Linguistic Relativity and Its Potential
Implications in the Field of Second Language
Acquisition
Richard King
B.A. (Mod.) Computer Science, Linguistics, and a Language (French)
Final Year Project, April 2011
Supervisor: Dr. David Singleton
Declaration
I hereby declare that this thesis is entirely my own work and that it has not been
submitted as an exercise for a degree at any other university.
April 5, 2011
Richard King
i
Permission to Lend
I agree that the Library and other agents of the College may lend or copy this
thesis upon request.
April 5, 2011
Richard King
ii
Acknowledgements
First and foremost, I would like to thank Professor David Singleton, my supervisor, 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 course
of 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 been
invaluable.
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 was
largely based.
I would also like to thank my family for putting up with me, and being there
for me over the last four years, and indeed, the 18 before that. And particularly
my 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.
iii
Quotes
“The marvellous thing is that even in studying linguistics, we find that the universe
as 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 profound truth may well be another profound truth."
– Niels Bohr
“Pour examiner la vérité, il est besoin, une fois dans sa vie, de mettre toutes choses
en doute autant qu’il se peut.1 ”
– René Descartes
“One is always a long way from solving a problem until one actually has the answer."
– 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)
iv
Contents
Preamble
Declaration . . . . .
Permission to Lend
Acknowledgements
Quotes . . . . . . .
Contents . . . . . .
List of Figures . . .
List of Tables . . . .
.
.
.
.
.
.
.
i
i
ii
iii
iv
v
viii
ix
1 Introduction
1.1 Aims and Overview . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
2
2
2 Linguistic Relativity
2.1 Ancient Roots of the Hypothesis . . . . . . . . . . . . . . . . . . .
2.2 19th and Early 20th Centuries - The Beginnings of Modern Thinking on Linguistic Relativity . . . . . . . . . . . . . . . . . . . . . .
2.2.1 Benjamin Lee Whorf . . . . . . . . . . . . . . . . . . . . .
2.3 Current Writers . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.1 Lucy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.2 Boroditsky . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.3 Everett . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.4 Slobin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4 Linguistic Relativity in Modern Culture . . . . . . . . . . . . . .
2.4.1 Literature . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.2 Artificial Languages . . . . . . . . . . . . . . . . . . . . . .
2.4.3 Programming Languages . . . . . . . . . . . . . . . . . . .
2.5 Evidence in Support of the Hypothesis . . . . . . . . . . . . . . .
2.6 Criticisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4
5
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
v
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
6
6
7
8
8
10
10
10
11
11
12
12
13
3 The Study of Second Language Acquisition
3.1 Language Transfer & Interlanguage . . . . . . . . . . . . . . . . .
3.2 Process of Acquisition . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Fossilisation and The Critical Period . . . . . . . . . . . . . . . .
15
16
16
17
4 Potential Implications of Linguistic Relativity
Acquisition
4.1 Research . . . . . . . . . . . . . . . . . . . .
4.1.1 Teresa Cadierno . . . . . . . . . . . .
4.1.2 Kenny R. Coventry, et al. . . . . . . .
4.1.3 Panos Athanasopoulos . . . . . . . .
4.1.4 Gale Stam . . . . . . . . . . . . . . .
19
20
20
21
21
22
in Second Language
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5 Empirical Research
5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 What is a Colour? . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.1 The Nature of Light . . . . . . . . . . . . . . . . . . . . . .
5.2.2 The Eye . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3 How Best to Go About It? . . . . . . . . . . . . . . . . . . . . . . .
5.3.1 How the Website Worked . . . . . . . . . . . . . . . . . . .
5.4 Discussion & Analysis of Results . . . . . . . . . . . . . . . . . .
5.4.1 Win Propensity in Comparison with a Colour’s ‘Greenness’
5.4.2 Score in Comparison with a Colour’s ‘Greenness’ . . . . .
24
25
28
28
29
29
30
32
33
33
6 Conclusions
6.1 Conclusions from Review of Literature . . . . . . . . . . . . . . .
6.2 Conclusions from Review of own Work . . . . . . . . . . . . . . .
40
41
42
References
44
Index
50
A Website Code
A.1 Database Structure
A.1.1 Battles . . .
A.1.2 Language . .
A.2 PHP classes . . . . .
A.2.1 db-gen.php .
A.2.2 index.php .
A.2.3 session.php
A.2.4 compare.php
A.2.5 rate.php . .
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
vi
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
I
.
I
.
I
.
II
.
II
. III
. III
. VI
. VI
. VIII
A.2.6 timeup.php . . . . . . . . . . . . . . . . . . . . . . . . . .
B Abbreviations
X
XII
C Comprehensive Results of the Study
XIII
C.1 Comparison of Dividing Points . . . . . . . . . . . . . . . . . . . XIII
C.2 Comparison of Extremes . . . . . . . . . . . . . . . . . . . . . . . XIV
C.3 Language Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . XV
vii
List of Figures
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
5.11
English - Colour vs. Propensity to be Greener
Dutch - Colour vs. Propensity to be Greener .
French - Colour vs. Propensity to be Greener
Italian - Colour vs. Propensity to be Greener .
Irish - Colour vs. Propensity to be Greener . .
English - Colour vs. Score . . . . . . . . . . .
Afrikaans - Colour vs. Score . . . . . . . . . .
French - Colour vs. Score . . . . . . . . . . . .
Spanish - Colour vs. Score . . . . . . . . . . .
Italian - Colour vs. Score . . . . . . . . . . . .
Portuguese - Colour vs. Score . . . . . . . . .
viii
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
34
34
35
35
36
37
37
38
38
39
39
List of Tables
C.1 The colour in the middle for English . .
C.2 The colour in the middle for French . . .
C.3 The colour in the middle for Italian . . .
C.4 The colour in the middle for Spanish . .
C.5 The colour in the middle for Portuguese
C.6 The colour in the middle for Dutch . . .
C.7 The colour in the middle for Afrikaans .
C.8 The colour in the middle for Irish . . . .
C.9 The colour extremes for English . . . . .
C.10 The colour extremes for French . . . . .
C.11 The colour extremes for Italian . . . . .
C.12 The colour extremes for Spanish . . . . .
C.13 The colour extremes for Portuguese . . .
C.14 The colour extremes for Dutch . . . . . .
C.15 The colour extremes for Afrikaans . . . .
C.16 The colour extremes for Irish . . . . . . .
C.17 All data gathered for English . . . . . . .
C.18 All data gathered for Afrikaans . . . . .
C.19 All data gathered for Dutch . . . . . . .
C.20 All data gathered for French . . . . . . .
C.21 All data gathered for Italian . . . . . . .
C.22 All data gathered for Spanish . . . . . .
C.23 All data gathered for Portuguese . . . . .
C.24 All data gathered for Irish . . . . . . . .
ix
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
XIII
XIII
XIV
XIV
XIV
XIV
XIV
XIV
XIV
XV
XV
XV
XV
XV
XV
XV
XVI
XVIII
XX
XXIII
XXV
XXVIII
XXX
XXXII
Abstract
As anyone who has ever studied a language will know, translation is not merely
a case of directly swapping words in one language for their equivalents in another. It is, (un)fortunately, somewhat more complicated than that. There exists
a myriad of different manners in which to express an idea or notion, and different manners are employed by different languages.
Benjamin Lee Whorf proposed that “[w]e dissect nature along lines laid down
by our native language,” and that “[l]anguage is not simply a reporting device for experience but a defining framework for it.” This is the fundamental
principle of Linguistic Relativity, a hypothesis the corner-stone of which is the
position which holds, essentially, that the phraseology of an idea or object in
an individual’s language influences how that individual perceives that idea or
concept.
This paper aims to establish any potential issues that may arise in the teaching and learning of a second or subsequent language as a result of this phenomenon, as well as to examine linguistic relativity itself in terms of any effect
it 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
1
1.1
Aims and Overview
The purpose of this paper is to examine Linguistic Relativity, a hypothesis
which claims that there is a direct link between cognitive perception of one’s
environment and the language(s) one knows, and how it may possibly pose issues for people attempting to learn a foreign language.
I will first begin with an analysis of the hypothesis, examining what exactly
it means, followed by a brief history lesson detailing the history of thought
on this topic, from ancient times up to the 19th century. I shall then delve into
more recent academic work, from both sides of the argument, from writers such
as Benjamin Lee Whorf (after whom the hypothesis is often called), and Steven
Pinker.
After this, I will discuss the field of Second Language Acquisition with reference to current research in the field that is, at least to some extent, relevant to
the study of Linguistic Relativity.
Then it remains to discuss various papers detailing research into the manifestations of Linguistic Relativity in non native speakers of a language with respect to their native language(s). In this section I will focus mainly on Han and
Cadierno (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, investigating variations in the perception of the green-blue spectrum across languages,
specifically analysing and comparing the point on the continuum at which any
given language separates green from blue, looking for both differences in languages concepts of individual colours, as well as looking for any variation in the
actual perception of the colour spectrum, which would hint at a certain degree
of Linguistic Determinism.
1.2
Motivation
While much research has been done into Linguistic Relativity, it has mainly
been focussed on the comparison of the cognitive processes of speakers of different languages rather than how it manifests itself in multilinguals. As a multilingual, albeit with only one L1, any implication of learning the languages I
have learnt, or of learning any subsequent languages on my cognition is of real
interest to me.
2
For me, at least, the study of human cognition and behaviour is fascinating.
The subtle patterns and the intricate ways through which they are explained
are awesome facets of the natural world.
Finally, linguistic relativity is one of the oldest and most hotly debated areas
of linguistics. From ancient Greek philosophers to modern academics, it has
been a topic of much debate, and tracking the hypothesis’ evolution over the
centuries is something I much enjoyed.
3
Chapter 2
Linguistic Relativity
“Language is the dress of thought.”
– Samuel Johnson, 1752
4
Linguistic Relativity (LR)1 is a hypothesis which claims, as Wilhem von
Humboldt put it, that “[t]he diversity of languages is not a diversity of signs
and 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 and
exists around us, by teaching us, or even forcing us, to think in a logic in keeping with the grammar of the language. Swoyer (2003) points out that when
analysing LR, one must avoid an all-or-none way of thinking, i.e. they key
question is “whether there are interesting and defensible versions of linguistic
relativism between those that are trivially true (the Babylonians didn’t have a
counterpart of the word ‘telephone’, so they didn’t think about telephones) and
those that are dramatic but almost certainly false (those who speak different
languages 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 of
language as a symbolic system which represents our experiences as the mind
represents them, and that all humans have the ability to have the same experiences. In Plato’s dialogue “Cratylus” (Sedley, 2003), Socrates is asked by Hermogenes and Cratylus as to whether the names of things are derived by nature,
or “physis”(‘φύσ ις’), (i.e. that there is an intrinsic link between the sounds
made an the objects the words represent) or by some human convention, or
“nomos”(‘η óµoς’), (i.e. that words are just an arbitrary collection of letters/sounds). Aristotle’s position, that language is used to represent experiences is
right at the core of the idea of LR, and shows us that the general concept of
considering the relationship between thought and language has existed since
the very early days of study into language. Socrates’ statements in “Cratylus”,
which, in fact, lean towards the idea that names are derived by nature2 , form
the basis for the later work by de Saussure (among others, e.g Klinkenberg and
Hjelmslev) into the relationships between signifier and signified, the general
field of semiotics.
1 Often
called the Sapir-Whorf hypothesis, named after the linguists who first seriously developed the idea.
2 In fact, at its most basic level, language may have originated from non-arbitrary naming
of 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 of
humans.
5
2.2
19th and Early 20th Centuries - The Beginnings
of Modern Thinking on Linguistic Relativity
Saussure (1916) is a compilation of the lectures given by the influential Swiss
linguist Ferdinand de Saussure (1857-1913). In it is detailed Saussure’s proposition of a dyadic model of a sign, which is made up of the signifier and the
signified. It is a psychological model which has the signifier as an “acoustic
image” and the signified as a “concept”. This was really the beginning of modern thinking into the relationship between language and thought. However, the
German philosopher Wilhelm von Humboldt (1767-1835) proposed a ‘Weltansicht’ (world-view) hypothesis, in which he was among the first to emphasize
the differences different languages reveal between the different cultures which
speak them (O’Donnell, 2008). This hypothesis held that speakers of different languages have varying ways of viewing the world, stating in “Essai sur les
langues du Nouveau Continent” (1812) that the language we speak “transplants
us” into the world we live in (ibid.). For von Humboldt, language and thought
were one and the same. He even went so far as to call language a “mirror of
the mind” (ibid.). Edward Sapir (1884-1936) of Columbia University developed
these ideas further, holding the opposite opinion to von Humboldt, in that he
did not believe that language and thought are one and the same, but rather that
they are closely related, and very similar, describing thought as “a refined interpretation of [language’s] content” (Sapir and Mandelbaum, 1949). Lucy (1992)
states that Sapir supported the LR principle, and believed that culture had a
certain degree of influence over language, stating that “[n]o two languages are
ever sufficiently similar to be considered as representing the same social reality”, and that the worlds of different cultures are distinct and “not merely the
same world with different labels attached” (ibid). He did not fully accept linguistic 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 called
simply the “Whorf Hypothesis”, one of the foremost interpretations of LR. He
held that language had an effect on thought, and that the structures of language
had implications for cognition. He originally worked as a fire inspector, and
3 Linguistic
Determinism is a position held by some, notably in Wittgenstein (1961), that
language limits or determines thought, whereas LR holds that one’s perception of the world is
related to one’s language.
