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Optimal categorisation: the nature of nominal classification systems

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Abstract

The debate as to whether language influences cognition has been long standing but has yielded conflicting findings across domains such as colour and kinship categories. Fewer studies have investigated systems such as nominal classification (gender, classifiers) across different languages to examine the effects of linguistic categorisation on cognition. Effective categorisation needs to be informative to maximise communicative efficiency but also simple to minimise cognitive load. It therefore seems plausible to suggest that different systems of nominal classification have implications for the way speakers conceptualise relevant entities. A suite of seven experiments was designed to test this; here we focus on our card sorting experiment, which contains two sub-tasks — a free sort and a structured sort. Participants were 119 adults across six Oceanic languages from Vanuatu and New Caledonia, with classifier inventories ranging from two to 23. The results of the card sorting experiment reveal that classifiers appear to provide structure for cognition in tasks where they are explicit and salient. The free sort task did not incite categorisation through classifiers, arguably as it required subjective judgement, rather than explicit instruction. This was evident from our quantitative and qualitative analyses. Furthermore, the languages employing more extreme categorisation systems displayed smaller variation in comparison to more moderate systems. Thus, systems that are more informative or more rigid appear to be more efficient. The study implies that the influence of language on cognition may vary across languages, and that not all nominal classification systems employ this optimal trade-off between simplicity and informativeness. These novel data provide a new perspective on the origin and nature of nominal classification.
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OPEN ACCESS
EDITORS
Miguel Oliveira, Jr. (UFAL)
René Almeida (UFS)
REVIEWERS
Aγγελική Αλβανούδη (A.U.Th.)
Alexander Cobbinah (USP)
ABOUT THE AUTHORS
Alexandra Grandison
Formal Analysis, Funding Acquisition,
Methodology, Project Administration,
Supervision, Validation, Writing
Review and Editing.
Michael Franjieh
Conceptualization, Data Curation,
Formal Analysis, Funding Acquisition,
Investigation, Project Administration,
Visualization, Writing Review
and Editing.
Lily Greene
Formal Analysis, Writing
Original Draft.
Greville G. Corbett
Conceptualization, Funding
Acquisition, Project Administration,
Supervision, Writing Review
and Editing.
DATES
Received: 07/19/2021
Accepted: 09/17/2021
Published: 12/03/2021
HOW TO CITE
GRANDISON, A.; FRANJIEH, M.;
GREENE, L.; CORBETT, G. G. (2021).
Optimal Categorisation: The Nature of
Nominal Classification Systems.
Cadernos de Linguística
, v. 2, n. 1, e393.
RESEARCH REPORT
OPTIMAL CATEGORISATION:
THE NATURE OF NOMINAL
CLASSIFICATION SYSTEMS
Alexandra GRANDISON
School of Psychology University of Surrey
Michael FRANJIEH
Surrey Morphology Group University of Surrey
Lily GREENE
School of Psychology University of Surrey
Greville G. CORBETT
Surrey Morphology Group University of Surrey
ABSTRACT
The debate as to whether language influences cognition has been long
standing but has yielded conflicting findings across domains such as
colour and kinship categories. Fewer studies have investigated systems
such as nominal classification (gender, classifiers) across different
languages to examine the effects of linguistic categorisation on cognition.
Effective categorisation needs to be informative
to maximise
communicative efficiency but also simple
to minimise cognitive load. It
therefore seems plausible to suggest that different systems of nominal
classification have implications for the way speakers conceptualise
relevant entities. A suite of seven experiments was designed to test this;
here we focus on our card sorting experiment, which contains two sub-
tasks a free sort and a structured sort. Participants were 119 adults
across six Oceanic languages from Vanuatu and New Caledonia, with
classifier inventories ranging from two to 23. The results of the card
sorting experiment reveal that classifiers appear to provide structure for
cognition in tasks where they are explicit and salient. The free sort task
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did not incite categorisation through classifiers, arguably as it required
subjective judgement, rather than explicit instruction. This was evident
from our quantitative and qualitative analyses. Furthermore, the
languages employing more extreme categorisation systems displayed
smaller variation in comparison to more moderate systems. Thus,
systems that are more informative or more rigid appear to be more
efficient. The study implies that the influence of language on cognition
may vary across languages, and that not all nominal classification
systems employ this optimal trade-off between simplicity and
informativeness. These novel data provide a new perspective on the
origin and nature of nominal classification.
