from 1 to 500, we would expect, inan idealised way, that all languages would have a dividing point at 250. This is,however, not the case.English, in fact, does exhibit its point of division at 250, and this may havesomething to do with the fact that the computer scientists who designed theRGB system in use for the experiment were native speakers of English. Afrikaans(260) and French (240) (5.4.2) vary little from English. Spanish has a value of230, slightly more deviant from the English value than the others, but no cor-relation is present to Portuguese(280) (5.4.2), which, as I hypothesised, maypossibly be due to the Iberian origin of the two languages. Most interesting isthe value for Italian (200) (5.4.2). Italian deviates significantly from the oth-ers, and while I hesitate to draw any absolute conclusions from this, I proposethat this may have something to do with the fact that three words are used (see§5.4.1) to cover the same colour space as we cover, in English, with just two.

36

0 100 200 300 400 500

0

1,000

2,000

ColourValue

Scor

eScoreTrend

Intercept

Figure 5.6: English - Colour vs. Score

0 100 200 300 400 500

0

500

1,000

1,500

ColourValue

Scor

e

ScoreTrend

Intercept

Figure 5.7: Afrikaans - Colour vs. Score

37

0 100 200 300 400 500

0

500

1,000

1,500

2,000

ColourValue

Scor

eScoreTrend

Intercept

Figure 5.8: French - Colour vs. Score

0 100 200 300 400 500

0

500

1,000

1,500

ColourValue

Scor

e

ScoreTrend

Intercept

Figure 5.9: Spanish - Colour vs. Score

38

0 100 200 300 400 500

0

500

1,000

1,500

2,000

ColourValue

Scor

eScoreTrend

Intercept

Figure 5.10: Italian - Colour vs. Score

0 100 200 300 400 500

0

500

1,000

1,500

2,000

ColourValue

Scor

e

ScoreTrend

Intercept

Figure 5.11: Portuguese - Colour vs. Score

39

Chapter 6

Conclusions

“Begin thus from the first act, and proceed; and, in conclusion, at the ill which thouhast done, be troubled, and rejoice for the good.”– Pythagoras, c. 530 BC

40

Language, culture and thought are undoubtedly linked inextricably. That agiven language has any effect on the cognitive processes of its native speakersis the source of great debate. But to suggest that we all look at things andbehave in a uniform manner is preposterous. The world is full of difference,and indeed, it is therein that its beauty lies. Language, being as essential to thehuman experience as it is, and being as varied as it is throughout the world, ispart of that beauty. To suggest that, although we all speak differently, we allthink in the same way would mean the world would be a much greyer, and amuch less enjoyable place to inhabit.

6.1 Conclusions from Review of Literature

From reviewing the literature that I have, it is possible to conclude many things.We can see that LR as a general notion has existed for a very long time. It is,and seemingly always has been, very controversial. The work done in Lucy andGaskins (2001) provides some of the first definite, experimentally verified evi-dence in support of the reality of Whorf’s original proposal. While many argueit, I believe I have shown that linguists such as Pinker and Chomsky have totallyfailed to support their oppositions to it, by falling into the trap of assuming thatLR and LD are one and the same. I believe also that I have provided reasonablesupport for the distinction that one must make between LD and LR1. Exam-ples such as the different rate of workplace accidents experienced by those inFinnish and Swedish speaking workplaces, as documented by Strømnes is veryclear evidence for LR, while not making any claims about LD. What is apparentis that LD is widely rejected among the academic community.A really interesting area related to LR and not explored here, brought aboutby Dan Slobin, is the idea of “thinking for speaking”. Of particular interestto me is in what way LR may affect people’s memories of events, and whetherlanguage acts as a filter for long term memory. Work done by Boroditsky et al.(2002) on Indonesian and English speakers seems to give some credence to thenotion that language does indeed steer memory towards those details whichthe speaker’s language deems to be more important (while Indonesian speakersmay not remember as well when an event occurred in relation to other events,as it is not necessary to tense verbs in that language as it is in English, theymay have a better memory for details of the event that do require marking inIndonesian, and which are not marked in English, e.g. the relative age of theactor in the event to the observer).

1Linguistic Determinism is the theory that the language you speak determines entirely howyou can and do interpret reality, whereas Linguistic Relativity suggests, simply, that non-linguistic behaviour is influenced by language.

41

The works I cited by Athanasopoulos and Stam seem to highlight the fact thatLR does indeed pose issues in the learning of language (also evidenced in Cadierno(2010)). However, both studies found that the language learners analysed thereinseemed to improve their nativelikeness over time and with increased input.What their studies show is that LR is an obstacle in language learning, but thatit is not an insurmountable one. Language learners must acquire a new modelof ‘thinking for speaking’ in order to produce utterances which are more likethose of native speakers. Essentially, the main implication of LR in the areaof SLA is that it does provide a stumbling block initially to learners, whichthey gradually overcome, but as motivation and attitude are such key factorsin the acquisition of language, the detrimental effects of this stumbling blockmay grow disproportionately to the actual problem posed. The only real way,it seems, to acquire the new model of ‘thinking for speaking’ is persistence anddedication.

6.2 Conclusions from Review of own Work

The data gathered from my own study points slightly in favour of weaker in-terpretations of LR in relation to interpretations of the colour spectrum. Con-versely, the evidence gathered in relation to LD very much points against it,with absolutely zero variation in the recognition of colours as being greener orbluer. Admittedly, the error in including a representation of the spectrum onthe test (as detailed in §5) makes this data not 100% reliable.The variations in the point at which languages seem to separate green and blueare interesting, and seem to evidence weak LR. The most interesting amongthem being that of Italian. Its use of three lexical items in the classification ofthe same interval of the colour spectrum, for which we, in English, use onlytwo, seems to have affected how native speakers of Italian divide the spectrumin comparison to native speakers of English, and other languages. It wouldhave been interesting to corroborate this data with data from Russian. Unfor-tunately, sufficient data was not gathered from that language, but it would besomething that would interest me to see in the future. Indeed, I was disap-pointed not to have gathered data on more languages over the course of theexperiment, as I expected that, if differences were to exist between languages intheir divisions of the colour spectrum, they would be most pronounced acrossdifferent language families (as such, comparing English with languages such asFinnish, Mandarin, Japanese, Arabic or Indonesian may have provided resultsthat are far more interesting). I have left the website open, and plan to checkfrom time to time to see if sufficient data for any of these languages has beenacquired for analysis, out of personal interest.

42

The implications of these differences are that language learners seem to needto acquire more than just a foreign lexicon and grammar rules, and that theparameter resetting proposed by supporters of Universal Grammar isn’t quiteenough to explain the actual process undergone in acquiring proficiency in aforeign tongue. They must, as stated in Athanasopoulos (2006), acquire a newpattern of ‘thinking for speaking’.

43

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N. O’Donnell. Linguistic Relativity and the Sapir-Whorf Hypothesis. The in-fluence of language on cognition and the implications for Second LanguageAcquisition and Bilingualism. Trinity College, Dublin - School of Linguistic,Speech and Communication Sciences, 2008. Undergraduate Thesis.

T.M. Olshewsky. Problems in the Philosophy of Language. Holt, Rinehart andWinston, 1969.

G. Osterberg. Topography of the layer of rods and cones in the human retina. Levin& Munksgaard, 1935.

E. Pederson, E. Danziger, D. Wilkins, S. Levinson, S. Kita, and G. Senft. Seman-tic typology and spatial conceptualization. Language, 74(3):557–589, 1998.ISSN 0097-8507.