6
some of his early thoughts on language arose from inspections of fire damaged
workplaces, where linguistic confusion was thought to be a contributing factor.
It became apparent to him that “the meaning of [a] situation” to people prescribed how they behaved and the level of caution they exercised. He further
emphasised that it was the linguistic meaning associated with the situation that
dictated behaviour. In one instance, he was examining a fire caused by the explosion of spent ‘gasoline drums’, in which workers at the plant were far more
cautious when handling the drums when they were full of the highly flammable
liquid than when the drums were ‘empty’. However, the drums that were empty
did not house a vacuum, nor did they house standard atmospheric air. They
held gasoline vapour, which in this case is far more dangerous than the liquid
fuel. The workers at the plant were casual enough when handling the empty
drums as to throw cigarette butts around. He says : “Physically, the situation
is hazardous, but the linguistic analysis according to regular analogy must employ 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 and
void, negative, inert,’ (2) applied in analysis of physical situations without regard 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 teaching
in the Department of Anthropology. Sapir, who was continuing the work of his
mentor, Franz Boas (Columbia University), into the relationship between language and culture4 , mentored Whorf. Sapir believed, primarily, that culture influenced language (at a lexical rather than morpho-syntactic level) (O’Donnell,
2008). Whorf continued in his own work, largely but not entirely independently, crafting the theory as it is known today. The term “Sapir-Whorf hypothesis” 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 in
the topic, and as such there is plenty of contemporary research and literature to
be found pertaining to LR. Some of the more prominent modern proponents of
the principle include John Lucy at the University of Chicago, Lera Boroditsky
4 It
was Boas who originally gave the famous example of the Eskimo (Inuit) words for snow.
7
at Stanford, Daniel Everett at Bentley, and Dan Slobin at UC Berkeley. In this
section 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 tends
to focus on the potential implication structural, that is syntactic, differences between languages may have for cognition (O’Donnell, 2008). He has spent a considerable amount of time studying and comparing English and Yucatec Maya,
and in Lucy (2004) (from O’Donnell (2008)), he notes that the speakers of the
languages are constrained in different ways when using nouns in the plural - in
English, plural is marked only for count nouns (discrete, discernable and countable 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 wish
to enumerate a mass noun, we are obligated to name the unit by which we count
count the thing, e.g. two locks of hair. However, in Yucatec, all forms with a numeral must take a numeral classifier, which, according to Lucy, “reflects the fact
that 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 objects that would retain their shapes over time (for which English presupposes
shape as the quantificational unit, and Yucatec the substance), predicting that
when 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 two
are 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. The
results of the experiment confirmed his prediction. He then sought to know
how speakers of each language would react when the object was more malleable in nature, a situation which forces English speakers to consider more the
material of the object. He predicted that speakers of both languages would tend
to choose material over shape. And again his predictions were largely correct
in terms of the results derived from the experiment. Interestingly, propensity
to chose by material is low for younger speakers of both languages, increasing
with 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 sentence describing a scene in different languages, it could be necessary to know
various small details about the scene in one language which wouldn’t necessarily be encoded. By taking her example phrase “the elephant ate the peanuts”,
8
and comparing it to how it would be repeated in different languages we can
see that many things we as English speakers do not even register are fundamental to the sentence structure in other languages, and, indeed, vice versa. In
Mandarin we have:
(1)
Dà
xiàng
chı̄ hāshēng
Adult elephant eat peanut
The elephant ate the peanuts
Note the lack of tensing in the verb ‘eat’. Conversely, French encodes information that we do not in English, and as such the original sentence could be
translated, depending on whether all or just some of the peanuts were eaten, in
either of the following two ways:
(2)
L’ éléphant a
mangé les
cacahuètes.
The elephant AUX ate
the.DEF peanuts
The elephant ate the peanuts
(3)
L’ éléphant a
mangé des
cacahuètes.
The elephant AUX ate
the.PART peanuts
The elephant ate (some of) the peanuts
French requires that whether it was some or all of the peanuts that were
eaten be explicitly encoded into the sentence. Other languages can require the
encoding of particular information that I’m certain most English speakers never
seriously consider, for example, Turkish requires that the verb be marked to
show whether the knowledge is first-hand or not. She states that these differences may have some impact on cognition. Personally, I feel it would be interesting to see if language had an effect on long term memory, that is, would
French speakers be more likely to remember correctly whether all or some of
the peanuts were eaten? Does one remember best those details that are linguistically 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 vertical model as evidence for LR. She found (Boroditsky, 2001) that English speakers answered “purely temporal questions” more quickly after horizontal primes
than vertical primes, with the opposite being the case for speakers of Mandarin.
Another interesting aspect of time models to explore would be the indigenous
South American language of Aymara, in which speakers gesture behind them
when 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 would
be expected of a speaker of English, and many other European languges.
9
2.3.3
Everett
Daniel Everett is best known for his work with the Pirahã people of the Brazilian Amazon in Everett et al. (2005). Their language is an anomaly, lacking
fundamental structures and concepts present in most other known languages.
The Pirahã language lacks words (and seemingly concepts) for numbers, and
is referred to as a “one, two, many” language, in that it distinguishes between
the one and two items, but any larger number of items is just referred to using
non-specific quantifiers. The tribe felt that as a result they were being cheated
in trade and asked if he could teach them numeracy. After 8 months, not a
single member of the community could count to ten, or do basic addition, and
the project was abandoned. Everett performed an experiment wherein Pirahã
speakers were tested to see if they could remember how many objects they were
shown. The could, most of the time, remember accurately up to four objects, but
after that the accuracy fell away dramatically. Everett claims that the cultural
ideology of the Pirahã is the reason that traits of language thought universal are
not present in Pirahã.
2.3.4
Slobin
Dan Slobin’s most influential work to date is Slobin (1996), in which he proposes his idea of “thinking for speaking” as a reformulation of the relationship between language and thought. It is his opinion that in order to become
a competent speaker of another language one must learn a mode of thinking
specific to that language. He is also interested in the field of language acquisition, making his research particularly pertinent to this thesis. One of the more
interesting works he has conducted is the “Frog Story Project”. In Berman and
Slobin (1994), he, along with Ruth Berman, developed a children’s story book
comprising 24 pictures and no words. It allows for narratives that have similar
content, but will vary by language and the age of the children who ‘read’ them.
The study compared English, Spanish, German, Turkish, and Hebrew - though
data 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 in
modern literature. The genre of book in which it most commonly occurs is
Science Fiction. There also exist artificial languages that have been developed
to take advantage of Whorfian interpretations of cognition, some deliberately,
and some by chance. In the field of computer programming, there has also been
10
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 totalitarian state, with a dictator called “Big Brother”. The protagonist is an employee in
the “Ministry of Truth” and is charged with altering historical records in order
to support the regime’s propaganda. One of the ways that the regime maintains
control is through modifying the language used by the citizens into what they
call “Newspeak”, allowing only certain thoughts and concepts to be present in
the the minds of the populous.
In 1966 Samuel R. Delany published “Babel-17”, a book that deals with an ongoing war in space, in which a language (with many interesting features, including lacking a 1st person singular pronoun) has been developed to be used
as a weapon. Learning it turns you into a traitor, but attracts you to learn it as
doing so can heighten your other senses and abilities.
2.4.2
Artificial Languages
Ithkuil (Quijada, J., 2004), a language created by John Quijada, was designed
to be “capable of high levels of conciseness and semantic detail while overtly
reflecting a deep level of cognitive conceptualization, more so than in natural
languages”. Kozlowski, S. (2004) examines the language in terms of the LR
hypothesis and its concise nature, arguing that the speed of human cognition
could be greatly increased (he suggests five- or six-fold increases in speed) by
speaking such a streamlined language. Unfortunately, there are no known fluent speakers (much less native speakers) of the language to test out this idea.
The “Newspeak” of Orwell’s aforementioned work was used by the totalitarian
regime to influence the population’s thoughts and general perception of reality. Newspeak sought the radical reduction in vocabulary, ultimately reducing
meaning to simple contrasts (good vs. bad). Another way the language was
used 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 agglutinative structure than contemporary English. Words like ‘bad’ are rendered as
‘ungood’5 , and irregularities and complexities are removed, for example ‘goodgooder-goodest’ has replaced ‘good-better-best’.
Brown (1966) sets out the stall for “Loglan”, a language based on first-order logical predicates, designed to test the LR hypothesis. He aimed for it to be so different from natural languages that anyone who learnt it would need to think in
5 Note
the similarity to the Esperanto prefix ‘mal-’ in big-granda vs. small-malgranda
11
a different way. Riner, R.D. (1990) says “As far as we can yet know, LOGLAN can
accommodate precisely and unambiguously the native ways of saying things
in any natural language. In fact, because it is logically rigorous, LOGLAN
forces the speaker to make the metaphysical (cultural, worldview) premises
in and of the natural language explicit in rendering the thought into (disambiguated) LOGLAN. Those assumptions, made explicit, become propositions
that are open for critical review and amendment - so not only can the SapirWhorf 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 up
according to what they see as the best way of solving problems. When applying
LR to programming languages, one would be hypothesising that the languages
known to the programmer influence him in his decision as to how best to solve a
problem. All programming is the search for the optimum solution to a problem,
and a good programmer would know the relative strengths of the languages in
his toolset when it comes to that particular area of problem-solving, and would
know which one was strongest. However, the hypothesis would hold that as he
is unaware that his strongest language may be weaker than languages he does
not know, he may not actually find the optimum solution to the problem at
hand. Graham (2004) deals with this subject, and suggests that because writing
in one language means thinking in that language, programmers will be happy
with whatever language they use as it is the language that dictates the program
they will write.
2.5
Evidence in Support of the Hypothesis
In the 1950s and 1960s, the hypothesis6 came under rigorous scrutiny. Brown
and Lenneberg (1954) conducted experiments which they designed in order
to discover whether or not the perception of the colour spectrum varied among
speakers of different languages. Berlin and Kay (1991) appeared to discredit the
entire principle when they claimed that universal linguistic constraints came in
to 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 a
great deal of support for weak versions of the hypothesis (i.e. that , as Roger
6 The
term “Sapir-Whorf” hypothesis, introduced by a student of Sapir’s, is commonly used
synonymously with the term “Linguistic Relativity principle”. It is, in fact, a misnomer, as the
principle, as defined by Whorf (alone) is not a hypothesis in the scientific sense.
12
Brown put it, “structural differences between language systems will, in general, be paralleled by nonlinguistic cognitive differences, of an unspecified sort,
in the native speakers of the language”), particularly when it comes to spatial
relations, but also in analysis of the colour spectrum. Drivonikou et al. (2007)
conducts some very interesting research in comparing the presence of Whorfian
effects on concepts in the left and right hemispheres of the brain, and concludes
that concepts analysed in the left hemisphere (the one more closely linked to
rational, logical and objective thinking) are more prone to Whorfian influence.
Among the concepts analysed in the left hemisphere of the brain is the colour
spectrum, which lends some credence to the idea that LR affects perception of
the colour spectrum.
Niemeier and Dirven (2000) is a collection of articles espousing support for
the idea of LR. An article there-contained by Gábor Győri examines the relationship between language and cognition. Győri defines human knowledge in
terms of genetic, neural and symbolic knowledge. Humans are the only species
to possess symbolic knowledge, and it is this type of knowledge that is by some
distance 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 can
know 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. Győri
then says that LR is a logical conclusion of this fact, stating that “the types of
knowledge that must by their nature be mediated to us through language will
also be processed in our minds in that medium”. In this case, whether or not
differences exist between how languages interpret reality is entirely irrelevant,
but what is clear is that cognition (the definition of which he takes to be “the
acquisition, organization and application of knowledge”) is undoubtedly influenced 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 Whorfianism. He notes that though many and varied, the majority are fallacies based
on the misrepresentation of Whorfianism and only disprove extreme statements
which nobody ever made.
Many of the criticisms made of LR seem to project claims of linguistic determinism on anyone who mentions linguistic relativity. Olshewsky (1969) claims
that “[t]he thrust of the Sapir-Whorf Hypothesis is that thought and culture not
only reflect the linguistic forms and categories with which they operate but are
determined by them”, which is completely false. Anyone assuming this to be
13
the position of a relativist, and from there seeking to discredit them is making
a grave error. There is much, often quite valid, criticism of determinism which
is wholly inapplicable to LR. Pinker (2000) speaks of identicality, determinism
and relativity as if they were all as absurd as each other. Identicality7 is thoroughly torn apart, without even bringing in his notion of mentalese, that is to
say that all humans perform cognitive processing in the same mental language,
and merely translate our ideas while speaking. He also demonstrates the falsity of determinism, before moving on to LR, which he conveniently side-steps.