SUMÉ
Le débat concernant l’influence de la langue sur la cognition existe depuis
longtemps et a produit des résultats contradictoires dans des domaines
tels que les catégories de couleur et de parenté. Rares, cependant, sont
les travaux qui ont étudié la variation à travers les langues des systèmes
de classification nominale (genre, classificateurs) afin d’examiner les
effets de la catégorisation linguistique sur la cognition. Une
catégorisation efficace doit être informative pour maximiser l’efficacité
de la communication, mais aussi simple pour minimiser la charge
cognitive. Il semble donc plausible que différents systèmes de
classification nominale puissent influer sur la façon dont les locuteurs
conceptualisent les entités pertinentes. Nous avons conçu une série de
sept expériences pour tester cette hypothèse. Nous nous concentrons ici
sur une expérience de tri de cartes, qui contient deux sous-tâche — un tri
libre et un tri structuré. Les 119 participants sont des adultes, locuteurs de
six langues océaniques du Vanuatu et de la Nouvelle-Calédonie. Ces
langues ont des inventaires de classificateurs allant de deux à 23. Les
résultats de l'expérience de tri de cartes révèlent que les classificateurs
semblent fournir une structure pour la cognition dans les tâches quand ils
sont explicites et saillants. Nos analyses quantitatives et qualitatives
montrent que la tâche de tri libre n’a pas incité à la catégorisation au
moyen de classificateurs, sans doute parce qu’elle nécessitait un
jugement subjectif, plutôt qu’une instruction explicite. En outre, nous
observons moins de variation dans les groupes produits par les
participants dans les langues présentant un système de classification
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plus complexe. Ainsi, les systèmes plus informatifs ou plus rigides
semblent plus utiles pour la cátegorisation. Cette étude indique que
l’influence de la langue sur la cognition peut varier d’une langue à l’autre,
et que les systèmes de classification nominale n’utilisent pas tous un
compromis optimal entre simplicité et informativité. Ces nouveaux
résultats offrent une perspective originale sur l’origine et la nature de la
classification nominale.
KEYWORDS
Nominal Classification; Grammatical Gender; Possessive Classifiers;
Oceanic Languages; Categorisation; Card Sorting.
MOTS-CLÉS
Classification Nominale; Genre Grammatical; Classificateurs Possessifs;
Langues Océaniennes; Catégorisation; Tri de Cartes.
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INTRODUCTION
Linguistic communication is a fundamental aspect of human experience, and one which
shows great variation, given that there are over 7,000 different languages spoken
worldwide (EBERHARD; SIMONS; FENNIG, 2021). It has been widely debated whether the
language spoken influences speakers’ perception and cognition; the claim that it does is
known as the Sapir-Whorf hypothesis (CARROLL, 1956). Some researchers argue that
structural differences in language influence the way a native speaker thinks about the world
(e.g. BROWN; LENNEBERG, 1954; LUPYAN et al., 2020) whilst others claim that language
does not shape cognition (e.g. SCOVEL, 1991; SPEED et al., 2020). Slobin (1996) reinterprets
the Sapir-Whorf hypothesis as ‘thinking for speaking’, a special type of thought process
involved in speech production, and argues that the variation in obligatory grammatical
elements across languages enables speakers to focus on different aspects of experience.
Categorisation is a process central to both communication and thought, and therefore
provides an appropriate arena to explore this much debated relationship between language
and cognition. Hawkins (2004) suggests that systems of categorisation should be
informative to maximise communicative effectiveness, and simple to minimise cognitive
effort, yet there is much variation in the types and numbers of categories utilised across
different languages of the world. For example, a system with many different categories is
highly descriptive and so highly informative but the result of having many descriptive
categories is that this relative complexity leads to high cognitive load.
Research investigating the Sapir-Whorf hypothesis has focused on a number of
different domains (GUMPERZ; LEVINSON, 1996) and a significant amount of work has been
conducted on linguistic colour categories (BERLIN; KAY, 1969). For example, it has been
found that differences in colour naming systems across languages reflect a near optimal
trade-off between simplicity and informativeness (JAMESON; D’ANDRADE, 1997).
Specifically, in colour naming systems that only have two terms those terms occur at
maximally distant points within colour space, so as to be optimally descriptive of the space.