S. Pinker. The language instinct: How the mind creates language. Harper PerennialModern Classics, 2000. ISBN 0060958332.

M. Pütz and M. Verspoor. Explorations in linguistic relativity. John BenjaminsPublishing Company, 2000. ISBN 9027237069.

47

Quijada, J. A Philosophical Grammar of Ithkuil, a Constructed Language.http://www.ithkuil.net - (Retrieved March 27th, 2011), 2004.

V.S. Ramachandran and E.M. Hubbard. Synaesthesia–a window into percep-tion, thought and language. Journal of Consciousness Studies, 8(12):3–34, 2001.ISSN 1355-8250.

Riner, R.D. LOGLAN and the Option of Clarity. ETC: A Review of GeneralSemantics, 3(47):269–280, 1990.

E. Sapir and D.G. Mandelbaum. Selected writings of Edward Sapir: in lan-guage, culture and personality. University of California Press, 1949. ISBN0520011155.

F. Saussure. Cours de linguistique générale, publié par Charles Bally et AlbertSéchehaye avec la collaboration de Albert Riedlinger. Lausanne et Paris, Payot,1916.

A. Schaff, N. Chomsky, and O. Wojtasiewicz. Language and cognition. McGraw-Hill, 1973. ISBN 0070550808.

D.N. Sedley. Plato’s Cratylus. Cambridge Univ Pr, 2003. ISBN 0521584922.

L. Selinker. Interlanguage. IRAL-International Review of Applied Linguistics inLanguage Teaching, 10(1-4):209–232, 1972. ISSN 0019-042X.

D.M. Singleton and Z. Lengyel. The age factor in second language acquisition: Acritical look at the critical period hypothesis. Multilingual Matters Ltd, 1995.ISBN 185359301X.

D.I. Slobin. From “thought and language” to “thinking for speaking”. Rethink-ing linguistic relativity, 17:70–96, 1996.

G. A. Stam. Can an L2 Speaker’s Patterns of Thinking for Speaking Change?Linguistic Relativity in Second Language Acquisition: Evidence of Thinking forSpeaking - Han Z.-H, Cadierno T, eds.(In press) Multilingual Matters, 2010.

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48

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49

Index

R2 values, 322-ary predicates, 273-ary predicates, 27

absolute-frame, 26adulthood, 17Afrikaans, 32Amazon, 10Arabic, 42Aristotle, 5Athanasopoulos, Panos, 21, 42, 43

Babel-17, 11Babylonians, 5beauty, 41behavior centered, 25Belfast, 27Berlin and Kay - colour acquisition, 27Berman, Ruth, 10bilingualism, 16biological constraints, 18Boroditsky, Lera, 7, 8, 26, 41boundary crossing, 20Brazil, 10Brother, Big, 11Brown, James C., 11Brown, Roger, 12

Cadierno, Teresa, 20, 42Celtic languages, 27, 32chess, 30Chinese, 9Chomsky, Noam, 14, 41chroma, 28

cognitive process, 2, 20colour continuum, 25, 27colour spectrum, 13, 25, 26, 31, 33, 42Colour Terminology, 27colourblindness, 27comic strip, 22communication breakdown, 17comprehensibility, 17computer based, 29computer programming, 10conceptual difference, 21cone, 28, 29containment relation, 21Corder, S.P., 17count noun, 8Coventry, Kenny, 21Cratylus, 5Critical Period Hypothesis, 17culture, 41

Danish, 20, 21data collection, 29database, I, IIde Saussure, Ferdinand, 5, 6decoding, 17Delany, Samuel R., 11domain centered, 26Drivonikou, Gilda, 13Dublin, 27Dutch, 26, 32

electromagnetic waves, 28elephant, 8Elo Algorithm, 30, 32

50

Elo algorithm, 30empirical research, 2empty, 7encoding, 17English, 8–11, 16, 20–22, 25, 26, 32,

41, 42Esperanto, 11Estonian, 25Everett, Daniel, 8, 10

Facebook, 30Facemash, 30Finland, 26Finnish, 25, 41, 42first-hand, 9football, 30foreign accent, 18foreign grammar rules, 43foreign language, 2foreign language literature, 17foreign lexicon, 43fossilisation, 17, 18France, 31French, 9, 16, 32Frog Story Project, 10

gasoline drums, 7German, 10, 20, 21, 32Germanic languages, 32gerund, 23Gestalt, 25gesture, 9Goidelic languages, 32Google, Xgrammar, 17grammaticality judgement, 22green-blue, 27green-blue continuum, XIII, 30green-blue spectrum, 2

Hermogenes, 5hexadecimal, VI

Hjelmslev, 5Hoijer, 7Hopi, 14horizontal, 9HTML, Ihue, 28human behaviour, 3, 5human cognition, 11human experience, 41Hungarian, 25

Iberia, 36identicality, 14illuminated surroundings, 29immersive environment, 16index, 17Indo-European, 25, 32Indonesian, 41, 42infra-red, 28input, 16interlanguage, 16, 17internet, 29intrinsic-frame, 27Irish, 20, 27, 32Italian, 32, 42Italy, 31Ithkuil, 11

Japanese, 21, 42

K coefficient, 31Klinkenberg, 5Kozlowski, S., 11Krashen, Stephen, 16

L1, 21, 22, 32L2, 16, 20–23language, 25, 41language acquisition, 10language and thought, 6, 10language transfer, 20language window, 17

51

learner language, 16Lenneberg, Eric, 12, 17linear regression, 32Linguistic Determinism, 2, 6, 14, 33,

41, 42Linguistic Relativity, 2, 5, 6, 10–14, 20,

21, 23, 27, 33, 41, 42linguistic relativity, 21Loglan, 11, 12long term memory, 9Long, M., 17loop, IIILucy, John, 6–8, 21, 25, 26, 41

malleable, 8Mandarin, 9, 42manner, 22manner-of-motion verb, 21mass noun, 8material, 8mathematics, VIIImediation, 13memory, 41mental compass, 26Mexico, 27midpoint, VImode of thinking, 10monoglot native speaker, 22monolingual speaker, 22multilingual, 2multilingualism, 16MyISAM, I

native language, IInative speaker, 20, 21, 42native-like proficiency, 18neural networks, 26neuron, 29Newspeak, 11Newton, Sir Isaac, 28Nineteen Eighty-Four, 11

nomos, 5non-plural-marking language, 20non-pro-drop languages, 20Norwegian, 25number, 22numeracy, 10

obstacle, 42one, two many language, 10Orwell, George, 11outer nuclear layer, 29

parameter resetting, 43partative, 9path, 22Paul Kay, 26peanuts, 8Pederson, E., 21, 26photoreceptor cells, 29PHP, I, VIphysiology, 14physis, 5picture description, 20pidgin, 16Pinker, Steven, 2, 14, 41Pirahã language, 10Pirahã people, 10Plato, 5plural-marking language, 20Portuguese, 32primary key, IIprism, 28pro-drop languages, 20programming language, 12Proto-Celtic, 28psychology, 22puberty, 17

QPT, 22quantificational unit, 8Quijada, John, 11

52

reading, 17red, 28relational model, Irelative-frame, 26retina, 28, 29RGB, II, III, VI, 32, 36rod-cells, 29Romance languages, 21, 32Russian, 20, 21, 42

Sapir, Edward, 6, 7Sapir-Whorf, 6, 7, 12, 13satellite-framed language, 20–22saturation, 28Science Fiction, 10score, 32Second Language Acquisition, 2, 16, 17,

20, 42second language learners, 21Selinker, L., 17semiotics, 5server, IIsession, IIshape, 8sharp downward kick, 34signified, 5signifier, 5Singleton, David, 17Slobin, Daniel I., 8, 10, 20, 41social media, IIISocrates, 5Spanish, 10, 16, 20–22, 32spatial relations, 21, 25, 26speaking, 17SQL, IStam, Gale, 22, 42structure centered, 25Strømnes, Frode, 25, 41substance, 22support relation, 21survey, 29