He doesn’t discuss any research into the relationship between thought and language, but gives a “clinching experiment” which appears to show that physiology, rather than language, is the dominant influence on the learning of new
colour words.
In his preface to Schaff et al. (1973), Noam Chomsky claims that “Whorf argues
that the structure of language plays a role in determining a world-view”, which
again, shows a confusion as to what LR actually is. He goes on to point out
a valid flaw in Whorf’s work, in that the hypothesis he puts forward is based
solely on his research into Hopi, with particular reference to the model of time
in the Hopi language. He validly points out that Whorf’s representation of the
English system is incorrect and that the argument made is tentative at best
(Lenneberg (1953) claims that the fact that Whorf described the Hopi model
of time in English is evidence which disproves LR as Whorf could translate the
idea. However, Whorf’s point was not that things in one language are not translatable into another, but rather that, in this case, an English speaker is able to
understand how a Hopi speaker thinks without thinking that way themselves).
However, even if Whorf’s original ideas on LR may have had false seeds, to
discount an entire philosophical position as a result, ignoring the fruits of any
subsequent research, is very naïve.
7 The
belief that language precisely, in a word-for-word fashion, determines thought.
14
Chapter 3
The Study of Second Language
Acquisition
“I wish life was not so short,” he thought. “Languages take such a time, and so do all
the things one wants to know about.”
– J.R.R. Tolkien, The Lost Road, c. 1936
15
Second language acquisition (SLA) is the process whereby a person learns
a second1 language. The focus of research into SLA is on the learner and what
process 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 used
in 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 of
SLA, but rather it is the goal of a second language learner, or the state attained
on having learnt a second language.
3.1
Language Transfer & Interlanguage
Language transfer is a feature of what is known as ‘learner language’, whereby
the language learner’s L1 influences productions in the L2. The syntactic and
lexico-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. Spanish 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 unlikely
to make). Learner language is of interest2 to those who study SLA as it gives an
insight into the mental representation of the language on the part of the learner.
It became apparent that ‘learner language’ could not be adequately described
as a cross between the L2 and the learner’s L1. Sometimes learners come out
with sentences which are not grammatical in either language. The concept of
‘interlanguage’(IL) was developed to explain this phenomenon. Interlanguage
is that learner language which incorporates: language transfer, overgeneralisation3 , and simplification4 .
3.2
Process of Acquisition
The input received by the learner appears to be the most important factor when
it comes to the learning of a language (Krashen, 1994, 1981). These papers
point to studies showing the amount of time spent in an immersive environment (e.g. living in a foreign country) is closely related to the rate at which the
learner learns. Krashen (2004) points to reading as being a key source of input
1 The word second, in this instance, is a misnomer. The field, in fact, concerns itself with the
acquisition of all languages which are not first languages (L1s).
2 Ellis and Barkhuizen (2005) says that it is the primary source of data for those who study
in the field as it is not yet possible to analyse such mental representations with brain scans etc..
3 The use of rules in ways that are not usually deemed correct. Essentially, not knowing
exceptions to rules.
4 At times, the language used can be very simple, bordering on a pidgin.
16
for the language learner. The more foreign language literature consumed, the
faster the learner learns.
Also important is the output produced by the learner. Practising speaking and
writing in the foreign language speeds up acquisition. Long (1996) holds that
overall interaction (be it reading, writing, listening, or speaking), particularly
when the learner has to guess at meaning when there is a communication breakdown, helps in improving overall comprehensibility of the input.
Long, (ibid.), also mentions that, for the best conditions for language acquisition, a learner must be engaged in the encoding and decoding of semantically meaningful content. That is, to a certain degree, saying that learners learn
best when performing internal translations of the content in the environment
in 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 which
approximates the grammar of the target language, gradually increasing in accuracy over time. Selinker (1972) claims that the approximation of the target
grammar may be stopped at one or more points, calling the permanent stop in
movement towards the TL grammar “fossilisation”. It is very common for fossilisation not to be overcome despite serious effort at learning. Selinker also
comments that learners may become complacent once they attain a level of
the language that allows them to communicate comfortably, and may, therefore, even subconsciously, lack the required motivation or desire to maintain
progress towards native-like grammar and general linguistic dexterity.
The idea that a language is not completely mastered, I feel, calls for mention of the Critical Period Hypothesis (CPH). This hypothesis (popularised by
Lenneberg (1967)), which is the subject of much debate in the field of linguistics, purports that one’s ability to learn a language decreases dramatically at a
predetermined point, and after such a point, great effort and determination is
required to successfully learn a language. Lenneberg (ibid.) claims that the window for achieving native-like competence in a language closes with the onset of
puberty, and that in order to gain full mastery in a language, it is necessary to
start learning it before this time.
In terms of SLA, this hypothesis is widely rejected. It is widely accepted, among
scholars 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 bilinguals, 5% begin learning one of their languages when they are in adulthood.
17
Any idea of an abrupt decline in one’s ability to perform a task would seem to
fly in the face of the natural, gradual decline that humans experience with aging. 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 a
native speaker.
Thus, fossilisation would appear to be a limit imposed by constraints other than
biological, such as the lack of motivation, lack of free time to dedicate to the
study of the language, or the lack of desire to attain fully native-like proficiency.
18
Chapter 4
Potential Implications of Linguistic
Relativity in Second Language
Acquisition
“My words fly up, my thoughts remain below:
Words without thoughts never to heaven go.”
– William Shakespeare, Hamlet, 1601
19
As I mentioned previously in §2.3.4, Slobin (1996) claims that in order to
fully 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 real
phenomenon. If one’s cognitive process is different from that of a native speaker
of another language (ceteris paribus), then the learning of that other language
requires not just the learning of its grammatical rules and vocabulary, but also
the 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 their
L1 into the L2 resulting in an L2 production that doesn’t fit that language’s
model. In this instance, it is important to note what is language transfer and
what is not. Speakers of pro-drop languages leaving out the subject pronoun in
their non-pro-drop L22 are not showing a difference in cognitive process, nor
are speakers of non-plural-marking languages failing to pluralise nouns in situations that a plural-marking language would require them to3 . Cadierno (2010)
discusses the relationship between LR and SLA in terms of motions involving a
boundary crossing event, and how speakers of verb- and satellite-framed (Spanish, 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. There
was a picture description task “which was designed to elicit the learners’ preferred means of expression when describing motion scenes depicting boundarycrossing situations”, for three different types of boundary crossing (into a bounded
space, out of a bounded space, and over a plane). The second and third tasks
were concerned with vocabulary production and recognition in order to guage
the range of options available to the subjects. In the second task, subjects were
asked 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
1 Particularly
the relations between objects.
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 simple
manifestation of a construction which is grammatical in the L1, and not in the L2, being used
in the L2.
3 An example of this would be native speakers of Irish, who, if saying the equivalent of “three
dogs, four dogs, five dogs”, produce in their own language a construction that would be translated very literally as “three dog, four dog, five dog”.
2 An
20
understood the meaning. When analysing the results, very little difference was
noticed between German and Russian L1 subjects (both of which are satelliteframed languages), however, when these results were compared the results obtained from the Spanish L1 subjects (Spanish, like all Romance languages, is a
verb-framed language) there was a marked difference when it came to the type
of construction used in the picture description task, the proportion of mannerof-motion verbs (to run, to jump, etc.) used in both the picture description task
and the vocabulary production task, and the number of verbs produced and
recognised in the vocabulary test. This data seems to point towards the conclusion that the subjects4 whose L1 is Spanish/verb-framed have issues in Danish
that 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 containment relations. On research into spatial relations, an example from Pederson
et al. (1998) is given (see §5.1 on page 26). The study documented in the paper was designed to test for conceptual differences in containment and support
relations between L1 speakers of English and Spanish, on the basis that Spanish 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 immediate
memory.
Very early on in the paper, it is confessed that the experiment failed to work
as planned, as there appeared to be no difference in sensitivity levels to containment and support relations between the two languages (when testing L1
speakers) in question, “challenging more extreme versions of linguistic relativity, and undermining the rationale for testing for these conceptual differences
for these relations in second language learners”(Coventry et al., 2010). With
no 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 the
languages 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 Lucy
and Gaskins (2001) as portrayed in §2.3.1, with Japanese L2 learners of English, examining learners at various levels and comparing them to monoglot
4 All
subjects in the study were resident in Denmark for between 18 and 30 months prior to
the study, and had partaken in a Danish government language education course.
21
native speakers of English, and monoglot native speakers of Japanese. Subjects 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 of
items as the pivot, but with different items. His expectation was that English
speakers would group as if number were important, and that Japanese speakers
would group as if the type of item or substance mattered more. The gathered
data did indeed reflect this, but also noted that more advanced Japanese L1
learners of English seemed to behave more like English speakers than either
Japanese monoglots, or the other, less advanced, English learners. From this,
he concludes that L2 acquisition may change cognitive processing which were
previously influenced by the L1.
“Overall then the results show that the cognition of the two L2 groups
correlates with their proficiency in the L2 and with their performance on the grammaticality judgement task. The more successful
the L2 learners are in the QPT5 and in the grammaticality judgement
of number marking and articles in English, the more they behave
like 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 NationalLouis University, based in Chicago) asks whether or not an L2 speaker’s model
of thinking for speaking can change. She performed a study, analysing the patterns of speech of a native speaker of Spanish resident in the United States of
America, and how they changed over a 9 year period (1997 - 2006). The subject had begun learning English two years prior to the beginning of the study.
Stam compared how the subject expressed path and manner in motion events
in both the L1 and L2 at the beginning and at the end of the study, and compared them to each other, and to similar productions of native speakers. The
subject was interviewed twice, in 1997 and 2006, and was asked to narrate the
same Sylvester and Tweety Bird comic strip on both occasions. Stam hypothesised that there would be a shift towards more native like productions in the L2
as proficiency increased over time. The results largely supported this notion.
Stam found that there was no change in the subjects use of Spanish, for either
manner or path. In terms of path events in the L2, the subject in the initial
interview used a mixture of her L1 (verb-framed) construction and the correct
L2 (satelite-framed) construction to narrate the comic strip. In the follow up
interview in 2006, it was found that the subject had, as hypothesised, drifted
5 The
Oxford Quick Placement Test, a language proficiency assessment test. My annotation.
22
to a substantially more native like use of constructions (that is, a decreased use
of gerund forms). However, the same results were not found in terms of manner, where the L1 construction persisted. Stam proposes several reasons as to
why this may be the case, among them that manner is seen as less important
than path in motion events, or that there is a lack of exposure to manner verbs
in 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 events
in the L2 over time indicates that it is possible for language learners to change
their model of thinking for speaking, albeit a gradual shift. The implication
here is that although LR may pose problems in language learning, it is not an
insurmountable obstacle. It can be overcome with persistence, patience and
dedication.
23
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
24
As part of this project, I decided to perform some research of my own into
whether or not one’s language affects perception. Having initially considered
an analysis of spatial relations, I settled on working on the colour spectrum. In
more specific terms, I thought it would be interesting to gather data on how different languages separate adjacent colours along the continuum of colours visible to the human eye, with the view that significantly different division points
would 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 into
Linguistic Relativity. Firstly, there is the “structure centered” approach, where
a unique or rare structure in a language is analysed to see if and how it impacts
upon the behaviour of speakers of that language (Lucy (1992), Lucy and Gaskins (2001) and Lucy (2004) examine Yucatec1 along these lines).
Secondly, there is the “behavior centered” approach, which is, to a certain extent, the inverse of of the “structure centered” approach, in that it analyses
the behaviours of different language communities and searches for contrasting linguistic phenomena that might explain any differences between the behaviours. Lucy (1997, pg. 303-304) discusses some very interesting research
done by Strømnes into the differences between Finnish and Swedish. He was
inspired to do this research because of the frustration he felt in his seeming
inability to learn Finnish. He deduced, from a series of novel experiments, that
“Swedish prepositions can be represented in terms of a vector geometry in a three-dimensional space whereas Finnish cases can be
represented in terms of a topology in a two-dimensional space coupled with a third dimension of time (or duration)”
(ibid.)
These differences are born out in many subtle ways. Strømnes noted differences
in the cinematography of Indo-European and Ural-Altaic language groups, noting that
“Indo-European (Swedish, Norwegian, English) productions formed
coherent temporal entities in which action could be followed from
beginning to end across scenes, whereas Ural-Altaic (Finnish, Hungarian, Estonian) productions showed more emphasis on static settings with only transitory movement and formed coherent personcentered entities in which scenes were linked by the emotional Gestalts
of persons”
(ibid.)