An example of this is the Dani language of Western New Guinea, which uses the terms
mola
for bright, warm colours and
mili
for dark cold colours. Systems with additional terms tend
to locate these referents at points that are located furthest from the existing referents
within colour space. Such investigations on colour naming systems have investigated British
English speakers (FRANKLIN et al., 2008; GILBERT et al., 2006) and made comparisons with
monolingual and bilingual populations in Europe (DELGADO, 2004; WINAWER et al., 2007),
East Asia (THAM et al., 2020), Africa (PILLING; DAVIES, 2004; ROBERSON et al., 2006) and
Oceania (ROBERSON; DAVIES; DAVIDOFF, 2000). The findings from such studies
investigating the influence of language on colour categories is conflicting. It can be
concluded that there are universal boundaries in colour naming that may govern
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prelinguistic colour cognition (e.g. CLIFFORD et al., 2009; MAULE; FRANKLIN, 2019), but also
that differences across languages in colour naming create variations in colour cognition
(KAY; REGIER, 2006), which appears to begin during colour term acquisition (FRANKLIN et
al., 2008). See Regier and Kay (2009) for a review. However, Bi (2017) argued that speakers’
conceptualisation may be altered by personal experience and culture-related knowledge,
and so the differences found in cognition may be due to culture, not language. Gibson et al.
(2017) highlight this, suggesting that American English speakers (with 11 colour terms) were
more efficient at naming and distinguishing colours than the Tsimane people of the Amazon,
who have only three colour terms. The results were attributed to industrialisation, and colour
terms being developed for artificial colours. On the other hand, the results could also be due
to a lack of task familiarity for the Tsimane speakers compared to the American English
speakers. Thus, it is important to consider the interplay between task familiarity, cultural,
environmental, and linguistic differences when making comparisons between speakers of
different languages.
Further research exploring the Sapir-Whorf hypothesis has focused on kinship
categories. Researchers have argued that languages differ in their categorisation of
kinship terms, but similarly to colour categories, the range of variation appears to be limited.
Kemp and Regier (2012) state that kin classification systems exhibit a near optimal trade-
off between simplicity and informativeness, to minimise cognitive load and maximise
efficient communication. However, these systems also show differences across cultures, as
they often relate to and are shaped by the cultural norms of marriage and living. Differences
in kinship categories across languages may also be shaped by communicative needs, and
as these communicative needs vary across cultures, the semantic systems should vary also
(KEMP; XU; REGIER, 2018). As semantic category systems are explained by the same
general principles across disparate domains such as colour and kinship categories, it is
arguable that categorisation has a domain-general basis. Therefore, it is important to
investigate linguistic relativity in other domains of categorisation, such as grammatical
gender and classifier systems.
It can be argued that aspects of grammar are a better means by which relativity can
be investigated compared to colour research, as grammar is not affected by sensory input.
Thus, language does not need to breach psychophysical barriers (SAMUEL; COLE; EACOTT,
2019). Vigliocco et al. (2005) argue that language may have less effect on discrete domains,
such as objects and sex, than on continuous domains such as colour, time, and space.
However, few studies have set out to investigate specific language systems across different
languages, to examine how speakers of those languages perform in classification tasks
(SERA et al., 2002). A broad variety of nominal classification systems exist across
languages, ranging over gender and classifier systems (see, for instance, SENFT, 2000).
Grammatical gender is extremely widespread across the world, present in some 44% of the
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languages in Corbett’s 256-language sample (2005). There is considerable variation; all
gender systems have 257 semantic core, most often including female versus male, but a
sizable minority have animate versus inanimate. The origin of such systems is naturally of
great interest, provoking debate as to whether the origin of gender lies in meaning (GRIMM,
1831) or form (BRUGMANN, 1889).
Until recently, typologists tended to oppose gender systems to classifier systems. This
was in part the result of historical accident, since the languages that were best known
represented only a part of the typological space. Now that “in-between” systems have been
documented, it is time to see these systems as part of a large typological space, in which
we can identify criteria which exist in a wide range of combinations (FEDDEN; CORBETT,
2017, 2018; CORBETT; FEDDEN; FINKEL, 2017; CORBETT; FEDDEN, 2018). In order to provide
an entry to the literature, however, it is useful to describe traditional classifier systems (as
in GRINEVALD, 2000). In the clearest instances, classifiers are separate words used to
classify nouns according to the shape, size and use of the referent. Similar to gender,
classifiers have underlying conceptual meanings, but also exhibit large variability in the
number of categories they contain, the size of these categories and in the consistency of
their use (SPEED et al., 2016).