Sweden, 26Swedish, 25, 41Sylvester and Tweety Bird, 22synesthaesia, 5

target language, 17tensing, 9Tepehua, 27thinking for speaking, 10, 22, 23, 41–

43thought, 41Totonacan language, 27transition, 30translate, 9trend line, 33Turkish, 9, 10Tzeltal, 26

ultimate confusion, 35ultra-violet, 28Universal Grammar, 43Ural-Altaic, 25USA, 22

verb-framed language, 20–22vertical, 9violet, 28vocabulary production, 20vocabulary recognition, 20von Humboldt, Wilhelm, 5, 6

website, 42weighted data, 33Welsh, 27Whorf, Benjamin L., 2, 6, 7, 10, 12, 41writing, 17

Yucatec Maya, 8, 25

Zuckerberg, Mark, 30

53

Appendix A

Website Code

In this appendix will be the SQL and PHP code related to the website usedto run the expreiment. Overall it consisted of a MySQL database along withseveral PHP/HTML files. There were many more files on the website than arerelevant to the data gathering process, such as those pages on which anyonecould check up on the current statistics.

A.1 Database Structure

The database contains 2 two table templates, one representing each individualcomparison of 2 colours, and the other, which is replicated over 80 times, rep-resents the information about each individual colour. It is, of course, a databsein the relational model.

A.1.1 Battles

The battles table represents the results of each individual choice made by a user,registering the colour on which they clicked as the winner, the other colouras the loser and the language code of the language being analysed in as thelanguage. The auto incrementing attribute battle_id serves as the uniqueidentifier of each tuple. The default characterset is explicitly defined as UTF-8and the AUTO_INCREMENT value is set to 1 (and is used in conjunction with thecolour_id attribute. MyISAM, which is the default engine in MySQL is explicitlyset out also.CREATE TABLE IF NOT EXISTS battles(

battle_id bigint(20) unsigned NOT NULL auto_increment,

winner bigint(20) unsigned NOT NULL,

loser bigint(20) unsigned NOT NULL,

language varchar(2) NOT NULL,

PRIMARY KEY(battle_id),

I

KEY winner(winner)

) ENGINE=MyISAM DEFAULT CHARSET=utf8 AUTO_INCREMENT=1;

A.1.2 Language

There are 81 tables representing the languages to be analysed, all of them fol-lowing the template outlined below. Essentially, what is represented is eachcolour in the language. Each colour has a colour_id attribute which containsthe unique ID of each colour for that language, which is the primary key. Theg and b attributes represent the GB in that colour’s RGB value (there is no Ras there is no red in the green-blue spectrum). The wins and losses attributesstore how many times that particular colour, in that particular language haswon or lost, respectively, a ‘battle’. Finally, the score attribute stores the coloursscore and is updated after every battle. The theory being that the colour withthe value closest to the default value of 1500 would be the dividing point in thecontinuum.CREATE TABLE IF NOT EXISTS language(

colour_id bigint(20) unsigned NOT NULL auto_increment,

g i n t NOT NULL,

b i n t NOT NULL,

score i n t (10) unsigned NOT NULL default ’ 1500 ’,wins i n t (10) unsigned NOT NULL default ’ 0 ’,losses i n t (10) unsigned NOT NULL default ’ 0 ’,PRIMARY KEY(colour_id)

) ENGINE=MyISAM DEFAULT CHARSET=utf8 AUTO_INCREMENT=1;

A.2 PHP classes

There are 13 files that are key to the operation of the site as a whole, but only6 of them are absolutely essential for the operation of the data gathering side.db-gen.php was used by me in order to set up the databases in a slightly moreautomated manner. This is it’s sole purpose, and for this reason (combined withsecurity risks caused by not doing so) it was removed from the server immedi-ately after its use. index.php is the front page of the site, providing informationabout the study, as well as a drop down menu for the user to select his or hernative language. From there, the site is directed to session.php, which beginsthe user’s session. It stores the user’s native language, and sets the time at whichthe session began, before redirecting to compare.php. In compare.php, the usermakes a decision between two colours. This choice is evaluated remotely byrate.php, which immediately afterwards will redirect the user back to a newinstance of compare.php. timeup.php is only acessed if a user’s session exceeds20 minutes in length. It terminates the session.

II

A.2.1 db-gen.php

Creates the array $shades and adds to it the colours in the spectum, in incre-ments of six (so as to keep reasonable the number of possible colour combina-tions on a page). The G and B values of each RGB can never be zero, so as toavoid dividing by zero in the operations performed later in rate.php. Then thebattles table is created, followed by a loop which creates the tables for all ofthe languages as per the template in A.1.2.<?php

include( ’ mysql . php ’);

$langs = array( " af " , " ar " , " an " , " eu " , " be " , " bn " , " bh " , " bs " , " br " , " bg " , " es " , " ca " , " zh " , " hr" , " cs " , " da " , " nl " , " en " , " e t " , " f i " , " f r " , " ka " , " de " , " e l " , " he " , " hi " , "hu" , " id " , " ga " , "i l " , " i t " , " j a " , " ks " , " kk " , "km" , " ko " , "ku" , " lo " , " lv " , " l t " , " lb " , "mk" , "mg" , "ms" , "mt" ,"nn" , " oc " , " pa " , " fa " , " pl " , " ps " , " pt " , "rm" , " ro " , " ru " , " gd " , " s r " , " sk " , " s l " , " so " , " s t ", " su " , "sw" , " sv " , " t l " , " ty " , " tg " , " ta " , " th " , " t r " , "uk" , " ur " , " uz " , " v i " , " cy " , " fy " , " xh" , " y i " , " yo " , " za " , " zu ");

for($i = 1; $i < 256; $i = $i + 6){

$shades[] = " ( ’ ".$i. " ’ , ’ 2 5 3 ’ ) ";$shades[] = " ( ’ 2 5 3 ’ , ’ ".$i. " ’ ) ";

}

mysql_query( "CREATE TABLE IF NOT EXISTS b a t t l e s (b a t t l e _ i d b i g i n t ( 2 0 ) unsigned NOT NULL auto_increment ,winner b i g i n t ( 2 0 ) unsigned NOT NULL,l o s e r b i g i n t ( 2 0 ) unsigned NOT NULL,language varchar ( 2 ) NOT NULL,PRIMARY KEY( b a t t l e _ i d ) ,KEY winner ( winner )

) ENGINE=MyISAM DEFAULT CHARSET=utf8 AUTO_INCREMENT=1; ");

foreach($langs as $l){

mysql_query( "CREATE TABLE IF NOT EXISTS ".$l. " (colour_id b i g i n t ( 2 0 ) unsigned NOT NULL auto_increment ,g i n t NOT NULL,b i n t NOT NULL,score i n t ( 1 0 ) unsigned NOT NULL defaul t ’1500 ’ ,wins i n t ( 1 0 ) unsigned NOT NULL defaul t ’ 0 ’ ,l o s s e s i n t ( 1 0 ) unsigned NOT NULL defaul t ’ 0 ’ ,PRIMARY KEY( colour_id )

) ENGINE=MyISAM DEFAULT CHARSET=utf8 AUTO_INCREMENT=1 ; ");

$query = " INSERT INTO ".$l. " ( g , b ) VALUES ". implode( ’ , ’, $shades). " ";i f (!mysql_query($query)) {

pr int mysql_error();

}

}

?>

A.2.2 index.php

The site index. It has been described above, and is really quite simple. I includeshare buttons for popular social media sites in order to try to get the word out.