1A
Mayan language spoken by the aboriginal peoples of Southern Mexico.
25
From this, he focussed on why accidents were more common in Finnish speaking factories in Finland than those in which Swedish was spoken (in both Sweden and Finland) and deduced that
“Finns organize the workplace in a way that favors the individual
worker (person) over the temporal organization of the overall production process. Lack of attention to the overall temporal organization 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 explain
differences in practical behaviour between groups by way of a known linguistic
difference.
Finally, there is the category of research under which this study falls, which
Lucy calls “domain centered”. In this type of study, a researcher will take a particular semantic domain (in my case colour nomenclature, but other examples
include spatial relations) and analyse correlations within it across various languages. Many researchers (among them Paul Kay and John Lucy) acknowledge
an issue in the study of the colour spectrum as a result of the fact that human
perception of the colour spectrum depends to a certain extent on the hardware
that the human has2 allowing them to perceive a limited field of colour. In
terms of research into spatial relations, Pederson et al. (1998) documents an
experiment in which speakers of “absolute-frame” and “relative-frame” languages3 were asked to perform a non-linguistic task in which they were first
presented with a table with 4 cards on it(having the same pattern but different
orientations), and were then rotated 180 degrees around the table and asked to
pick 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 the
dots in the opposite orientation to themselves (relatively) but with the exact
2 i.e.
the eye and the neural networks to the brain
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 expression of relations like “the boy is to the right of the girl”. In languages like Tzeltal, there is no
possible way to encode the meaning of the sentence “the boy is to the right of the girl”, instead requiring the speaker to encode it as something along the lines of “the girls is west of
the boy” (clearly requiring a phenomenal cognitive ability in having a mental compass even in
unfamiliar environs).
3 An
26
same orientation in the absolute frame4 .
To date, the largest single aspect of LR to have been studied is colour terminology. Rather than serving as a deterrent to further study in this field, the fact
that studies constantly contradict and challenge each other provides a rather
rich collection of reading materials from both sides, as well as being very open
to more research. Many people think of colours as absolute entities, whereas
they are, in fact, arbitrary points along a continuum. To perform a comparison
on the various arbitrary points at which different languages choose to differentiate one colour from another could provide interesting results.
As for why specifically the green-blue continuum was chosen, this comes down
to several factors. Firstly, Berlin and Kay (1991) lay out the ascending scale
according to which languages acquire terminology for different colours. On
this scale green and blue are acquired sequentially5 , and as a result may have
a less well defined boundary. I also chose to go with the blue-green continuum
rather than any other aspect of the visible colour spectrum as it is the subsection of visible light least susceptible to colourblindness6 , thus reducing as
much as possible the potential influence of defective colour perception due to
the 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 different languages define their colour “ideals”7 . In Irish, the word “glas” is translated as “green”. However, this word also covers silver (as in the colour of the
blade 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
4 It
is also worth noting that spatial relations are 2-ary or 3-ary predicates. English has 3ary predicates, referencing both objects and the speaker, whereas an absolute framed language
only takes into consideration the two objects for which the relation is being defined. Languages
that employ 3-ary predicates can also access 2-ary predicates (“Dublin is south of Belfast” is a
perfectly logical and grammatical sentence in English), but 2-ary relation employing languages
cannot move up. Tepehua (a Totonacan language spoken in Central Mexico by fewer than
10,000 people) is an example of an intrinsic framed language, which references immediate
relations (beside, on top of, etc.) rather than abstract ones (north, uphill, etc.). Intrinsic and
Absolute framed languages operate under the same constraints.
5 If a language has 6 colour terms, then it has a term for blue, if it has only 5, it does not have
blue but has both of green and yellow (along with black, white, and red). If it has only four
terms for colours, it has black, white, red and then one of either green or yellow, so in strict
terms, the acquisition of terminology for green and blue, although always sequential, may not
be one after the other, but may well be.
6 According 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).
7 That is to say the frequency of light that native speakers picture upon hearing a specific
colour term.
8 “capall glas” would be translated to English as “grey horse”.
27
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 colour
of 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 physical
phenomenon, there are not rays of blue, red, green, etc. shooting around the
place. Colour recognition is, fundamentally a psychological process, dependant
on three factors - the physical properties of light, the working of the retina and
psychological 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 detectable
by the eye. It is the frequency of these individual waves that determines what
colour we perceive. Shorter wavelengths have an increasingly violet appearance
(ultra-violet light is that light with too short a wavelength for us to see), whereas
those waves with longer wavelengths have a more red appearance (infra-red
light is light with too long a wavelength to be seen). The actual sensation of
colour occurs with the stimulation of cones on the retina (see 5.2.2). Colours
that 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 colour
created by a wave with a wavelength of 6 × 10−7 metres, or more likely a combination of two or more waves (for example yellow and red, having wavelengths
of 5.8×10−7 and 7×10−7 metres, respectively)). As the eye is a synthesiser (i.e. it
cannot divide a compound colour into it’s original constituents10 ), colours are
generally 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. In
nature, colours mix with white to become more dilute, or more properly: more
or less saturated. The degree of saturation is called a colour’s “chroma”. The
final parameter which must be considered is “luminance”, or the amount of
9 The
Proto-Celtic root for both green and blue is “*glasto-”. See: The University of Wales
(2011) and Matasović (2009).
10 In 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 listening
to an orchestra, can pick out individual instruments playing, and is thus termed an analyser.
28
black that is mixed into a colour. We can see this by comparing the appearance
of a spectrally pure red colour when it is in well illuminated surroundings, and
when it is dimly lit.
Important work into colour and its perception was done by Young (1802), who
postulated that all colours are composed of three elementary colours. Helmholtz
(1852) went further and identified the three elementary colours as red, green
and 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 receptors in the eye.
5.2.2
The Eye
The inside of the human eye is lined with a light sensitive tissue called the
retina. The retina is a complex structure which is comprised of many layers of
neurons. Of these layers, the only one we really need to take into consideration for the purposes of this paper is the “Outer nuclear layer” (Carter-Dawson
and Lavail, 1979), which contains the photoreceptor cells, which have two main
types. There are rod-cells which function in dim light, giving black and white
vision. Colour receptors are called cone-cells, and these function in daytime
light (Björn, 2002). There are three variations of cone-cells, each being more
sensitive 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 average, 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% of
that number. The peak wavelength for each type of cone, naturally, varies from
human to human, but the figure rarely varies by more than 10−6 metres either
side of the averages. The S cone has an average peak at 4.3 × 10−7 metres, while
the M cone has its average peak in the vicinity of 5.4 × 10−7 metres, and the
L cone’s peak averages at about 5.7 × 10−7 metres. There is generally sensitivity 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. For
obvious reasons, the larger the data set was the better, and as such I decided that
the data gathering process should be a computer based survey, and the most
efficient way (I felt) of gathering a wide sample (particularly when looking for
speakers of different languages) was to host this survey on the internet. From
29
this point, various options were available to me. I could ask participants to rate
any 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 the
dividing point between green and blue, I could have subjects watch a video of
the transition between green and blue and ask them to click when they felt the
colour had changed from green to blue, or blue to green. The idea I settled on in
the 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 a
sporting match-up, with the winner being the greener colour. The colour at
the end of the experiment with the highest percentage of wins was deemed the
greenest, and that with the lowest percentage was the bluest. Each shade on the
continuum was also assigned an original score11 , which was modified with each
result as per the Elö ranking algorithm, which is used to rank chess players and
international football teams. It was then a modified version of the Facemash
website 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 the
experiment, and what would be required of them. They then selected their
native language and were directed to another page which would be used for
the testing. On this page, the subject was presented with 2 colours and below
them, a display of where the dividing point was for their language based on
current data12 . The page also contained the basic instruction “Click which one
you think is greener”. Upon clicking which colour the subject perceived as
greener, the result was then analysed by the program. The program updated
the database for the number of wins and losses incurred by the colour, and then
performed 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 as
the dividing point between both green and blue, as it had both a roughly similar
percentage of wins and a score that remained relatively neutral when the results
were weighted.
The Elo Algorithm
Elo (1978) set out a method for ranking chess players. In it, any two chess
players with a score can be ranked. It compares the two on several grounds. For
11 I
settled on the relatively arbitrary number of 1500.
feature was unintentionally left over after testing, and may have played some role in
influencing subjects in their decision making.
12 This
30
every match-up, there is an expected result. In the case where C1 and C2 are
pitted against one another, the expected result for C1 (EC1 ) would be
1
1 + 10(ScoreC1 )−(ScoreC2 )/400
,
and similarly, the expected result for C2 (EC2 ) would be
1
1 + 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:
0
ScoreC1
= ScoreC1 + K(R − EC1 )
where:
0
• ScoreC1
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 algorithm. 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 competition. 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 he
would gain very few points, and this is multiplied by his K number. However
were he to lose, he stands to lose a lot of points, and the coefficient magnifies
this - 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 points
on the spectrum that were being analysed. If two were extremely close to each
other, this may be difficult for the subject to discern, and thus the result was
treated as insignificant. If the colours were very far apart (for example: the
green of the Italian flag and the blue of the French flag), any ‘shock’ result
would surely be an error on the part of the subject, and the result would also
be considered insignificant13 . Between these two poles, I divided the remaining
shades into two separate groups, leaving me with the insignificant pairs being
those 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.
13 If
this were to continue for any language, over time the insignificant results would become
significant
31
5.4
Discussion & Analysis of Results
The data gathered provided me with specific values for the percentage of time a
colour was chosen, and its score at the end of the study, as calculated on an ongoing basis by the Elo Algorithm during the study. Every colour was assigned
a 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 gathered from 8 languages: English, Afrikaans, Dutch, French, Italian, Spanish, Portuguese and Irish15 . Unfortunately, these 8 languages do not provide a particularly 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 dialect
of Dutch) all being Germanic in origin; French, Spanish, Portuguese and Italian are all Romance (though there may be a greater level of similarity between
Spanish and Portuguese than the others due to the fact that they both immediately descended from Ibero-Romance); and Irish is a Goidelic Celtic language.
The largest dataset collected was that of English, consisting of almost 7000 individual clicks, going all the way down to Irish and Portuguese, with around
400 clicks each. Data was collected for other languages, but there was insufficient data upon which to perform useful analysis, for example, German had in
the 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 the
development of the website, which displays the green-blue continuum at the
bottom, 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 have
influenced 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, and
also to its ‘score’ when the results were weighted. As English was the largest
data set16 , it’s R2 values, i.e. how accurately it could predict one value from
14 As
the colours were generated for the study using 24-bit Truecolor RGB values. With Red
always being zero, the range from blue to green are those colours that fall between h0,0,255i
and h0,255,0i, 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.
15 A caveat when using data like this pertaining to the Irish language is that it would be very
difficult 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 of
native Irish speakers will have English language proficiency indistinguishable from English
monoglots. Thus, unexpected similarities between the two may need to be taken with a pinch
of salt.
16 And, conveniently, my L1!
32
another, was the highest on all fronts, thus I chose it as the standard against
which I will compare the data from the other languages.
5.4.1
Win Propensity in Comparison with a Colour’s ‘Greenness’
To illustrate any points made, I will use the following 5 languages in discussing
this 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 the
colour’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 language
begins seeing green. English and Dutch in this case have R2 values greater than
0.9, French has 0.89, and Irish and Italian have 0.77 and 0.79 respectively. The
results therefore are reasonably reliable. The slope of each language is very
gradual, but what is striking is that the slopes for English, Dutch, French and
Irish are all exactly 0.19%. That would suggest that speakers of all of these
languages do not perceive differently in any way the green-blue spectrum. The
slope for Italian is just 0.17%. Given the 21% error margin that must be afforded to the data from the language as per its R2 value, the difference is quite
insignificant. If this statistic was significant, it could point to the fact that Italian, like Russian and several other languages, has 3 words to cover what we
call ‘blue’ and ‘green’, namely ‘blu’, ‘azzurro’ and ‘verde’. This difference is insignificant, 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 hypothesis, i.e. Linguistic Determinism, in that there seems to be absolutely no effect of
language 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 a
particular shade was deemed greener to the score associated with that point on
the spectrum (which gives data in a more ‘weighted’ fashion), there seemed, on
first glance to be no difference. However, on closer inspection, an unexpected
trend clearly visible in English (5.4.2) and Afrikaans (5.4.2) actually permeates
throughout all of the languages. The fact that much more data was gathered for
languages like English, Dutch and Afrikaans means that trends are more pronounced, 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
R2 = 0.981
Score
Trend
GreenPercent
1
0.8
0.6
0.4
0.2
0
0
100
200 300 400
ColourValue
500
Figure 5.1: English - Colour vs. Propensity to be Greener
R2 = 0.9363
Score
Trend
GreenPercent
1
0.8
0.6
0.4
0.2
0
0
100
200 300 400
ColourValue
500
Figure 5.2: Dutch - Colour vs. Propensity to be Greener
downward kick below the trend line and a gradual return to the trend as seen
for English would be more obvious in all of the languages.