Grammatical gender has been shown to alter object concepts for German and Spanish
speakers, as differences in similarity judgements between people and objects were at-
tributed to grammatical gender (BORODITSKY; SCHMIDT; PHILLIPS, 2003). However, other
researchers (BENDER; BELLER; KLAUER, 2011; LANDOR, 2014) found no evidence of a posi-
tive correlation between grammatical gender and the conceptualisation of objects, sug-
gesting that gender does not alter conceptual representations. In a similar vein, Speed et
al. (2016) argued that classifier systems reflect conceptual structure rather than affect it,
supporting the view that language may reflect thought rather than constrain it. Specifically,
speakers of Dutch (a language without classifiers) and speakers of Mandarin (a language
with classifiers) were asked to assess the similarity of a target object to four comparison
objects. One of the four comparison objects shared a classifier with the target object, while
the others did not. Despite the fact that Dutch does not have classifiers, both Dutch and
Mandarin speakers assessed the target object as more similar to the object with a shared
classifier compared to the other distractor objects. Furthermore, Dutch and Mandarin
speakers performed identically, suggesting that even speakers of a language without clas-
sifiers are sensitive to the object similarities that underlie classifier systems. This finding
provides support for the idea that classifier systems may be closely linked to how humans
conceptualise objects. In subsequent work Speed et al. (2020) did not find any difference
between speakers of classifier vs. non-classifier languages (Mandarin and Dutch respec-
tively) when looking at numeral classifiers specifically. This demonstrates that speakers of
a non-classifier language are also affected by the types of conceptual similarities that
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underpin classifier systems. Such systems may therefore reflect this conceptual structure,
rather than shaping it. This indicates that the influence of language is perhaps more re-
stricted than was previously suggested. Furthermore, Malt and Wolff (2010) found no evi-
dence that classifier categories are used as foundations for categorisation, compared to
thematic or taxonomic relations, when classifiers are not explicitly stated. Therefore, clas-
sifiers may not function to structure and influence cognition. However, previous research
separated stimuli according to language. For example, object names were given in Manda-
rin for Mandarin speakers and in English for English speakers (SPEED et al., 2016). Thus, the
differing behaviour of speakers across languages may be attributed to the grammar or lexis
of the speakers’ native language, rather than due to any underlying differences in cognition
(PINKER, 1997). Beller et al. (2015) aimed to disentangle cultural and linguistic effects on
cognition by asking participants to assign a male or female voice to a range of nouns from
different semantic categories. It was found that gender systems did have an impact on
voice assignment, but these grammatical effects were small in comparison with effects
brought about by culturally conveyed associations. This suggests that cultural factors are
a stronger influence than linguistic factors within and across semantic domains. Thus, the
evidence on the relationship between grammatical gender and cognition is very mixed to
date. This has been recently exemplified by Samuel et al. (2019) in a systematic review of 43
articles involving almost 6000 participants. The overall conclusion was that support for lin-
guistic relativity within the domain of grammatical gender was strongly task and context
dependent. Indeed, as Bender et al. (2016) summarise, it appears that cognitive effects of
gender are more likely to occur when stimuli are presented linguistically than when they are
presented as pictures when measures explicitly ask for gender-relevant assignments, and
also when languages have two genders (feminine and masculine) rather than three (femi-
nine, masculine, neuter class). However, Bender et al. (2016) did find gender congruency ef-
fects in German, a language with three genders, when taking into account different seman-
tic categories of nouns. Despite this, previous studies have utilised a range of different
methodologies and focused on different noun categories, making it difficult to draw con-
crete conclusions about the interplay between language and thought within this domain (for
an overview see CUBELLI, PAOLIERI, LOTTO & JOB, 2011). This highlights a need for further
experimental work exploring grammatical gender, including classifier systems, to enable
direct comparison across tasks within the same context. This will enable the mechanisms
underlying any relative effects of language on cognition to be more clearly revealed.
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1. THE CURRENT STUDY
Oceanic languages contrast inalienable and alienable possession using two different
possessive constructions direct and indirect. Possession here is understood broadly,
including ‘possession’ of body parts and kin, through to legal possession of items, and
temporary association. Inalienable possession is typically encoded by the direct
construction, in which the possessor indexing is marked directly on the possessed noun (1a).
Alienable possession, on the other hand, uses the indirect possession construction; it is
typically marked using one of a set of possessive classifiers where the possessor indexing
attaches to the possessive classifier rather than the possessed noun (1b), hence the term
‘indirect construction’.