III

<!DOCTYPE html PUBLIC " −//W3C//DTD XHTML 1.0 T r a n s i t i o n a l //EN" " http : / /www. w3 . org /TR/xhtml1/DTD/xhtml1− t r a n s i t i o n a l . dtd " >

<html xmlns= " http : / /www. w3 . org /1999/xhtml " ><head>

< l i nk re l = " s t y l e s h e e t " type= " t e x t / c s s " href= " s t y l e . c s s " />

< l i nk re l = " shor tcut icon " href= " favicon . i c o " />

<scr ip t type= " t e x t / j a v a s c r i p t " >

function newPopup(url) {

popupWindow = window.open(

url,’popUpWindow’,’height=500,width=800,top=10’)

}

</script>

</head>

<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />

<title>Cross-Linguistic Colour Spectrum Analysis</title>

</head>

<body align="right" rightmargin="25%" leftmargin="25%">

<h1>Cross-Linguistic Colour Spectrum Analysis</h1>

<h2>Finding the Mid-Point between Green and Blue</h2>

<a href="#" onclick="javascript: newPopup(’./Participant.Information.Leaflet.pdf’);">

<u>Download a PDF of the Participant Information Leaflet <img src="pdf.png" alt="

PDF icon" width="16" height="16" /></u></a>

<p>Hi, and thanks for coming to this website that I have set up in order to conduct

research for my final year project, which investigates the potential implications

of <a href="http://en.wikipedia.org/wiki/Linguistic_relativity">linguistic

relativity</a> in the field of language learning. What this site is focussing on

is how different languages interpret the colour spectrum, more specifically, at

what point different languages split up green and blue.</p>

<p>To do this, I have divised an experiment based loosely upon the FaceMash website

created by Facebook founder Mark Zuckerberg. On the following page you will be

asked to click on the shade along the spectrum which you think is more close to

the "ideal" green. Sometimes both colours will appear blue to you. In this

instance, choose the one you think is more towards the green end of the spectrum.

If both are green, then click the one you think is "greener". If colours are too

close to call, the algorithm will already have marked the result as unimportant,

so it won’t matter which one you click. There’ll be a spectrum segment at the

bottom of the page showing you the current midpoint for your language. This isn’t

a reliable indication until there are a few hundred clicks for a language, and

please don’t try to base you decisions off of it!</p>

<p>If you are a native speaker of more than one language, please choose the one you

conduct your daily life through. However, if one of your native languages is

English, please choose English as reading this will have influenced you in

someway. The black-white-grey colour scheme was chosen to influence you as little

as possible.</p>

<p>Finally, I shall post the results of this study to this site at some point in

March for those of you that are interested. Meanwhile, you can look at the

current statistics.</p>

<p>So, thanks again. Even a few minutes of your time will make a big difference! If

you have any feedback, or your native language is not listed, please find my

details in the "Contact" section!</p>

-- Richard King, January 2011

<br /><br />

<center>

<form id="lang" name="lang" method="post" action="session.php">

<label>

<select name="lang" id="lang">

<option disabled>Please choose your native language:</option>

IV

<optgroup label="----------------">

<option value="af">Afrikaans</option>

<option value="ar">Arabic</option>

<option value="an">Aragonese</option>

<option value="eu">Basque</option>

<option value="be">Belarusian</option>

.

.

.

<option value="yo">Yoruba</option>

<option value="za">Zhuang</option>

<option value="zu">Zulu</option>

</optgroup>

<input type="submit" name="mysubmit" value="Begin!"/>

</select>

</label>

</form>

<script type="text/javascript">

(function() {

var s = document.createElement(’SCRIPT’), s1 = document.getElementsByTagName(’SCRIPT’

)[0];

s.type = ’text/javascript’;

s.async = true;

s.src = ’http://widgets.digg.com/buttons.js’;

s1.parentNode.insertBefore(s, s1);

})();

</script>

<table>

<tr>

<td align="right"><a href="http://twitter.com/share" class="twitter-share-button"

data-url="http://fyp.richardking.me" data-text="Check this out! Cross-Linguistic

Colour Spectrum Analysis" data-count="vertical">Tweet</a><script type="text/

javascript" src="http://platform.twitter.com/widgets.js"></script></td>

<td align="right"><a class="DiggThisButton DiggMedium" href="http://digg.com/submit?

url=http://fyp.richardking.me/"></a></td>

<td align="right"><script src="http://connect.facebook.net/en_US/all.js#xfbml=1"></

script><fb:like href="fyp.richardking.me" layout="box_count" show_faces="false"

width="0" action="recommend" font="trebuchet ms"></fb:like></td>

<td align="right"><script type="text/javascript" src="http://reddit.com/static/button

/button2.js"></script></td>

<td align="right"><script src="http://www.stumbleupon.com/hostedbadge.php?s=5"></

script></td>

<td align="right"><a title="Post to Google Buzz" class="google-buzz-button" href="

http://www.google.com/buzz/post" data-button-style="normal-count"></a>

<script type="text/javascript" src="http://www.google.com/buzz/api/button.js"></

script></td>

</tr>

</table>

<br />

&nbsp;

<br />

<a href="index.php">Home</a> | <a href="contact.php">Contact</a> | <a href="curr.php"

>Current Data</a> | <a href="priv.php">Privacy</a> | <a href="code.php">Code</a>

</center>

</body>

</html>

V

A.2.3 session.php

This simple script sets the language value to a session variable and also setsthe clock for the 20 minute time allowance, before redirecting the browser tocompare.php.<?php

session_start();

$_SESSION[’lang’] = $_POST[’lang’];

$_SESSION[’start’] = time();

header(’location: ./compare.php’);

?>

A.2.4 compare.php

Initially, the script verifies that a valid session is in operation, and if not redi-rects the browser to the site index. It then verifies that the session has notbeen going on longer than 20 minutes , and if it has, redirects the browser totimeup.php. The rgb2html function converts a set of RGB values to its hex-adecimal equivalent. After this comes the random selection of 2 colours. Afterthis is the calculation of which colour is the midpoint, as described in 5.4. Thencomes the creation of the table used to represent the spectrum, with the pointcorresponding to the midpoint to be set to black. The last thing the PHP scriptdoes is calculate the total number of clicks performed for that language, doingso by summing the total number of wins for each language.<?php

session_start();

if(!(isset($_SESSION[’start’])) || !(isset($_SESSION[’lang’]))){

header(’location: ./index.php’);

}

if((time() - $_SESSION[’start’]) > 1199){

header(’location: ./timeup.php’);

}

include(’mysql.php’);

function rgb2html($r, $g=-1, $b=-1)

{

if (is_array($r) && sizeof($r) == 3)

list($r, $g, $b) = $r;

$r = intval($r); $g = intval($g);

$b = intval($b);

$r = dechex($r<0?0:($r>255?255:$r));

$g = dechex($g<0?0:($g>255?255:$g));

$b = dechex($b<0?0:($b>255?255:$b));

VI

$color = (strlen($r) < 2?’0’:’’).$r;

$color .= (strlen($g) < 2?’0’:’’).$g;

$color .= (strlen($b) < 2?’0’:’’).$b;

return $color;

}

$result = @mysql_query("SELECT * FROM ".$_SESSION[’lang’]." ORDER BY RAND() LIMIT 0,2

");

while($row = mysql_fetch_object($result)) {

$images[] = (object) $row;

}

$colour1 = rgb2html(0, $images[0]->g, $images[0]->b);

$colour2 = rgb2html(0, $images[1]->g, $images[1]->b);