This blip would appear to represent where the languages separate blue from
green. Taking the point where the actual (blue) line crosses the trend line
34
R2 = 0.8926
Score
Trend
GreenPercent
1
0.8
0.6
0.4
0.2
0
0
100
200 300 400
ColourValue
500
Figure 5.3: French - Colour vs. Propensity to be Greener
R2 = 0.7972
Score
Trend
GreenPercent
1
0.8
0.6
0.4
0.2
0
0
100
200 300 400
ColourValue
500
Figure 5.4: Italian - Colour vs. Propensity to be Greener
(red)17 we can read off the point of the spectrum where ultimate confusion
17 Another
possible reading of the graph is that the bottom of the trough immediately following this crossing is the dividing point between the colours in the eyes of the language in
question.
35
R2 = 0.7756
Score
Trend
GreenPercent
1
0.8
0.6
0.4
0.2
0
0
100
200 300 400
ColourValue
500
Figure 5.5: Irish - Colour vs. Propensity to be Greener
on the part of the subject between green and blue occurs. Admittedly, due to
the number of outlying results for some of the languages, I had to discern the
crossing 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, in
an 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 have
something to do with the fact that the computer scientists who designed the
RGB 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 of
230, slightly more deviant from the English value than the others, but no correlation is present to Portuguese(280) (5.4.2), which, as I hypothesised, may
possibly be due to the Iberian origin of the two languages. Most interesting is
the value for Italian (200) (5.4.2). Italian deviates significantly from the others, and while I hesitate to draw any absolute conclusions from this, I propose
that 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
Score
Trend
Intercept
Score
2,000
1,000
0
0
100
200 300 400
ColourValue
500
Figure 5.6: English - Colour vs. Score
Score
Trend
Intercept
1,500
Score
1,000
500
0
0
100
200 300 400
ColourValue
500
Figure 5.7: Afrikaans - Colour vs. Score
37
Score
Trend
Intercept
2,000
Score
1,500
1,000
500
0
0
100
200 300 400
ColourValue
500
Figure 5.8: French - Colour vs. Score
Score
Trend
Intercept
1,500
Score
1,000
500
0
0
100
200 300 400
ColourValue
500
Figure 5.9: Spanish - Colour vs. Score
38
Score
Trend
Intercept
2,000
Score
1,500
1,000
500
0
0
100
200 300 400
ColourValue
500
Figure 5.10: Italian - Colour vs. Score
Score
Trend
Intercept
2,000
Score
1,500
1,000
500
0
0
100
200 300 400
ColourValue
500
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 thou
hast done, be troubled, and rejoice for the good.”
– Pythagoras, c. 530 BC
40
Language, culture and thought are undoubtedly linked inextricably. That a
given language has any effect on the cognitive processes of its native speakers
is the source of great debate. But to suggest that we all look at things and
behave 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 the
human experience as it is, and being as varied as it is throughout the world, is
part of that beauty. To suggest that, although we all speak differently, we all
think in the same way would mean the world would be a much greyer, and a
much 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 and
Gaskins (2001) provides some of the first definite, experimentally verified evidence in support of the reality of Whorf’s original proposal. While many argue
it, I believe I have shown that linguists such as Pinker and Chomsky have totally
failed to support their oppositions to it, by falling into the trap of assuming that
LR and LD are one and the same. I believe also that I have provided reasonable
support for the distinction that one must make between LD and LR1 . Examples such as the different rate of workplace accidents experienced by those in
Finnish and Swedish speaking workplaces, as documented by Strømnes is very
clear evidence for LR, while not making any claims about LD. What is apparent
is that LD is widely rejected among the academic community.
A really interesting area related to LR and not explored here, brought about
by Dan Slobin, is the idea of “thinking for speaking”. Of particular interest
to me is in what way LR may affect people’s memories of events, and whether
language 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 the
notion that language does indeed steer memory towards those details which
the speaker’s language deems to be more important (while Indonesian speakers
may 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, they
may have a better memory for details of the event that do require marking in
Indonesian, and which are not marked in English, e.g. the relative age of the
actor in the event to the observer).
1 Linguistic
Determinism is the theory that the language you speak determines entirely how
you can and do interpret reality, whereas Linguistic Relativity suggests, simply, that nonlinguistic behaviour is influenced by language.
41
The works I cited by Athanasopoulos and Stam seem to highlight the fact that
LR does indeed pose issues in the learning of language (also evidenced in Cadierno
(2010)). However, both studies found that the language learners analysed therein
seemed to improve their nativelikeness over time and with increased input.
What their studies show is that LR is an obstacle in language learning, but that
it is not an insurmountable one. Language learners must acquire a new model
of ‘thinking for speaking’ in order to produce utterances which are more like
those of native speakers. Essentially, the main implication of LR in the area
of SLA is that it does provide a stumbling block initially to learners, which
they gradually overcome, but as motivation and attitude are such key factors
in the acquisition of language, the detrimental effects of this stumbling block
may grow disproportionately to the actual problem posed. The only real way,
it seems, to acquire the new model of ‘thinking for speaking’ is persistence and
dedication.
6.2
Conclusions from Review of own Work
The data gathered from my own study points slightly in favour of weaker interpretations of LR in relation to interpretations of the colour spectrum. Conversely, the evidence gathered in relation to LD very much points against it,
with absolutely zero variation in the recognition of colours as being greener or
bluer. Admittedly, the error in including a representation of the spectrum on
the test (as detailed in §5) makes this data not 100% reliable.
The variations in the point at which languages seem to separate green and blue
are interesting, and seem to evidence weak LR. The most interesting among
them being that of Italian. Its use of three lexical items in the classification of
the same interval of the colour spectrum, for which we, in English, use only
two, seems to have affected how native speakers of Italian divide the spectrum
in comparison to native speakers of English, and other languages. It would
have been interesting to corroborate this data with data from Russian. Unfortunately, sufficient data was not gathered from that language, but it would be
something that would interest me to see in the future. Indeed, I was disappointed not to have gathered data on more languages over the course of the
experiment, as I expected that, if differences were to exist between languages in
their divisions of the colour spectrum, they would be most pronounced across
different language families (as such, comparing English with languages such as
Finnish, Mandarin, Japanese, Arabic or Indonesian may have provided results
that are far more interesting). I have left the website open, and plan to check
from time to time to see if sufficient data for any of these languages has been
acquired for analysis, out of personal interest.
42
The implications of these differences are that language learners seem to need
to acquire more than just a foreign lexicon and grammar rules, and that the
parameter resetting proposed by supporters of Universal Grammar isn’t quite
enough to explain the actual process undergone in acquiring proficiency in a
foreign tongue. They must, as stated in Athanasopoulos (2006), acquire a new
pattern of ‘thinking for speaking’.
43
References
D. Alford. Reality, Mind and Language as Field, Wave and Particle. unpublished
ms, available from author, 1981.
K. Allan. Aristotle’s footprints in the linguist’s garden. Language Sciences, 26
(4):317–342, 2004. ISSN 0388-0001.
P. Athanasopoulos. Effects of the grammatical representation of number on
cognition in bilinguals. Bilingualism: Language and Cognition, 9(01):89–96,
2006. ISSN 1469-1841.
B. Berlin and P. Kay. Basic color terms: Their universality and evolution. Univ of
California Pr, 1991. ISBN 0520076354.
R.A. Berman and D.I. Slobin. Relating events in narrative: A crosslinguistic developmental study. Lawrence Erlbaum, 1994. ISBN 0805814353.
L.O. Björn.
Photobiology:
the science of light and life.
Academic Publishers, 2002.
ISBN 9781402008429.
http://books.google.com/books?id=GUVHtJCtM0UC.
Kluwer
URL
L. Boroditsky. Does Language Shape Thought?: Mandarin and English Speakers’ Conceptions of Time* 1. Cognitive psychology, 43(1):1–22, 2001. ISSN
0010-0285.
L. Boroditsky. Linguistic relativity. Wiley Online Library, 2003.
L. Boroditsky, W. Ham, and M. Ramscar. What is universal in event perception? Comparing English and Indonesian speakers. In Proceedings of the 24th
annual meeting of the Cognitive Science Society, pages 136–141. Citeseer, 2002.
L. Boroditsky, L.A. Schmidt, and W. Phillips. Sex, syntax, and semantics. Language in mind: Advances in the study of language and thought, pages 61–79,
2003.
J.C. Brown. Loglan. The Loglan Institute, 1966.
44
R.W. Brown and E.H. Lenneberg. A study in language and cognition. The Journal of Abnormal and Social Psychology, 49(3):454, 1954. ISSN 0096-851X.
T. Cadierno. Motion in Danish as a second language: does the learner’s L1 make
a difference. Linguistic Relativity in Second Language Acquisition: Evidence of
Thinking for Speaking - Han Z.-H, Cadierno T, eds.(In press) Multilingual Matters, 2010.
L.D. Carter-Dawson and M.M. Lavail. Rods and cones in the mouse retina. ii.
autoradiographic analysis of cell generation using tritiated thymidine. The
Journal of comparative neurology, 188(2):263–272, 1979. ISSN 1096-9861.
V.J. Cook. Second language learning and language teaching. Hodder Education,
2008. ISBN 0340958766.
S.P. Corder and S.P. Corder. Error analysis and interlanguage. Oxford Univ Press,
1986. ISBN 0194370739.
K. R. Coventry, B. Valdés, and P. Guijarro-Fuentes. Thinking for Speaking and
Immediate Memory for Spatial Relations. Linguistic Relativity in Second Language Acquisition: Evidence of Thinking for Speaking - Han Z.-H, Cadierno T,
eds.(In press) Multilingual Matters, 2010.
C.A. Curcio, K.R. Sloan, R.E. Kalina, and A.E. Hendrickson. Human photoreceptor topography. The Journal of comparative neurology, 292(4):497–523,
1990. ISSN 1096-9861.
GV Drivonikou, P. Kay, T. Regier, RB Ivry, AL Gilbert, A. Franklin, and IRL
Davies. Further evidence that Whorfian effects are stronger in the right visual
field than the left. Proceedings of the National Academy of Sciences, 104(3):1097,
2007.
R. Ellis and G.P. Barkhuizen. Analysing learner language. Oxford University
Press, 2005. ISBN 0194316343.
A.E. Elo. The rating of chessplayers, past and present. Batsford, 1978. ISBN
0713418605.
D.L. Everett, B. Berlin, M.A. Gonçalves, P. Kay, S.C. Levinson, A. Pawley, A. Surrallés, M. Tomasello, A. Wierzbicka, and D.L. Everett. Cultural constraints
on grammar and cognition in Pirahã. Current Anthropology, 46(4):621–646,
2005. ISSN 0011-3204.
P. Graham. Hackers & painters: big ideas from the computer age. O’Reilly Media,
Inc., 2004. ISBN 0596006624.
45
Z. Han and T. Cadierno. Linguistic Relativity in SLA: Thinking for Speaking. Second Language Acquisition. Multilingual Matters, 2010.
H. Helmholtz. On the theory of compound colors. Philosophical Magazine, 4(4):
519–534, 1852.
H. Hoijer. Language in culture. University of Chicago Press, 1954.
W.V. Humboldt. On the Comparative Study of Language and Its Relation to the
Different Periods of Language Development. Essays on Language/Wilhelm von
Humboldt, Frankfurt am Main: Lang, 1997:1–22, 1820.
A.P. König. Beiträge zur Psychologie und Physiologie der Sinnesorgane: Hermann
von Helmholtz als Festgruss zu seinem siebzigsten Geburtstag. Voss, 1891.
Kozlowski, S. Speed of thought ((Russian) trans. Google Translate). Computerra,
26-27, 2004.
S. Krashen. The input hypothesis and its rivals. Implicit and explicit learning of
languages, pages 45–77, 1994.
S.D. Krashen. Second language acquisition and second language learning. Oxford
University Press, 1981. ISBN 0080253385.
S.D. Krashen. The Power of Reading. Second Edition. Libraries Unlimited, 2004.
E.H. Lenneberg. Cognition in ethnolinguistics. Language, 29(4):463–471, 1953.
ISSN 0097-8507.
E.H. Lenneberg. Biological foundations of language. Wiley, 1967.
M.H. Long. The role of the linguistic environment in second language acquisition. Handbook of second language acquisition, 2:413–468, 1996.
J. Lucy. Language, culture, and mind in comparative perspective. In M. Achard and
S. Kemmer (ed.), Language, Culture, and Mind., chapter 1. Chicago University
Press, 2004.
J.A. Lucy. Language diversity and thought: A reformulation of the linguistic relativity hypothesis. Cambridge Univ Pr, 1992. ISBN 0521387973.