Iaai, New Caledonia (DOTTE, 2017, p. 341)
1a.
hinyö-k
b.
haalee-k kuli
mother-1SG CL.PET-1SG dog
‘my mother’ ‘my dog (pet)’
inalienable possession alienable possession
direct construction indirect construction
Our study on optimal categorisation focuses solely on the indirect construction with
possessive classifiers which, in the Oceanic languages, range from two (e.g. Merei, Vanuatu)
to more than thirty classifiers found in the Micronesian sub-group within Oceanic. The
inventory size of the possessive classifiers in our sample languages ranges from two to 23
(Table 1). An important point about these Oceanic possessive classifiers is that they have
been termed ‘relational’ (LICHTENBERK, 1983). Relational indicates that a possessed noun
can co-occur with different classifiers depending on the relation of the possessor to the
referent of the noun, that is, how the possessor intends to use the possessed noun referent,
as in (2).
Lewo, Vanuatu (EARLY, 1994, p. 216)
2a.
ma-na wi
b.
sa-na wi
CL.DRINK-3SG water CL.GENERAL-3SG water
‘her water (for drinking)’ ‘her water (for washing)’
The classifier systems in the Oceanic languages map the principles of simplicity and
informativeness onto the speaker-hearer distinction. Simple systems with fewer classifiers
favour the speaker, whereas informative systems with more classifiers favour the hearer.
There is an added principle of the degree of semantic transparency held by the members
of each classifier. We therefore see a trade-off in the Oceanic classifier systems between
the principles of simplicity, informativeness and semantic transparency.
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Figure 1.
Map showing the location of the six languages included in the current study.
We have chosen carefully a sample of six Oceanic languages spoken in Vanuatu and
New Caledonia (Figure 1). Our choice is based on several criteria. First, the sample
languages are closely related so that we can use the differences between the languages as
a proxy for development through time. Second, the sample languages vary in their inventory
size of classifiers so that we can investigate how optimal a simple system with two
classifiers is compared to an informative system with 23 classifiers. Third, the classifiers
themselves vary in their semantic transparency in terms of the membership of nouns. For
example, the drink classifier in Iaai and Nêlêmwa only allows nouns whose referents are
intended to be drunk, while the corresponding classifier in Lewo, Vatlongos and North
Ambrym not only includes nouns whose referents are able to be drunk, such as water or
green coconut, but also nouns whose referents refer to houses and mats. Finally, the sample
languages represent different stages in the grammaticalisation from noun to classifier to
marker of grammatical gender. Due to the varying inventory size of the classifier systems
in the sample languages not all classifiers are shared across all languages. Even the three
common classifiers - general, food and drink - reconstructed for Proto Oceanic are only
shared across four languages in our sample, as shown in Table 1.
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Language
Number of Classifiers
General
Food
Drink
Merei
2
ü
1
Lewo
3
ü
ü
ü
Vatlongos
4
ü
ü
ü
North Ambrym
5
ü
ü
ü
Nêlêmwa
20
ü
2
3
ü
Iaai
23
ü
ü
ü
Table 1.
The size of classifier inventories and the common classifier categories.
The first stage of grammaticalisation is the emergence of classifiers from other parts
of speech. There is evidence of this first stage in Nêlêmwa where some directly possessed
nouns can be used as incipient classifiers, as in (3). Bril (2002) reports that the directly
possessed noun
waja-ny
‘my boat’, given in (3a) can be used as an incipient classifier for
boats but only if the possessed noun is modified, as in (3b) with the modifier
hnap
‘sail’. These
appositional phrases are precursors to classifiers.
Nêlêmwa, New Caledonia (BRIL, 2002, pp. 259, 369)
3a.
iva waja-ny
b.
waja-ny
wany
hnap
where boat-1SG boat-1SG boat sail
‘where is my boat? ‘my sail boat’
However, work with speakers of Nêlêmwa revealed that there is considerable
variation in how speakers use these incipient classifiers. Some speakers are able to use
the
waja-
as a classifier, without the possessed noun being modified. Other speakers use
waja-
as a classifier with extended semantics, which includes all transport items such as
cars and bicycles.
Classifiers themselves can become more rigid in terms of their noun-classifier
collocation and thus function more similarly to a grammatical gender system in terms of
noun assignment. For example, the noun
we
‘water’ in North Ambrym can only ever occur
with the drink classifier. Compare (4) with the comparable Lewo example (2).
North Ambrym, Vanuatu (FRANJIEH, 2016, p. 95)
4a.
ma
-n we
b. *
mwena
-n we
CL.DRINK-
3SG water
CL.GENERAL-
3SG water
‘his/her water (for any purpose)’ [not possible]
1
Merei has collapsed the two originally distinct edible and drinkable classifiers into one ‘consumable’ classifier.