$result = mysql_query("SELECT * FROM ".$_SESSION[’lang’]." ORDER BY ABS(score - 1500)

LIMIT 1");

while($row = mysql_fetch_object($result)) $mid[] = (object) $row;

$g = $mid[0]->g;

$b = $mid[0]->b;

$mid_point = ($b < $g) ? 350 : 0;

$mid_point = round($mid_point + (1.3671875 * min($b, $g))) - 1;

$tables = "<table border=’0’ cellpadding=’0’ cellspacing=’0’><tr>";

for($iter = 0; $iter < 100; $iter = $iter + 1){

$color = "";

if($iter < 50){

$color = rgb2html(0, ($iter / 0.1953125), 255);

}else{

$color = rgb2html(0, 255, ((100 - $iter) / 0.1953125));

}

if($iter == round($mid_point/7)) {

$color = "#000000";

}

$tables = $tables."<td bgcolor=’".$color."’ width=’7’ height=’45’>";

$tables = $tables."</td>";

}

$tables = $tables."</tr></table>";

$res = @mysql_query("SELECT * FROM ".$_SESSION[’lang’].";");

while($row = mysql_fetch_object($res)) $colours[] = (object) $row;

$sum = 0;

foreach($colours as $c){

$sum = $sum + $c->wins;

}

mysql_close();

?>

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/

xhtml1/DTD/xhtml1-transitional.dtd">

<html xmlns="http://www.w3.org/1999/xhtml">

<head>

<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />

VII

<link rel="stylesheet" type="text/css" href="style.css" />

<title>Colour Comparison</title>

</head>

<body leftmargin="20%" rightmargin="20%" topmargin="10px">

<center>

<h1>ColourMash!</h1>

Click the colour you think is greener!

<br /><br />

<table border="0" cellpadding="0" cellspacing="25">

<tr>

<td bgcolor="#<?=$colour1?>" valign="top" width="300" height="300" style="cursor:

pointer;" onclick="document.location.href=’rate.php?w=<?=$images[0]->

colour_id?>&l=<?=$images[1]->colour_id?>’;"></td>

<td bgcolor="#<?=$colour2?>" valign="top" width="300" height="300" style="cursor:

pointer;" onclick="document.location.href=’rate.php?w=<?=$images[1]->colour_id

?>&l=<?=$images[0]->colour_id?>’;"></td>

</tr>

</table>

<h1>Results so far:</h1>

<p>Remember! This display isn’t accurate until there’s been a few hundred clicks for

the language, so if it’s jumping around a lot, or not even there, don’t worry -

it’s just you’re one of the first people to use it!</p>

So far, there have been <b><?=$sum?></b> clicks for this language!<br />

<?=$tables?>

<br /><center>

<br /><br />

<a href="index.php">Home</a> | <a href="contact.php">Contact</a> | <a href="curr.php"

>Current Data</a> | <a href="priv.php">Privacy</a> | <a href="code.php">Code</a>

</center>

</body>

</html>

A.2.5 rate.php

This script performs the manipulations on the user’s choice of one colour overanother. The mathematics of it are described in 5.3. Initial tests are performedon the $_GET data to verify that it is not valid and non malicious.<?php

session_start();

include(’mysql.php’);

function k_calc($g1, $b1, $g2, $b2) {

if($g1 == 253 && $g2 == 253) {

if((($b1/6 * 0.95) < $b2/6) && ($b2/6 < ($b1/6 * 1.05))) {

return round(6 * (max($b1, $b2)/min($b1, $b2)));

}

if((($b1/6 * 0.85) < $b2/6) && ($b2/6 < ($b1/6 * 1.15))) {

return round(24 * (max($b1, $b2)/min($b1, $b2)));

}

if((($b1/6 * 0.35) < $b2/6) && ($b2/6 < ($b1/6 * 1.65))) {

return round(36 * (max($b1, $b2)/min($b1, $b2)));

}

return round(12 * (max($b1, $b2)/min($b1, $b2)));

}

VIII

if($b1 == 253 && $b2 == 253) {

if((($g1/6 * 0.95) < $g2/6) && ($g2/6 < ($g1/6 * 1.05))) {

return round(6 * (max($g1, $g2)/min($g1, $g2)));

}

if((($g1/6 * 0.85) < $g2/6) && ($g2/6 < ($g1/6 * 1.15))) {

return round(24 * (max($g1, $g2)/min($g1, $g2)));

}

if((($g1/6 * 0.35) < $g2/6) && ($g2/6 < ($g1/6 * 1.65))) {

return round(36 * (max($g1, $g2)/min($g1, $g2)));

}

return round(12 * (max($g1, $g2)/min($g1, $g2)));

}

$tmp = min($g1, $b1) + min($g2, $b2);

$max = 498;

if($tmp > ($max * 0.8)){

return 6;

}

if($tmp > ($max * 0.6)){

return 24;

}

if($tmp > ($max * 0.2)){

return 36;

}

return 12;

}

function expected($Rb, $Ra) {

return 1/(1 + pow(10, ($Rb-$Ra)/400));

}

function win($score, $expected, $k) {

return $score + $k * (1-$expected);

}

function loss($score, $expected, $k) {

return $score + $k * (0-$expected);

}

if ($_GET[’w’] && $_GET[’l’] && $_SESSION[’lang’]) {

if (is_numeric($_GET[’w’]) && is_numeric($_GET[’l’])) {

$result = mysql_query("SELECT * FROM ".$_SESSION[’lang’]." WHERE colour_id = ".

$_GET[’w’]." ");

$winner = mysql_fetch_object($result);

$result = mysql_query("SELECT * FROM ".$_SESSION[’lang’]." WHERE colour_id = ".

$_GET[’l’]." ");

$loser = mysql_fetch_object($result);

$kloseness = k_calc($winner->g,$winner->b,$loser->g,$loser->b);

$winner_expected = expected($loser->score, $winner->score);

IX

$winner_new_score = win($winner->score, $winner_expected, $kloseness);

mysql_query("UPDATE ".$_SESSION[’lang’]." SET score = ".$winner_new_score.", wins

= wins+1 WHERE colour_id = ".$_GET[’w’]);

$loser_expected = expected($winner->score, $loser->score);

$loser_new_score = loss($loser->score, $loser_expected, $kloseness);

mysql_query("UPDATE ".$_SESSION[’lang’]." SET score = ".$loser_new_score.",

losses = losses+1 WHERE colour_id = ".$_GET[’l’]);

mysql_query("INSERT INTO battles SET winner = ".$_GET[’w’].", loser = ".$_GET[’l’

].", language = ".$_SESSION[’lang’]." ");

}

header(’location: ./compare.php’);

}

?>

A.2.6 timeup.php

On this page, the session is unset, i.e. the start time and the user’s chosen lan-guage is dropped. This is done to prevent strain to a user’s eyes. They areoffered a redirect to Google.<?php

if(isset($_SESSION[’lang’])){

unset($_SESSION[’lang’]);

}

if(isset($_SESSION[’start’])){

unset($_SESSION[’start’]);

}

?>

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/

xhtml1/DTD/xhtml1-transitional.dtd">

<html xmlns="http://www.w3.org/1999/xhtml">

<head>

<link rel="stylesheet" type="text/css" href="style.css" />

<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />

<title>Time’s Up!</title>

</head>

<body topmargin="8px" leftmargin="28%" rightmargin="28%">

<h1>Time’s Up!</h1>

<h4>So that you don’t strain your eyes, this session has auto-terminated after

roughly 20 minutes.</h4>

<p>Thank you so much for your participation! If your eyes aren’t feeling tired,

please click <a href="./index.php">here</a> to start over.</p>

<p>Otherwise, feel free to come back at any point! The more data gathered, the better

! Meanwhile, happy surfing!</p>

<center>

<a href="http://www.google.com/"><img src="http://blogs.pcworld.com/staffblog/

archives/Google-Logo-350px.jpg" alt="Google" /></a>

<br />

&nbsp;

<br />

<a href="index.php">Home</a> | <a href="contact.php">Contact</a> | <a href="curr.php"

>Current Data</a> | <a href="priv.php">Privacy</a> | <a href="code.php">Code</a>

X

</center>

</body>

</html>

XI

Appendix B

Abbreviations

• CPH - Critical Period Hypothesis

• HTML - Hypertext Markup Language

• IL - Interlanguage

• L1 - Native language

• L2 - Second or subsequent language

• LD - Linguistic Determinism

• LR - Linguistic Realtivity

• PHP - PHP: Hypertext Preprocessor

• RGB - Red, Green, Blue: Parameters for identifying a colour.