J.A. Lucy. Linguistic relativity. Annual review of anthropology, pages 291–312,
1997. ISSN 0084-6570.
46
J.A. Lucy and S. Gaskins. Grammatical categories and the development of classification preferences: A comparative approach. Language acquisition and conceptual development, pages 257–283, 2001.
D. Malacara. Color vision and colorimetry: theory and applications. Press Monograph Series. SPIE Press, 2002. ISBN 9780819442284.
R. Matasović. Etymological Dictionary of Proto-Celtic.
9004173366.
Brill, 2009.
ISBN
J.C. Maxwell. Theory of the perception of colors. Trans. R. Scottish Soc. Arts, 4:
394–400, 1856.
Sir Isaac Newton. New theory of light and colors. Philosophical Transactions, 6
(80):3075–3087, 1671.
S. Niemeier and R. Dirven. Evidence for linguistic relativity. John Benjamins
Publishing Company, 2000. ISBN 9027237050.
R.E. Núñez and E. Sweetser. With the future behind them: Convergent evidence from Aymara language and gesture in the crosslinguistic comparison
of spatial construals of time. Cognitive Science, 30(3):401–450, 2006. ISSN
1551-6709.
N. O’Donnell. Linguistic Relativity and the Sapir-Whorf Hypothesis. The influence of language on cognition and the implications for Second Language
Acquisition 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 and
Winston, 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. Semantic typology and spatial conceptualization. Language, 74(3):557–589, 1998.
ISSN 0097-8507.
S. Pinker. The language instinct: How the mind creates language. Harper Perennial
Modern Classics, 2000. ISBN 0060958332.
M. Pütz and M. Verspoor. Explorations in linguistic relativity. John Benjamins
Publishing 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 perception, 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 General
Semantics, 3(47):269–280, 1990.
E. Sapir and D.G. Mandelbaum. Selected writings of Edward Sapir: in language, culture and personality. University of California Press, 1949. ISBN
0520011155.
F. Saussure. Cours de linguistique générale, publié par Charles Bally et Albert
Séchehaye avec la collaboration de Albert Riedlinger. Lausanne et Paris, Payot,
1916.
A. Schaff, N. Chomsky, and O. Wojtasiewicz. Language and cognition. McGrawHill, 1973. ISBN 0070550808.
D.N. Sedley. Plato’s Cratylus. Cambridge Univ Pr, 2003. ISBN 0521584922.
L. Selinker. Interlanguage. IRAL-International Review of Applied Linguistics in
Language Teaching, 10(1-4):209–232, 1972. ISSN 0019-042X.
D.M. Singleton and Z. Lengyel. The age factor in second language acquisition: A
critical look at the critical period hypothesis. Multilingual Matters Ltd, 1995.
ISBN 185359301X.
D.I. Slobin. From “thought and language” to “thinking for speaking”. Rethinking 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 for
Speaking - Han Z.-H, Cadierno T, eds.(In press) Multilingual Matters, 2010.
C. Swoyer. Relativism. Stanford Encyclopedia of Philosophy, 2003.
The University of Wales.
Proto-Celtic to English Wordlist.
University of Wales Department of Celtic Languages Website, 2011.
URL
http://www.wales.ac.uk/Resources/Documents /Research/CelticLanguages/ProtoCelticEnglishWordlist.pdf , retrieved April 3rd,
2011.
48
B.L. Whorf, J.B. Carroll, and S. Chase. Language, thought, and reality: Selected
writings of Benjamin Lee Whorf, volume 2. MIT press Cambridge, MA, 1956.
L. Wittgenstein. Tractatus logico-philosophicus. Routledge & Paul, 1961.
T. Young. The Bakerian Lecture: On the theory of light and colours. Philosophical Transactions of the Royal Society of London, 92:12–48, 1802. ISSN 02610523.
49
Index
R2 values, 32
2-ary predicates, 27
3-ary predicates, 27
cognitive process, 2, 20
colour continuum, 25, 27
colour spectrum, 13, 25, 26, 31, 33, 42
Colour Terminology, 27
absolute-frame, 26
colourblindness, 27
adulthood, 17
comic strip, 22
Afrikaans, 32
communication breakdown, 17
Amazon, 10
comprehensibility, 17
Arabic, 42
computer based, 29
Aristotle, 5
computer programming, 10
Athanasopoulos, Panos, 21, 42, 43
conceptual difference, 21
cone, 28, 29
Babel-17, 11
containment relation, 21
Babylonians, 5
Corder, S.P., 17
beauty, 41
count noun, 8
behavior centered, 25
Coventry, Kenny, 21
Belfast, 27
Berlin and Kay - colour acquisition, 27 Cratylus, 5
Critical Period Hypothesis, 17
Berman, Ruth, 10
culture, 41
bilingualism, 16
biological constraints, 18
Boroditsky, Lera, 7, 8, 26, 41
boundary crossing, 20
Brazil, 10
Brother, Big, 11
Brown, James C., 11
Brown, Roger, 12
Cadierno, Teresa, 20, 42
Celtic languages, 27, 32
chess, 30
Chinese, 9
Chomsky, Noam, 14, 41
chroma, 28
Danish, 20, 21
data collection, 29
database, I, II
de Saussure, Ferdinand, 5, 6
decoding, 17
Delany, Samuel R., 11
domain centered, 26
Drivonikou, Gilda, 13
Dublin, 27
Dutch, 26, 32
electromagnetic waves, 28
elephant, 8
Elo Algorithm, 30, 32
50
Elo algorithm, 30
Hjelmslev, 5
empirical research, 2
Hoijer, 7
empty, 7
Hopi, 14
encoding, 17
horizontal, 9
English, 8–11, 16, 20–22, 25, 26, 32, HTML, I
41, 42
hue, 28
Esperanto, 11
human behaviour, 3, 5
Estonian, 25
human cognition, 11
Everett, Daniel, 8, 10
human experience, 41
Hungarian, 25
Facebook, 30
Facemash, 30
Iberia, 36
Finland, 26
identicality, 14
Finnish, 25, 41, 42
illuminated surroundings, 29
first-hand, 9
immersive environment, 16
football, 30
index, 17
foreign accent, 18
Indo-European, 25, 32
foreign grammar rules, 43
Indonesian, 41, 42
foreign language, 2
infra-red, 28
foreign language literature, 17
input, 16
foreign lexicon, 43
interlanguage, 16, 17
fossilisation, 17, 18
internet, 29
France, 31
intrinsic-frame, 27
French, 9, 16, 32
Irish, 20, 27, 32
Frog Story Project, 10
Italian, 32, 42
Italy, 31
gasoline drums, 7
Ithkuil, 11
German, 10, 20, 21, 32
Germanic languages, 32
Japanese, 21, 42
gerund, 23
K coefficient, 31
Gestalt, 25
Klinkenberg, 5
gesture, 9
Kozlowski, S., 11
Goidelic languages, 32
Krashen, Stephen, 16
Google, X
grammar, 17
grammaticality judgement, 22
green-blue, 27
green-blue continuum, XIII, 30
green-blue spectrum, 2
Hermogenes, 5
hexadecimal, VI
L1, 21, 22, 32
L2, 16, 20–23
language, 25, 41
language acquisition, 10
language and thought, 6, 10
language transfer, 20
language window, 17
51
learner language, 16
Lenneberg, Eric, 12, 17
linear regression, 32
Linguistic Determinism, 2, 6, 14, 33,
41, 42
Linguistic Relativity, 2, 5, 6, 10–14, 20,
21, 23, 27, 33, 41, 42
linguistic relativity, 21
Loglan, 11, 12
long term memory, 9
Long, M., 17
loop, III
Lucy, John, 6–8, 21, 25, 26, 41
malleable, 8
Mandarin, 9, 42
manner, 22
manner-of-motion verb, 21
mass noun, 8
material, 8
mathematics, VIII
mediation, 13
memory, 41
mental compass, 26
Mexico, 27
midpoint, VI
mode of thinking, 10
monoglot native speaker, 22
monolingual speaker, 22
multilingual, 2
multilingualism, 16
MyISAM, I
native language, II
native speaker, 20, 21, 42
native-like proficiency, 18
neural networks, 26
neuron, 29
Newspeak, 11
Newton, Sir Isaac, 28
Nineteen Eighty-Four, 11
nomos, 5
non-plural-marking language, 20
non-pro-drop languages, 20
Norwegian, 25
number, 22
numeracy, 10
obstacle, 42
one, two many language, 10
Orwell, George, 11
outer nuclear layer, 29
parameter resetting, 43
partative, 9
path, 22
Paul Kay, 26
peanuts, 8
Pederson, E., 21, 26
photoreceptor cells, 29
PHP, I, VI
physiology, 14
physis, 5
picture description, 20
pidgin, 16
Pinker, Steven, 2, 14, 41
Pirahã language, 10
Pirahã people, 10
Plato, 5
plural-marking language, 20
Portuguese, 32
primary key, II
prism, 28
pro-drop languages, 20
programming language, 12
Proto-Celtic, 28
psychology, 22
puberty, 17
QPT, 22
quantificational unit, 8
Quijada, John, 11
52
reading, 17
red, 28
relational model, I
relative-frame, 26
retina, 28, 29
RGB, II, III, VI, 32, 36
rod-cells, 29
Romance languages, 21, 32
Russian, 20, 21, 42
Sweden, 26
Swedish, 25, 41
Sylvester and Tweety Bird, 22
synesthaesia, 5
target language, 17
tensing, 9
Tepehua, 27
thinking for speaking, 10, 22, 23, 41–
43
Sapir, Edward, 6, 7
thought, 41
Sapir-Whorf, 6, 7, 12, 13
Totonacan language, 27
satellite-framed language, 20–22
transition, 30
saturation, 28
translate, 9
trend line, 33
Science Fiction, 10
score, 32
Turkish, 9, 10
Second Language Acquisition, 2, 16, 17, Tzeltal, 26
20, 42
ultimate confusion, 35
second language learners, 21
ultra-violet, 28
Selinker, L., 17
Universal Grammar, 43
semiotics, 5
Ural-Altaic, 25
server, II
USA, 22
session, II
shape, 8
verb-framed language, 20–22
sharp downward kick, 34
vertical, 9
signified, 5
violet, 28
signifier, 5
vocabulary production, 20
Singleton, David, 17
vocabulary recognition, 20
Slobin, Daniel I., 8, 10, 20, 41
von Humboldt, Wilhelm, 5, 6
social media, III
Socrates, 5
website, 42
Spanish, 10, 16, 20–22, 32
weighted data, 33
spatial relations, 21, 25, 26
Welsh, 27
speaking, 17
Whorf, Benjamin L., 2, 6, 7, 10, 12, 41
SQL, I
writing, 17
Stam, Gale, 22, 42
Yucatec Maya, 8, 25
structure centered, 25
Strømnes, Frode, 25, 41
Zuckerberg, Mark, 30
substance, 22
support relation, 21
survey, 29
53
Appendix A
Website Code
In this appendix will be the SQL and PHP code related to the website used
to run the expreiment. Overall it consisted of a MySQL database along with
several PHP/HTML files. There were many more files on the website than are
relevant to the data gathering process, such as those pages on which anyone
could check up on the current statistics.
A.1
Database Structure
The database contains 2 two table templates, one representing each individual
comparison of 2 colours, and the other, which is replicated over 80 times, represents the information about each individual colour. It is, of course, a databse
in 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 colour
as the loser and the language code of the language being analysed in as the
language. The auto incrementing attribute battle_id serves as the unique
identifier of each tuple. The default characterset is explicitly defined as UTF-8
and the AUTO_INCREMENT value is set to 1 (and is used in conjunction with the
colour_id attribute. MyISAM, which is the default engine in MySQL is explicitly
set 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 following the template outlined below. Essentially, what is represented is each
colour in the language. Each colour has a colour_id attribute which contains
the unique ID of each colour for that language, which is the primary key. The
g and b attributes represent the GB in that colour’s RGB value (there is no R
as there is no red in the green-blue spectrum). The wins and losses attributes
store how many times that particular colour, in that particular language has
won or lost, respectively, a ‘battle’. Finally, the score attribute stores the colours
score and is updated after every battle. The theory being that the colour with
the value closest to the default value of 1500 would be the dividing point in the
continuum.
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 d e f a u l t ’ 1500 ’ ,
wins i n t (10) unsigned NOT NULL d e f a u l t ’ 0 ’ ,
losses i n t (10) unsigned NOT NULL d e f a u l t ’ 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 only
6 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 more
automated manner. This is it’s sole purpose, and for this reason (combined with
security risks caused by not doing so) it was removed from the server immediately after its use. index.php is the front page of the site, providing information
about the study, as well as a drop down menu for the user to select his or her
native language. From there, the site is directed to session.php, which begins
the user’s session. It stores the user’s native language, and sets the time at which
the session began, before redirecting to compare.php. In compare.php, the user
makes a decision between two colours. This choice is evaluated remotely by
rate.php, which immediately afterwards will redirect the user back to a new
instance of compare.php. timeup.php is only acessed if a user’s session exceeds
20 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 increments of six (so as to keep reasonable the number of possible colour combinations on a page). The G and B values of each RGB can never be zero, so as to
avoid dividing by zero in the operations performed later in rate.php. Then the
battles table is created, followed by a loop which creates the tables for all of
the languages as per the template in A.1.2.