2
Nêlêmwa does not have a possessive classifier that is used for general possessions (i.e. possessions not included
in the other classifiers’ semantic domains). Instead it employs either direct possessive constructions or a
prepositional construction. We include the prepositional construction in our count of the inventory size of
possessive classifiers as it functions semantically in a way similar to the general classifiers found in other
Oceanic languages.
3
Nêlêmwa does not have a dedicated classifier covering all edible possessions. Instead it has developed independent
possessive classifiers for fruit and vegetables, unripe fruit, starchy food, meat, chewable food, and sugarcane.
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Turning now to a particularly relevant investigation, Franjieh (2016, 2018)
investigated the nominal classification system of possessive classifiers in North
Ambrym, an Oceanic language spoken in Vanuatu (see Figure 1). Participants were
asked to list all nouns occurring with each classifier, in a free list task. Participants also
completed a noun categorisation task, and were presented with a set of nouns and
asked to provide the associated possessive classifier. Finally, participants were asked
to describe video vignettes depicting different contextual interactions. Franjieh (2016)
argued that the possessive classifiers in North Ambrym are markers of gender, due to
the more rigid collocation between a noun and classifier, compared to more typical
relational classifier systems found in other Oceanic languages. The study found that for
North Ambrym, nouns that are more central to a classifier’s semantic categories and
are typical possessions, only occur with one classifier, whereas nouns that are less
typical possessions and less central to the classifier’s semantic categories can be
assigned to different classifiers (FRANJIEH, 2018).
We aim to explore the findings of Franjieh (2016, 2018) further, through investigation of
the semantic domains of the possessive classifier systems in six related Oceanic languages,
in order to shed light on the cognitive functions and efficiency of varying classifier systems.
Such an investigation has not been carried out before in a set of languages showing such
significant linguistic variation yet with such high cultural similarity and close geographic
proximity (Figure 1).
Having selected the languages for our study, we investigated the semantic domains of
the possessive classifiers in these languages using a card sorting experiment, which
enables us to investigate how participants categorise objects and what governs perceived
similarity. Participants sorted images in a free card sort task, where they were asked to
group the images in any way they chose, followed by a structured card sort task, where they
were asked to group images based on the classifiers in their language. All participants were
presented with the same stimuli, to enable comparison of sorting and the relative influence
of classifiers across the different languages (MALT; WOLFF, 2010). Comparison between
the groupings in the free sort task and the structured sort task provides an insight into the
relative influence of linguistic and cultural factors on participants’ categorisation. Data
were analysed using a mixed methods approach.
It was predicted that classifiers would provide a structure for cognition for the
languages tested in this study. A series of additional specific hypotheses were made to
enable investigation of this overarching research question:
Hypothesis 1:
Speakers of languages with larger classifier inventories (Iaai and Nêlêmwa)
will produce a larger number of categories in the free sort and structured sort tasks,
resulting in more categories across the two tasks, compared to speakers of languages with
smaller classifier inventories (Merei, Lewo, Vatlongos, and North Ambrym);
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Hypothesis 2:
Similar numbers of categories will be made in the free sort and the structured
sort tasks for all languages;
Hypothesis 3:
Languages with larger classifier inventories (Iaai and Nêlêmwa) will exhibit
greater variation in number of categories across participants in comparison to languages
with smaller classifier inventories (Merei, Lewo, Vatlongos, and North Ambrym);
Hypothesis 4:
North Ambrym, with a more rigid gender-like system of noun-classifier
assignment, will exhibit less variation in comparison to languages with more typical
classifier-like systems whose systems allow variable noun assignment (Merei, Lewo,
Vatlongos, Nêlêmwa, and Iaai).
2. METHOD
Participants.
The participants were 119 adults from villages in Vanuatu and New Caledonia.
Data from one further participant was excluded since they collaborated on their answers.
A G*Power analysis was conducted for a large effect size (
d
= .40) and an alpha of .05.
Results showed that this number of participants was an appropriate sample size to achieve
.80 power (FAUL et al., 2009). Of our sample, 54.5% were male, and 45.5% were female. The
age range was 18-77 years (
M
= 43.25,
SD
= 15.29). Additional demographic information for
each language is presented in Table 2. The education level of participants ranged from 0-
19 years of education, and the average length of education for participants was 8.5 years.
Participants were recruited through convenience and snowball sampling methods via initial
contacts in each of the language communities. In compensation for their time, participants
were offered a small monetary amount or gift in accordance with cultural norms of each
country. Prior to conducting this study, ethical approval was obtained from the University of
Surrey ethics committee.