• SQL - Structured Query Language

• SLA - Second Language Acquisition

• TL - Target language

XII

Appendix C

Comprehensive Results of the Study

In this appendix are tables representing interesting or important aspects of the datagathered during the Empirical Research part of this paper. The third part of this ap-pendix gives the entirety of the data gathered for each language, the second part detailsthe colour extremes of each language, and the first section gives the data for the lan-guage as to which point along the continuum was least easy to difinitively call green orblue, and thus won the title of “dividing point”.

C.1 Comparison of Dividing Points

The following tables contain the approximation attained from each language as to thepoint in the spectrum at which that particular language differentiates between greenand blue.

Green Blue Win Percentage253 253 57.5

Table C.1: The colour in the middle for English

Green Blue Win Percentage241 253 41.6

Table C.2: The colour in the middle for French

XIII

Green Blue Win Percentage205 253 50

Table C.3: The colour in the middle for Italian

Green Blue Win Percentage223 253 37.5

Table C.4: The colour in the middle for Spanish

Green Blue Win Percentage253 247 50

Table C.5: The colour in the middle for Portuguese

Green Blue Win Percentage253 247 72.5

Table C.6: The colour in the middle for Dutch

Green Blue Win Percentage247 253 52.6

Table C.7: The colour in the middle for Afrikaans

Green Blue Win Percentage253 223 45.9

Table C.8: The colour in the middle for Irish

C.2 Comparison of Extremes

The upper colour in each of these tables represent the colour which won the highestpercentage of ‘battles’ and is, thus, the extreme green colour for it’s language. Similarly,the colour on the bottom is the colour which lost the greatest percentage of ‘battles’, andis, accordingly, the extreme blue for it’s language.

Green Blue Win Percentage253 19 93.3

7 253 3.2

Table C.9: The colour extremes for English

XIV

Green Blue Win Percentage253 7 100

1 253 4.3

Table C.10: The colour extremes for French

Green Blue Win Percentage253 1 10055 253 4.8

Table C.11: The colour extremes for Italian

Green Blue Win Percentage253 1 100

1 253 0

Table C.12: The colour extremes for Spanish

Green Blue Win Percentage253 253 10031 253 0

Table C.13: The colour extremes for Portuguese

Green Blue Win Percentage253 7 97.7

1 253 0

Table C.14: The colour extremes for Dutch

Green Blue Win Percentage253 1 10049 253 0

Table C.15: The colour extremes for Afrikaans

Green Blue Win Percentage253 49 10031 253 0

Table C.16: The colour extremes for Irish

C.3 Language Tables

In this section, there is a table for each language, detailing the data colleceted in orderto perform the analysis. Please feel free to do your own analysis on it!

XV

Table C.17: All data gathered for English

Green Blue Score Wins Losses253 19 2374 153 11253 31 2300 147 12253 7 2617 133 11253 49 2023 148 13253 25 2440 130 12253 1 2705 149 15253 43 2224 139 14253 37 2232 143 15253 55 2104 143 15253 13 2427 144 16253 61 2118 118 17253 73 2023 160 25253 79 2205 130 22253 115 1896 132 27253 97 1988 119 25253 85 2055 140 32253 67 1764 126 30253 91 1242 142 35253 121 2033 133 34253 103 1932 127 36253 127 1886 125 36253 133 1893 130 39253 109 1862 95 32253 139 1889 125 44253 157 1784 103 39253 145 1903 118 50253 151 1874 99 42253 163 1806 112 48253 181 1664 83 49253 193 1559 93 56253 169 1739 91 56253 253 1672 107 69253 205 1503 89 63253 211 1455 90 64253 223 1665 91 65253 187 2704 88 65253 199 1587 82 61

XVI

Table C.17: All data gathered for English (continued)

Green Blue Score Wins Losses253 217 1606 86 66253 247 1467 95 73253 175 1965 88 68253 253 1573 96 81217 253 1627 77 74253 235 1648 77 75241 253 1686 79 77253 241 1410 80 78253 229 1461 72 74205 253 1503 78 88235 253 1594 69 80247 253 1679 68 81229 253 1726 72 88223 253 1584 70 90211 253 361 64 96199 253 1569 64 98175 253 1431 63 110181 253 1376 49 87169 253 1360 58 104193 253 1453 49 90187 253 1427 58 109157 253 1315 56 113139 253 1178 51 107163 253 1298 44 123133 253 1319 34 99121 253 2228 38 112151 253 1241 39 116127 253 1118 39 133145 253 1201 33 115103 253 1057 35 13679 253 1115 29 11385 253 1140 32 12773 253 1009 29 122115 253 976 29 14091 253 966 24 12097 253 916 23 12067 253 910 24 13149 253 839 18 127

XVII

Table C.17: All data gathered for English (continued)

Green Blue Score Wins Losses109 253 362 17 13761 253 996 16 1301 253 545 16 13543 253 853 14 12919 253 869 15 14255 253 906 16 15437 253 1086 14 15413 253 761 13 15531 253 593 12 14825 253 697 7 1387 253 374 5 153

Table C.18: All data gathered for Afrikaans

Green Blue Score Wins Losses253 1 2536 22 0253 7 1725 10 0253 31 1680 17 1253 25 1749 15 1253 43 1715 15 1253 55 1711 15 1253 73 1640 15 1253 97 1605 9 1253 115 1620 13 2253 61 1683 24 4253 13 1749 10 2253 37 1658 14 3253 85 1670 23 5253 19 1660 20 5253 169 979 12 3253 139 1660 19 5253 127 1576 11 3253 49 1678 14 4253 103 1605 19 6253 79 1590 9 3253 151 1588 14 5253 145 1641 16 6

XVIII

Table C.18: All data gathered for Afrikaans (contin-ued)

Green Blue Score Wins Losses253 109 1608 13 5253 157 1591 10 4253 67 1530 12 5253 181 1527 7 3253 253 1634 15 7253 229 1518 13 7241 253 1680 11 6253 187 1554 14 8253 133 1517 10 6253 253 1548 10 6253 247 1521 8 5253 175 1580 12 8253 241 1486 13 9235 253 1657 10 7253 91 1561 7 5253 121 1482 9 7253 205 1509 9 7253 163 1470 11 9193 253 1601 12 10253 199 1470 9 8253 193 1483 10 9247 253 1595 10 9223 253 1631 12 12253 223 1444 11 12253 217 1440 9 10211 253 1584 8 9253 211 1405 7 8187 253 1591 6 7175 253 1535 10 12181 253 1591 8 10217 253 1528 8 10199 253 1589 8 12151 253 1474 7 11205 253 1568 7 11145 253 1484 5 8253 235 1378 6 10157 253 1482 4 8

XIX

Table C.18: All data gathered for Afrikaans (contin-ued)