<?php
include ( ’ mysql . php ’ );
$langs = array ( " a f " , " a r " , " an " , " eu " , " be " , " bn " , " bh " , " bs " , " br " , " bg " , " e s " , " ca " , " zh " , " hr
" , " c s " , " da " , " nl " , " en " , " e t " , " f i " , " f r " , " ka " , " de " , " e l " , " he " , " h i " , " hu " , " i d " , " ga " , "
i l " , " i t " , " j a " , " ks " , " kk " , "km" , " ko " , " ku " , " l o " , " l v " , " l t " , " l b " , "mk" , "mg" , "ms" , " mt " ,
" nn " , " oc " , " pa " , " f a " , " pl " , " ps " , " pt " , "rm" , " ro " , " ru " , " gd " , " s r " , " sk " , " s l " , " so " , " s t "
, " su " , " sw " , " sv " , " t l " , " t y " , " t g " , " t a " , " th " , " t r " , " uk " , " ur " , " uz " , " v i " , " cy " , " f y " , " xh
" , " y i " , " yo " , " za " , " zu " );
f o r ($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 v a r c h a r ( 2 ) NOT NULL,
PRIMARY KEY( b a t t l e _ i d ) ,
KEY winner ( winner )
) ENGINE=MyISAM DEFAULT CHARSET=u t f 8 AUTO_INCREMENT=1; " );
foreach ($langs as $l){
mysql_query( "CREATE TABLE IF NOT EXISTS " .$l. " (
c o l o u r _ i d 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,
s c o r e i n t ( 1 0 ) unsigned NOT NULL d e f a u l t ’ 1 5 0 0 ’ ,
wins i n t ( 1 0 ) unsigned NOT NULL d e f a u l t ’ 0 ’ ,
l o s s e s i n t ( 1 0 ) unsigned NOT NULL d e f a u l t ’ 0 ’ ,
PRIMARY KEY( c o l o u r _ i d )
) ENGINE=MyISAM DEFAULT CHARSET=u t f 8 AUTO_INCREMENT=1 ; " );
$query = " INSERT INTO " .$l. " ( g , b ) VALUES " .implode( ’ , ’ , $shades). " " ;
i f (! mysql_query($query)) {
p r i n t mysql_error ();
}
}
?>
A.2.2
index.php
The site index. It has been described above, and is really quite simple. I include
share 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" " h t t p : / /www. w3 . org /TR/
xhtml1 /DTD/ xhtml1− t r a n s i t i o n a l . dtd " >
<html xmlns= " h t t p : / /www. w3 . org /1999/ xhtml " >
<head>
< l i n k r e 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 n k r e l = " s h o r t c u t i c o n " href = " f a v i c o n . i c o " />
< s c r i p 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 sets
the clock for the 20 minute time allowance, before redirecting the browser to
compare.php.
<?php
session_start ();
$_SESSION[’lang ’] = $_POST[’lang ’];
$_SESSION[’start ’] = time ();
header(’location: ./ compare.php’);
?>
A.2.4
compare.php
Initially, the script verifies that a valid session is in operation, and if not redirects the browser to the site index. It then verifies that the session has not
been going on longer than 20 minutes , and if it has, redirects the browser to
timeup.php. The rgb2html function converts a set of RGB values to its hexadecimal equivalent. After this comes the random selection of 2 colours. After
this is the calculation of which colour is the midpoint, as described in 5.4. Then
comes the creation of the table used to represent the spectrum, with the point
corresponding to the midpoint to be set to black. The last thing the PHP script
does is calculate the total number of clicks performed for that language, doing
so by summing the total number of wins for each language.
<?php
session_start ();
if(!( isset($_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
$color
$color
return
= (strlen($r) < 2?’0’:’’).$r;
.= (strlen($g) < 2?’0’:’’).$g;
.= (strlen($b) < 2?’0’:’’).$b;
$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 over
another. The mathematics of it are described in 5.3. Initial tests are performed
on 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 = ".$_SESSION[’lang ’]." ");
}
header(’location: ./ compare.php’);
}
?>
A.2.6
timeup.php
On this page, the session is unset, i.e. the start time and the user’s chosen language is dropped. This is done to prevent strain to a user’s eyes. They are
offered a redirect to Google.
<?php
if(isset($_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 -350 px.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 data
gathered during the Empirical Research part of this paper. The third part of this appendix gives the entirety of the data gathered for each language, the second part details
the colour extremes of each language, and the first section gives the data for the language as to which point along the continuum was least easy to difinitively call green or
blue, 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 the
point in the spectrum at which that particular language differentiates between green
and blue.
Green
253
Blue
253
Win Percentage
57.5
Table C.1: The colour in the middle for English
Green
241
Blue
253
Win Percentage
41.6̇
Table C.2: The colour in the middle for French
XIII
Green
205
Blue
253
Win Percentage
50
Table C.3: The colour in the middle for Italian
Green
223
Blue
253
Win Percentage
37.5
Table C.4: The colour in the middle for Spanish
Green
253
Blue
247
Win Percentage
50
Table C.5: The colour in the middle for Portuguese
Green
253
Blue
247
Win Percentage
72.5
Table C.6: The colour in the middle for Dutch
Green
247
Blue
253
Win Percentage
52.6
Table C.7: The colour in the middle for Afrikaans
Green
253
Blue
223
Win Percentage
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 highest
percentage 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’, and
is, accordingly, the extreme blue for it’s language.
Green
253
7
Blue
19
253
Win Percentage
93.3
3.2
Table C.9: The colour extremes for English
XIV
Green
253
1
Blue
7
253
Win Percentage
100
4.3
Table C.10: The colour extremes for French
Green
253
55
Blue
1
253
Win Percentage
100
4.8
Table C.11: The colour extremes for Italian
Green
253
1
Blue
1
253
Win Percentage
100
0
Table C.12: The colour extremes for Spanish
Green
253
31
Blue
253
253
Win Percentage
100
0
Table C.13: The colour extremes for Portuguese
Green
253
1
Blue
7
253
Win Percentage
97.7
0
Table C.14: The colour extremes for Dutch
Green
253
49
Blue
1
253
Win Percentage
100
0
Table C.15: The colour extremes for Afrikaans
Green
253
31
Blue
49
253
Win Percentage
100
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 order
to perform the analysis. Please feel free to do your own analysis on it!
XV
Table C.17: All data gathered for English
Green
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
Blue
19
31
7
49
25
1
43
37
55
13
61
73
79
115
97
85
67
91
121
103
127
133
109
139
157
145
151
163
181
193
169
253
205
211
223
187
199
Score
2374
2300
2617
2023
2440
2705
2224
2232
2104
2427
2118
2023
2205
1896
1988
2055
1764
1242
2033
1932
1886
1893
1862
1889
1784
1903
1874
1806
1664
1559
1739
1672
1503
1455
1665
2704
1587
XVI
Wins
153
147
133
148
130
149
139
143
143
144
118
160
130
132
119
140
126
142
133
127
125
130
95
125
103
118
99
112
83
93
91
107
89
90
91
88
82
Losses
11
12
11
13
12
15
14
15
15
16
17
25
22
27
25
32
30
35
34
36
36
39
32
44
39
50
42
48
49
56
56
69
63
64
65
65
61
Table C.17: All data gathered for English (continued)
Green
253
253
253
253
217
253
241
253
253
205
235
247
229
223
211
199
175
181
169
193
187
157
139
163
133
121
151
127
145
103
79
85
73
115
91
97
67
49
Blue
217
247
175
253
253
235
253
241
229
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
Score
1606
1467
1965
1573
1627
1648
1686
1410
1461
1503
1594
1679
1726
1584
361
1569
1431
1376
1360
1453
1427
1315
1178
1298
1319
2228
1241
1118
1201
1057
1115
1140
1009
976
966
916
910
839
XVII
Wins
86
95
88
96
77
77
79
80
72
78
69
68
72
70
64
64
63
49
58
49
58
56
51
44
34
38
39
39
33
35
29
32
29
29
24
23
24
18
Losses
66
73
68
81
74
75
77
78
74
88
80
81
88
90
96
98
110
87
104
90
109
113
107
123
99
112
116
133
115
136
113
127
122
140
120
120
131
127
Table C.17: All data gathered for English (continued)
Green
109
61
1
43
19
55
37
13
31
25
7
Blue
253
253
253
253
253
253
253
253
253
253
253
Score
362
996
545
853
869
906
1086
761
593
697
374
Wins
17
16
16
14
15
16
14
13
12
7
5
Losses
137
130
135
129
142
154
154
155
148
138
153
Table C.18: All data gathered for Afrikaans
Green
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
Blue
1
7
31
25
43
55
73
97
115
61
13
37
85
19
169
139
127
49
103
79
151
145
Score
2536
1725
1680
1749
1715
1711
1640
1605
1620
1683
1749
1658
1670
1660
979
1660
1576
1678
1605
1590
1588
1641
XVIII
Wins
22
10
17
15
15
15
15
9
13
24
10
14
23
20
12
19
11
14
19
9
14
16
Losses
0
0
1
1
1
1
1
1
2
4
2
3
5
5
3
5
3
4
6
3
5
6
Table C.18: All data gathered for Afrikaans (continued)
Green
253
253
253
253
253
253
241
253
253
253
253
253
253
235
253
253
253
253
193
253
253
247
223
253
253
211
253
187
175
181
217
199
151
205
145
253
157
Blue
109
157
67
181
253
229
253
187
133
253
247
175
241
253
91
121
205
163
253
199
193
253
253
223
217
253
211
253
253
253
253
253
253
253
253
235
253
Score
1608
1591
1530
1527
1634
1518
1680
1554
1517
1548
1521
1580
1486
1657
1561
1482
1509
1470
1601
1470
1483
1595
1631
1444
1440
1584
1405
1591
1535
1591
1528
1589
1474
1568
1484
1378
1482
XIX
Wins
13
10
12
7
15
13
11
14
10
10
8
12
13
10
7
9
9
11
12
9
10
10
12
11
9
8
7
6
10
8
8
8
7
7
5
6
4
Losses
5
4
5
3
7
7
6
8
6
6
5
8
9
7
5
7
7
9
10
8
9
9
12
12
10
9
8
7
12
10
10
12
11
11
8
10
8
Table C.18: All data gathered for Afrikaans (continued)
Green
229
139
133
97
127
121
79
163
103
19
109
169
61
115
13
43
1
67
85
91
7
37
25
73
31
49
55
Blue
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
Score
1460
1436
1960
1388
1413
1385
1643
1454
1398
1285
1368
1426
1335
1364
1174
1176
1098
1331
1351
1234
1029
1305
1290
1301
1319
1183
330
Wins
5
4
6
5
5
6
3
5
4
3
2
3
3
2
3
4
2
2
2
3
2
1
1
1
0
0
0
Losses
11
9
14
12
12
15
9
15
13
13
9
14
16
12
19
26
14
14
14
23
18
13
14
16
13
25
19
Table C.19: All data gathered for Dutch
Green
253
253
253
253
253
Blue
7
31
43
1
61
Score
2051
2000
1920
2365
1789
XX
Wins
43
49
48
43
29
Losses
1
2
2
2
2
Table C.19: All data gathered for Dutch (continued)
Green
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
229
253
253
253
253
253
253
Blue
19
73
13
79
67