Language
No. participants
Gender
Age
Years of education
Male
Female
M
SD
M
Merei
21
15
6
32.57
11.67
6.30
Lewo
23
13
10
45.78
12.11
7.90
Vatlongos
24
9
15
36.71
13.35
6.30
North Ambrym
23
12
11
44.04
12.60
7.50
Nêlêmwa
12
7
5
53.58
19.13
11.00
Iaai
16
9
7
54.25
13.10
12.06
Table 2.
Demographic information for each language, including overall number of participants, number of female and
male participants, mean age and mean education level of participants.
Experimental Setup.
The card sorting experiment was conducted alongside a free listing
experiment and a video vignette experiment, which will be reported elsewhere. The order of
these three experiments and the stimuli used within each was randomised across
participants. However, the order of the three tasks within the card sorting experiment was
fixed so that all participants did a naming task, followed by a free sort task and then a
structured sort task. Testing took place across 59 days between July and October 2019;
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during this time between one and three participants were tested each day. Participants were
typically tested seated at a table inside or on the veranda of their house, a shelter or church.
Participants usually completed the card sort task on mats on the floor, or on a large table.
Before the recording of any data, participants were provided with information on the study,
and gave informed consent. For the Vanuatu languages, the information sheet and consent
form were read aloud to participants in Bislama (the lingua franca of Vanuatu) and oral
consent was provided. Informed consent was recorded with an audio recorder, since some
participants are not literate. For the New Caledonia participants, the information sheet and
consent form were written in French (the lingua franca of New Caledonia), and presented to
them to read and sign. These details were also summarised orally by the experimenter. The
protocols were translated into Bislama and French and checked by one native speaker for
Bislama and one for French. These were back-translated by English native speakers, one
fluent in Bislama, and one fluent in French, to validate the translation.
Materials and Design.
Participants were presented with 60 cards detailing a
standardised set of images. These images depicted entities and different interactional uses
of an entity. For example, a live pig and a pig being roasted (see Figure 2 for all images). The
choice of images aimed to represent the total inventory of semantic categories covered by
the classifiers in all six languages, apart from Iaai and Nêlêmwa. In Iaai 20 out of 23 classifiers
were covered, with ‘idea/thought’, ‘noise/sound’ and ‘mana/strength’ not being covered, as
these are abstract entities that were not possible to depict with the type of images used in
this study. In Nêlêmwa there are four classifiers that are associated with different ways of
catching and using fish ‘catch-speargun’, catch-line’, ‘catch-net’ and ‘catch-sell’. The
pictures of fish in our set of images were not targeted to these different specific classifiers,
though they may occur with at least one. Thus 17 of Nêlêmwa’s 20 classifiers were targeted.
Images were sized 10cm x 10cm with a resolution of 1181 X 1181 pixels.
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1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
16 17 18 19 20
21 22 23 24 25
26 27 28 29 30
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Figure 2.
All images used in the pile sort experiment. The picture IDs correspond to the IDs in Table 3.
Procedure
. Participants first completed a naming task. They were presented with each
image in turn and asked to name the entity to ensure understanding. The images were then
laid out in front of the participants, who were asked to free sort the cards into groups. The
verbatim instructions for this task are given below:
31 32 33 34 35
36 37 38 39 40
41 42 43 44 45
46 47 48 49 50
51 52 53 54 55
56 57 58 59 60
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There are sixty pictures in front of you. I would like you to sort these pictures into groups
such that pictures in the same group are related to each other. There are many different
ways you can group the pictures together. There is no right or wrong way to do it. You can
group the pictures together any way you like according to your logic, your way of viewing
things. You can then divide the pictures into different groups. You can make as many
groups as you want to. Maybe there will be groups with lots of pictures, others with just
one or two pictures, that's not a problem. Try not to spend too much time on it, It should
be spontaneous. Then you will be asked to say a word or a little sentence in the language
to explain each group.
After the task, participants were asked to give a summary of each pile, explaining why
these cards were grouped together. The verbatim instructions are below:
Now, please tell me one word in your language that describes each of the groups that you
have made. If you can’t think of a single word, you can tell me several words or a sentence.
In order to give a title to the group. You can start with any of the groups you have made.
The cards were then shuffled again and laid out in front of the participants who were
then asked to sort the cards into groups relating to the classifiers of their mother-tongue
language, in a structured card sort. This ensured that the different classifiers were used.
Participants were asked to sort images into groups according to which word they would use
to mean ‘mine’, similarly to the colour naming task in Grandison, Davies, and Sowden (2014).