Green Blue Score Wins Losses229 253 1460 5 11139 253 1436 4 9133 253 1960 6 1497 253 1388 5 12127 253 1413 5 12121 253 1385 6 1579 253 1643 3 9163 253 1454 5 15103 253 1398 4 1319 253 1285 3 13109 253 1368 2 9169 253 1426 3 1461 253 1335 3 16115 253 1364 2 1213 253 1174 3 1943 253 1176 4 261 253 1098 2 1467 253 1331 2 1485 253 1351 2 1491 253 1234 3 237 253 1029 2 1837 253 1305 1 1325 253 1290 1 1473 253 1301 1 1631 253 1319 0 1349 253 1183 0 2555 253 330 0 19

Table C.19: All data gathered for Dutch

Green Blue Score Wins Losses253 7 2051 43 1253 31 2000 49 2253 43 1920 48 2253 1 2365 43 2253 61 1789 29 2

XX

Table C.19: All data gathered for Dutch (continued)

Green Blue Score Wins Losses253 19 1883 39 3253 73 1817 34 3253 13 1962 41 4253 79 1788 36 4253 67 1830 33 4253 37 1704 36 5253 139 1713 32 5253 109 1737 33 6253 91 1831 51 10253 169 1716 34 7253 97 1755 38 8253 115 1758 33 7253 49 2083 37 8253 25 1796 30 7253 103 1673 36 9253 127 1697 28 7253 55 1596 33 9253 151 1655 29 9253 85 1849 25 8253 187 1664 28 9253 133 1628 33 12253 199 1587 38 14253 247 1490 29 11253 163 1593 30 12253 157 1603 36 16253 121 1584 25 12253 181 1572 22 12253 205 1480 27 15253 229 1420 37 24253 253 1569 25 18253 235 1410 22 17229 253 1628 23 18253 145 1522 19 15253 217 1460 24 20253 241 1410 29 25253 193 1161 31 28253 175 1533 22 21253 223 1401 21 21

XXI

Table C.19: All data gathered for Dutch (continued)

Green Blue Score Wins Losses217 253 1616 28 28205 253 1579 29 29253 253 1539 27 28253 211 1476 19 20235 253 1613 18 21241 253 1623 22 26211 253 1556 19 23175 253 1439 19 30247 253 1604 17 27223 253 1547 13 24139 253 1441 15 30181 253 1644 19 39199 253 1466 16 33145 253 1443 11 2391 253 1349 10 22193 253 1471 12 28187 253 1438 12 29169 253 1429 9 22157 253 1431 14 3579 253 1258 14 39115 253 1321 11 32109 253 1330 10 34163 253 1389 9 31151 253 1352 9 31133 253 1360 11 4097 253 1219 9 33121 253 1245 9 3873 253 1325 8 3555 253 1230 8 3667 253 1215 7 36103 253 1227 6 3843 253 1205 5 3361 253 1181 6 4131 253 1117 5 37127 253 1181 5 4313 253 991 4 4537 253 1149 3 3525 253 1037 4 49

XXII

Table C.19: All data gathered for Dutch (continued)

Green Blue Score Wins Losses85 253 1156 3 3819 253 991 3 507 253 880 2 3549 253 1028 2 431 253 226 0 32

Table C.20: All data gathered for French

Green Blue Score Wins Losses253 7 1976 24 0253 19 1837 19 0253 25 1832 21 0253 37 1690 14 0253 79 1697 23 1253 1 2371 22 2253 91 1760 30 3253 97 1684 20 2253 109 1692 19 2253 67 1715 18 2253 43 2000 23 3253 61 1724 23 3253 49 1697 22 3253 121 1713 19 3253 13 1790 11 2253 55 1644 15 3253 139 1612 14 3253 145 1674 22 5253 115 1560 15 4253 31 1597 10 3253 73 1594 8 3253 163 1080 16 6253 103 1586 12 5253 85 1584 7 3253 127 1551 13 6253 151 1632 15 7253 223 1495 10 5253 169 1525 13 7

XXIII

Table C.20: All data gathered for French (continued)

Green Blue Score Wins Losses253 175 1499 13 7253 133 1584 11 6253 157 1518 7 4253 253 1557 12 7253 181 1516 12 8247 253 1682 10 7253 217 1473 15 12253 187 1437 13 11253 229 1406 13 12199 253 1574 9 9253 247 1429 8 8187 253 1582 10 11253 241 1425 9 10127 253 1513 8 9253 193 1436 12 14217 253 1440 10 12253 205 1446 9 11253 235 1408 10 13181 253 1519 6 8193 253 1544 8 11253 211 1396 5 7241 253 1528 5 7169 253 1478 11 16115 253 1488 6 9229 253 1579 10 15253 253 1458 6 9253 199 1189 7 11205 253 1493 10 16151 253 1494 8 14235 253 3005 9 16223 253 1560 5 9211 253 1545 6 12175 253 1546 5 1297 253 1378 7 1755 253 1317 5 14121 253 1450 6 1767 253 1379 4 1249 253 1355 4 14

XXIV

Table C.20: All data gathered for French (continued)

Green Blue Score Wins Losses163 253 1398 3 1343 253 1286 3 15109 253 1340 3 16157 253 1370 3 16133 253 1339 4 2313 253 1179 3 1891 253 1324 3 1873 253 1271 2 1379 253 1324 2 15145 253 1346 2 1625 253 1194 2 18103 253 1238 2 1885 253 1292 2 2031 253 1177 1 137 253 1129 1 1419 253 1256 1 1537 253 1242 1 18139 253 1363 1 1861 253 1195 1 211 253 0 1 22

Table C.21: All data gathered for Italian

Green Blue Score Wins Losses253 1 2412 12 0253 61 1788 26 1253 7 1949 17 1253 73 1680 15 1253 121 1759 29 2253 13 1928 27 2253 127 1628 11 1253 19 1803 19 2253 25 1719 15 2253 103 1661 14 2253 133 1646 20 3253 31 1622 12 2253 55 1679 18 3

XXV

Table C.21: All data gathered for Italian (continued)

Green Blue Score Wins Losses253 67 1633 16 3253 43 1664 18 4253 85 1658 17 4253 91 1660 12 3253 79 1578 15 4253 97 1672 20 6241 253 1676 16 5253 109 1591 14 5253 139 1547 24 9253 37 1658 10 4253 115 1526 18 8253 181 1504 12 6253 151 1529 13 7253 163 1582 13 7253 49 1537 7 4253 199 1498 12 7253 235 1519 10 6253 145 911 9 6253 169 1535 12 8181 253 1579 9 6253 211 1458 10 7247 253 1715 5 4211 253 1527 11 9217 253 1551 7 673 253 850 11 10253 175 1519 11 10253 193 1499 13 13205 253 146 9 9253 223 1548 6 6253 253 1483 7 7163 253 1518 10 11199 253 1423 10 11229 253 1562 8 9253 157 1442 9 11187 253 1604 9 11253 205 1447 9 11253 217 1394 8 10169 253 1469 7 10

XXVI

Table C.21: All data gathered for Italian (continued)

Green Blue Score Wins Losses253 241 1427 7 10253 247 1364 13 19253 229 1397 6 9115 253 1456 6 10139 253 2077 6 1097 253 1415 7 12127 253 1426 8 14175 253 3344 6 12235 253 1494 5 10253 187 1393 10 2485 253 1410 5 13223 253 1501 8 2379 253 2214 5 1531 253 1376 4 13253 253 1381 3 10103 253 1342 5 17145 253 1402 4 1549 253 1267 6 2543 253 1291 4 18109 253 1369 3 14121 253 1378 3 147 253 1271 4 1937 253 1305 4 20133 253 1410 2 1019 253 1257 2 111 253 17 2 13193 253 1334 3 2091 253 1344 2 1461 253 1170 3 2567 253 1277 1 11157 253 1386 1 1125 253 1246 1 1313 253 1159 1 15151 253 1325 1 1655 253 1289 1 20