37
139
109
91
169
97
115
49
25
103
127
55
151
85
187
133
199
247
163
157
121
181
205
229
253
235
253
145
217
241
193
175
223
Score
1883
1817
1962
1788
1830
1704
1713
1737
1831
1716
1755
1758
2083
1796
1673
1697
1596
1655
1849
1664
1628
1587
1490
1593
1603
1584
1572
1480
1420
1569
1410
1628
1522
1460
1410
1161
1533
1401
XXI
Wins
39
34
41
36
33
36
32
33
51
34
38
33
37
30
36
28
33
29
25
28
33
38
29
30
36
25
22
27
37
25
22
23
19
24
29
31
22
21
Losses
3
3
4
4
4
5
5
6
10
7
8
7
8
7
9
7
9
9
8
9
12
14
11
12
16
12
12
15
24
18
17
18
15
20
25
28
21
21
Table C.19: All data gathered for Dutch (continued)
Green
217
205
253
253
235
241
211
175
247
223
139
181
199
145
91
193
187
169
157
79
115
109
163
151
133
97
121
73
55
67
103
43
61
31
127
13
37
25
Blue
253
253
253
211
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
Score
1616
1579
1539
1476
1613
1623
1556
1439
1604
1547
1441
1644
1466
1443
1349
1471
1438
1429
1431
1258
1321
1330
1389
1352
1360
1219
1245
1325
1230
1215
1227
1205
1181
1117
1181
991
1149
1037
XXII
Wins
28
29
27
19
18
22
19
19
17
13
15
19
16
11
10
12
12
9
14
14
11
10
9
9
11
9
9
8
8
7
6
5
6
5
5
4
3
4
Losses
28
29
28
20
21
26
23
30
27
24
30
39
33
23
22
28
29
22
35
39
32
34
31
31
40
33
38
35
36
36
38
33
41
37
43
45
35
49
Table C.19: All data gathered for Dutch (continued)
Green
85
19
7
49
1
Blue
253
253
253
253
253
Score
1156
991
880
1028
226
Wins
3
3
2
2
0
Losses
38
50
35
43
32
Table C.20: All data gathered for French
Green
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
Blue
7
19
25
37
79
1
91
97
109
67
43
61
49
121
13
55
139
145
115
31
73
163
103
85
127
151
223
169
Score
1976
1837
1832
1690
1697
2371
1760
1684
1692
1715
2000
1724
1697
1713
1790
1644
1612
1674
1560
1597
1594
1080
1586
1584
1551
1632
1495
1525
XXIII
Wins
24
19
21
14
23
22
30
20
19
18
23
23
22
19
11
15
14
22
15
10
8
16
12
7
13
15
10
13
Losses
0
0
0
0
1
2
3
2
2
2
3
3
3
3
2
3
3
5
4
3
3
6
5
3
6
7
5
7
Table C.20: All data gathered for French (continued)
Green
253
253
253
253
253
247
253
253
253
199
253
187
253
127
253
217
253
253
181
193
253
241
169
115
229
253
253
205
151
235
223
211
175
97
55
121
67
49
Blue
175
133
157
253
181
253
217
187
229
253
247
253
241
253
193
253
205
235
253
253
211
253
253
253
253
253
199
253
253
253
253
253
253
253
253
253
253
253
Score
1499
1584
1518
1557
1516
1682
1473
1437
1406
1574
1429
1582
1425
1513
1436
1440
1446
1408
1519
1544
1396
1528
1478
1488
1579
1458
1189
1493
1494
3005
1560
1545
1546
1378
1317
1450
1379
1355
XXIV
Wins
13
11
7
12
12
10
15
13
13
9
8
10
9
8
12
10
9
10
6
8
5
5
11
6
10
6
7
10
8
9
5
6
5
7
5
6
4
4
Losses
7
6
4
7
8
7
12
11
12
9
8
11
10
9
14
12
11
13
8
11
7
7
16
9
15
9
11
16
14
16
9
12
12
17
14
17
12
14
Table C.20: All data gathered for French (continued)
Green
163
43
109
157
133
13
91
73
79
145
25
103
85
31
7
19
37
139
61
1
Blue
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
Score
1398
1286
1340
1370
1339
1179
1324
1271
1324
1346
1194
1238
1292
1177
1129
1256
1242
1363
1195
0
Wins
3
3
3
3
4
3
3
2
2
2
2
2
2
1
1
1
1
1
1
1
Losses
13
15
16
16
23
18
18
13
15
16
18
18
20
13
14
15
18
18
21
22
Table C.21: All data gathered for Italian
Green
253
253
253
253
253
253
253
253
253
253
253
253
253
Blue
1
61
7
73
121
13
127
19
25
103
133
31
55
Score
2412
1788
1949
1680
1759
1928
1628
1803
1719
1661
1646
1622
1679
XXV
Wins
12
26
17
15
29
27
11
19
15
14
20
12
18
Losses
0
1
1
1
2
2
1
2
2
2
3
2
3
Table C.21: All data gathered for Italian (continued)
Green
253
253
253
253
253
253
241
253
253
253
253
253
253
253
253
253
253
253
253
181
253
247
211
217
73
253
253
205
253
253
163
199
229
253
187
253
253
169
Blue
67
43
85
91
79
97
253
109
139
37
115
181
151
163
49
199
235
145
169
253
211
253
253
253
253
175
193
253
223
253
253
253
253
157
253
205
217
253
Score
1633
1664
1658
1660
1578
1672
1676
1591
1547
1658
1526
1504
1529
1582
1537
1498
1519
911
1535
1579
1458
1715
1527
1551
850
1519
1499
146
1548
1483
1518
1423
1562
1442
1604
1447
1394
1469
XXVI
Wins
16
18
17
12
15
20
16
14
24
10
18
12
13
13
7
12
10
9
12
9
10
5
11
7
11
11
13
9
6
7
10
10
8
9
9
9
8
7
Losses
3
4
4
3
4
6
5
5
9
4
8
6
7
7
4
7
6
6
8
6
7
4
9
6
10
10
13
9
6
7
11
11
9
11
11
11
10
10
Table C.21: All data gathered for Italian (continued)
Green
253
253
253
115
139
97
127
175
235
253
85
223
79
31
253
103
145
49
43
109
121
7
37
133
19
1
193
91
61
67
157
25
13
151
55
Blue
241
247
229
253
253
253
253
253
253
187
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
Score
1427
1364
1397
1456
2077
1415
1426
3344
1494
1393
1410
1501
2214
1376
1381
1342
1402
1267
1291
1369
1378
1271
1305
1410
1257
17
1334
1344
1170
1277
1386
1246
1159
1325
1289
XXVII
Wins
7
13
6
6
6
7
8
6
5
10
5
8
5
4
3
5
4
6
4
3
3
4
4
2
2
2
3
2
3
1
1
1
1
1
1
Losses
10
19
9
10
10
12
14
12
10
24
13
23
15
13
10
17
15
25
18
14
14
19
20
10
11
13
20
14
25
11
11
13
15
16
20
Table C.22: All data gathered for Spanish
Green
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
217
253
229
253
253
253
253
211
253
253
253
253
253
253
199
241
253
Blue
7
31
37
1
49
25
43
139
91
121
13
79
73
67
127
181
133
61
115
217
253
253
253
103
145
229
199
253
97
151
175
235
85
157
253
253
193
Score
1726
1685
1644
2183
1628
1652
1596
1561
1563
1551
1545
1525
1650
1586
1596
1620
1589
1549
1553
1503
1717
1599
1646
1539
1524
1469
1490
1524
1506
1552
1503
1482
1511
1526
1554
1592
1498
XXVIII
Wins
11
8
8
7
7
6
5
5
4
3
2
2
11
7
6
15
9
8
8
8
4
4
6
6
6
6
4
4
2
5
6
6
3
3
4
4
5
Losses
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
3
2
2
2
2
1
1
2
3
3
3
2
2
1
3
4
4
2
2
3
3
4
Table C.22: All data gathered for Spanish (continued)
Green
253
253
253
169
253
235
253
181
139
151
253
193
253
133
253
223
253
49
55
205
85
121
175
253
127
109
145
253
43
247
103
67
115
187
7
19
157
163
Blue
223
55
109
253
187
253
19
253
253
253
163
253
211
253
205
253
247
253
253
253
253
253
253
253
253
253
253
241
253
253
253
253
253
253
253
253
253
253
Score
1478
1487
977
1553
1525
1530
1502
1563
1500
1486
1507
1500
1478
1516
1486
1459
1385
1394
1431
1511
1508
1485
1494
1481
2126
1458
1484
1393
1434
1462
1418
1385
1443
1462
1211
1368
1404
1379
XXIX
Wins
7
4
4
3
3
2
1
1
3
3
3
3
3
4
2
3
3
4
4
3
2
2
1
1
2
1
1
2
1
1
2
1
1
1
1
1
1
1
Losses
6
4
4
3
3
2
1
1
4
4
4
4
4
6
3
5
5
7
8
6
4
4
2
2
5
3
3
7
4
5
11
6
6
7
8
9
9
10
Table C.22: All data gathered for Spanish (continued)
Green
1
13
97
37
25
31
61
91
73
79
253
Blue
253
253
253
253
253
253
253
253
253
253
169
Score
653
1219
1365
1386
1384
1393
1366
1406
1457
1483
1488
Wins
0
0
0
0
0
0
0
0
0
0
0
Losses
9
8
8
7
6
5
5
5
3
1
1
Table C.23: All data gathered for Portuguese
Green
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
Blue
13
19
31
49
79
73
109
37
7
67
1
43
61
91
97
169
199
25
103
55
85
163
Score
1821
1692
1698
1741
1649
1727
1659
1687
1979
1705
2461
1619
1650
1652
1718
1583
1592
1677
1614
1548
1606
1504
XXX
Wins
17
12
12
19
12
18
18
12
10
18
8
8
15
15
12
12
11
13
12
6
9
9
Losses
0
0
0
0
0
1
1
1
1
2
1
1
2
2
2
2
2
3
3
2
3
3
Table C.23: All data gathered for Portuguese (continued)
Green
253
253
253
253
253
253
253
253
253
253
253
253
157
229
211
253
253
223
253
199
217
253
253
253
253
193
175
253
181
253
145
253
205
109
163
187
121
Blue
181
193
229
121
175
151
157
115
253
145
133
217
253
253
253
127
205
253
247
253
253
241
211
223
187
253
253
139
253
253
253
235
253
253
253
253
253
Score
1538
1486
1482
329
1501
3032
1577
1541
1609
1497
1506
1473
1561
1555
1652
1499
1473
1582
1445
1559
1565
1421
1450
1498
1496
1555
1501
1412
1518
1505
1483
223
1496
1542
1511
1475
1487
XXXI
Wins
9
6
11
8
8
10
10
7
8
9
7
7
8
8
10
4
7
4
4
7
6
6
5
5
4
4
3
4
6
6
3
2
5
4
4
3
4
Losses
3
2
4
3
3
4
4
3
5
7
6
6
7
7
9
4
7
4
4
8
7
7
6
6
5
5
4
6
9
10
6
4
11
9
9
7
10
Table C.23: All data gathered for Portuguese (continued)
Green
247
139
85
241
103
19
97
91
37
169
55
49
1
67
115
79
43
61
133
7
13
25
31
73
127
151
235
Blue
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
Score
1518
1430
1402
1477
1399
1349
1349
1375
1313
1419
1340
1346
764
1558
1396
1331
1254
1329
1354
1189
1237
1281
1302
1418
1392
1420
1441
Wins
3
5
3
3
4
2
2
2
2
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
Losses
8
14
9
9
13
10
10
12
14
8
9
11
12
12
12
13
14
15
16
7
12
11
13
6
7
4
6
Table C.24: All data gathered for Irish
Green
253
253
253
253
253
Blue
1
7
19
37
49
Score
2921
1826
1608
1621
1683
XXXII
Wins
3
6
5
7
13
Losses
0
0
0
0
0
Table C.24: All data gathered for Irish (continued)
Green
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
199
253
205
253
253
253
181
253
253
79
253
247
133
223
Blue
85
115
151
169
67
61
31
13
91
97
79
133
43
55
73
109
25
163
121
145
139
211
241
103
253
199
253
247
175
127
253
181
235
253
187
253
253
253
Score
1613
1582
1608
1542
1654
1635
1656
1617
1592
1580
1557
1583
1640
1610
1634
1587
1644
1584
1561
1545
1546
1495
1524
1502
1555
1486
1582
1498
1484
1447
1597
1399
1455
1493
1485
1559
1525
1649
XXXIII
Wins
8
7
7
3
15
10
9
8
7
7
6
11
10
10
13
13
8
8
7
7
6
5
7
4
2
6
8
10
7
6
6
7
7
5
7
5
5
5
Losses
0
0
0
0
1
1
1
1
1
1
1
2
2
2
3
3
2
2
2
2
2
2
3
2
1
3
4
5
4
4
4
5
5
5
7
5
6
6
Table C.24: All data gathered for Irish (continued)
Green
253
193
253
229
253
1
109
253
187
163
253
253
211
253
157
175
241
61
103
121
253
7
115
127
139
145
151
217
235
43
67
85
169
19
13
73
55
25
Blue
253
253
223
253
253
253
253
157
253
253
193
205
253
217
253
253
253
253
253
253
229
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
253
Score
1502
1499
1476
1515
1468
435
1474
1433
1514
1490
1397
1455
3299
1449
1502
1482
1559
1402
1445
1388
91
1232
1412
1424
1446
1407
1448
1478
1487
1442
1388
1402
1399
1316
1285
1377
1328
1202
XXXIV
Wins
5
3
3
3
3
2
4
2
2
4
3
3
1
2
5
2
3
3
2
2
1
1
2
1
1
3
1
1
1
1
2
1
1
1
1
1
1
0
Losses
6
4
4
4
4
3
6
3
3
8
6
6
2
4
12
5
8
9
6
7
4
5
10
5
5
15
5
5
5
6
12
7
7
8
9
10
15
9
Table C.24: All data gathered for Irish (continued)
Green
31
37
49
91
97
Blue
253
253
253
253
253
Score
1348
1034
1337
1373
1363
XXXV
Wins
0
0
0
0
0
Losses
10
7
10
7
8