XXVII

Table C.22: All data gathered for Spanish

Green Blue Score Wins Losses253 7 1726 11 0253 31 1685 8 0253 37 1644 8 0253 1 2183 7 0253 49 1628 7 0253 25 1652 6 0253 43 1596 5 0253 139 1561 5 0253 91 1563 4 0253 121 1551 3 0253 13 1545 2 0253 79 1525 2 0253 73 1650 11 1253 67 1586 7 1253 127 1596 6 1253 181 1620 15 3253 133 1589 9 2253 61 1549 8 2253 115 1553 8 2253 217 1503 8 2217 253 1717 4 1253 253 1599 4 1229 253 1646 6 2253 103 1539 6 3253 145 1524 6 3253 229 1469 6 3253 199 1490 4 2211 253 1524 4 2253 97 1506 2 1253 151 1552 5 3253 175 1503 6 4253 235 1482 6 4253 85 1511 3 2253 157 1526 3 2199 253 1554 4 3241 253 1592 4 3253 193 1498 5 4

XXVIII

Table C.22: All data gathered for Spanish (continued)

Green Blue Score Wins Losses253 223 1478 7 6253 55 1487 4 4253 109 977 4 4169 253 1553 3 3253 187 1525 3 3235 253 1530 2 2253 19 1502 1 1181 253 1563 1 1139 253 1500 3 4151 253 1486 3 4253 163 1507 3 4193 253 1500 3 4253 211 1478 3 4133 253 1516 4 6253 205 1486 2 3223 253 1459 3 5253 247 1385 3 549 253 1394 4 755 253 1431 4 8205 253 1511 3 685 253 1508 2 4121 253 1485 2 4175 253 1494 1 2253 253 1481 1 2127 253 2126 2 5109 253 1458 1 3145 253 1484 1 3253 241 1393 2 743 253 1434 1 4247 253 1462 1 5103 253 1418 2 1167 253 1385 1 6115 253 1443 1 6187 253 1462 1 77 253 1211 1 819 253 1368 1 9157 253 1404 1 9163 253 1379 1 10

XXIX

Table C.22: All data gathered for Spanish (continued)

Green Blue Score Wins Losses1 253 653 0 913 253 1219 0 897 253 1365 0 837 253 1386 0 725 253 1384 0 631 253 1393 0 561 253 1366 0 591 253 1406 0 573 253 1457 0 379 253 1483 0 1253 169 1488 0 1

Table C.23: All data gathered for Portuguese

Green Blue Score Wins Losses253 13 1821 17 0253 19 1692 12 0253 31 1698 12 0253 49 1741 19 0253 79 1649 12 0253 73 1727 18 1253 109 1659 18 1253 37 1687 12 1253 7 1979 10 1253 67 1705 18 2253 1 2461 8 1253 43 1619 8 1253 61 1650 15 2253 91 1652 15 2253 97 1718 12 2253 169 1583 12 2253 199 1592 11 2253 25 1677 13 3253 103 1614 12 3253 55 1548 6 2253 85 1606 9 3253 163 1504 9 3

XXX

Table C.23: All data gathered for Portuguese (contin-ued)

Green Blue Score Wins Losses253 181 1538 9 3253 193 1486 6 2253 229 1482 11 4253 121 329 8 3253 175 1501 8 3253 151 3032 10 4253 157 1577 10 4253 115 1541 7 3253 253 1609 8 5253 145 1497 9 7253 133 1506 7 6253 217 1473 7 6157 253 1561 8 7229 253 1555 8 7211 253 1652 10 9253 127 1499 4 4253 205 1473 7 7223 253 1582 4 4253 247 1445 4 4199 253 1559 7 8217 253 1565 6 7253 241 1421 6 7253 211 1450 5 6253 223 1498 5 6253 187 1496 4 5193 253 1555 4 5175 253 1501 3 4253 139 1412 4 6181 253 1518 6 9253 253 1505 6 10145 253 1483 3 6253 235 223 2 4205 253 1496 5 11109 253 1542 4 9163 253 1511 4 9187 253 1475 3 7121 253 1487 4 10

XXXI

Table C.23: All data gathered for Portuguese (contin-ued)

Green Blue Score Wins Losses247 253 1518 3 8139 253 1430 5 1485 253 1402 3 9241 253 1477 3 9103 253 1399 4 1319 253 1349 2 1097 253 1349 2 1091 253 1375 2 1237 253 1313 2 14169 253 1419 1 855 253 1340 1 949 253 1346 1 111 253 764 1 1267 253 1558 1 12115 253 1396 1 1279 253 1331 1 1343 253 1254 1 1461 253 1329 1 15133 253 1354 1 167 253 1189 0 713 253 1237 0 1225 253 1281 0 1131 253 1302 0 1373 253 1418 0 6127 253 1392 0 7151 253 1420 0 4235 253 1441 0 6

Table C.24: All data gathered for Irish

Green Blue Score Wins Losses253 1 2921 3 0253 7 1826 6 0253 19 1608 5 0253 37 1621 7 0253 49 1683 13 0

XXXII

Table C.24: All data gathered for Irish (continued)

Green Blue Score Wins Losses253 85 1613 8 0253 115 1582 7 0253 151 1608 7 0253 169 1542 3 0253 67 1654 15 1253 61 1635 10 1253 31 1656 9 1253 13 1617 8 1253 91 1592 7 1253 97 1580 7 1253 79 1557 6 1253 133 1583 11 2253 43 1640 10 2253 55 1610 10 2253 73 1634 13 3253 109 1587 13 3253 25 1644 8 2253 163 1584 8 2253 121 1561 7 2253 145 1545 7 2253 139 1546 6 2253 211 1495 5 2253 241 1524 7 3253 103 1502 4 2199 253 1555 2 1253 199 1486 6 3205 253 1582 8 4253 247 1498 10 5253 175 1484 7 4253 127 1447 6 4181 253 1597 6 4253 181 1399 7 5253 235 1455 7 579 253 1493 5 5253 187 1485 7 7247 253 1559 5 5133 253 1525 5 6223 253 1649 5 6

XXXIII

Table C.24: All data gathered for Irish (continued)

Green Blue Score Wins Losses253 253 1502 5 6193 253 1499 3 4253 223 1476 3 4229 253 1515 3 4253 253 1468 3 41 253 435 2 3109 253 1474 4 6253 157 1433 2 3187 253 1514 2 3163 253 1490 4 8253 193 1397 3 6253 205 1455 3 6211 253 3299 1 2253 217 1449 2 4157 253 1502 5 12175 253 1482 2 5241 253 1559 3 861 253 1402 3 9103 253 1445 2 6121 253 1388 2 7253 229 91 1 47 253 1232 1 5115 253 1412 2 10127 253 1424 1 5139 253 1446 1 5145 253 1407 3 15151 253 1448 1 5217 253 1478 1 5235 253 1487 1 543 253 1442 1 667 253 1388 2 1285 253 1402 1 7169 253 1399 1 719 253 1316 1 813 253 1285 1 973 253 1377 1 1055 253 1328 1 1525 253 1202 0 9

XXXIV

Table C.24: All data gathered for Irish (continued)

Green Blue Score Wins Losses31 253 1348 0 1037 253 1034 0 749 253 1337 0 1091 253 1373 0 797 253 1363 0 8

XXXV