See below for the verbatim instructions for the structured sort task:
In your language <INSERT LANGUAGE NAME> there are many different ways of saying ‘this
belongs to me’
depending on the function of the object. I want you to put the different
pictures into groups so that the pictures in each group all go with a particular way of saying
‘this belongs to me’. Each group of pictures that you make will go with a different way of
saying ‘this belongs to me’. It's as if for each image, you have to say to yourself 'this is mine
and the name of the object' and you put it with the other images for which you would say
the same for the word which means ‘this is mine’.
3. RESULTS
Analysis overview and data screening
. A mixed methods approach was used to analyse the
data. Analysis of Variance (ANOVA) was conducted to investigate the numbers of categories
made across languages for each of the tasks (free sort and structured sort). Pairwise
comparisons were used to further investigate significant main effects and interactions.
Statistical analyses were conducted using R (R CORE TEAM, 2020). A qualitative approach
was employed to explore the labels assigned to the various piles produced by participants
within the free sort. Both the labels and the cards sorted into the corresponding piles were
analysed through a qualitative lens, taking cues from Thematic Analysis (BRAUN; CLARKE,
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2006) in order to parse out any themes and/or narrative structure present in the labelling.
Thematic Analysis involves becoming familiar with the similarities and differences within
the data, generating ‘codes’, and defining the framework of overarching themes and sub-
themes (BRAUN; CLARKE, 2020). An advantage of Thematic Analysis is that it is a flexible
approach to organising data, which allows for nuanced thinking and consideration of
unforeseen insights (TERRY et al., 2017). However, the loose structure of Thematic Analysis
means that the data could become overwhelming, potentially leading to superficial
observations and a lack of coherence (NOWELL et al., 2017). As such, to maintain the depth,
richness, and quality of the observations, it was necessary to keep a rigorous track of the
emerging themes from the initial analyses until the final stages of refinement (MCGRATH;
PALMGREN; LILJEDAHL, 2019).
Naming task
. Percentage accuracies were calculated for all languages for the images
used in the card sort experiment (see Table 3). English translations of terms were assessed,
and conceptually similar terms collapsed. For example, ‘tea’ and ‘liquid tea’ were collapsed
as conceptually the same, as well as ‘yam’, ‘yam for a ceremony’ and ‘one yam’. However,
‘coconut’ and ‘fruit’ were not collapsed, nor were ‘clothes’ and ‘woman’, for example. Images
with 75% accuracy in one language were excluded from analysis across all languages,
which led to our omitting five images: tea, necklace, dress worn, hat, sun. The figure of 75%
was data driven and arose as a logical cut off to maximise the number of pictures included
in the analysis whilst implementing stringent conceptual consistency.
ID
Picture Name
Language
Merei
Lewo
Vatlongos
North
Ambrym
Nêlêmwa
Iaai
1
ball
100%
100%
100%
100%
100%
100%
2
banyan tree
100%
100%
100%
100%
77%
100%
3
basket not worn
100%
100%
100%
100%
100%
100%
4
basket worn
100%
100%
100%
100%
100%
100%
5
breadfruit on tree
100%
100%
100%
100%
100%
95%
6
breadfruit roasting
95%
96%
96%
96%
100%
95%
7
burden/heavy load
76%
87%
96%
100%
100%
100%
8
canoe
91%
100%
100%
100%
100%
88%
9
chair
100%
100%
100%
100%
100%
100%
10
chewing gum
81%
87%
100%
91%
100%
100%
11
coconut - germinating
100%
100%
100%
100%
100%
100%
12
coconut (dry) off tree
100%
100%
100%
100%
100%
100%
13
coconut (dry) on tree
100%
100%
100%
100%
100%
100%
14
coconut (green) off tree
100%
100%
100%
100%
100%
100%
15
coconut (green) on tree
100%
100%
96%
100%
100%
100%
16
coconut flesh
95%
100%
100%
91%
100%
100%
17
coconut palm
100%
100%
96%
100%
100%
100%
18
coconut palms
100%
100%
96%
100%
100%
100%
19
coconut plantation
95%
100%
96%
100%
100%
100%
20
dog
100%
100%
100%
100%
100%
100%
21
dress hanging
100%
100%
100%
100%
100%
100%
22
dress worn
48%
44%
50%
52%
77%
88%
23
fire
100%
100%
100%
100%
100%
100%
24
fish caught
100%
100%
100%
100%
100%
100%
25
fish pl caught
100%
100%
100%
100%
77%
100%
26
fish roasted
100%
100%
100%
100%
100%
100%
27
fish swimming
100%
100%
100%
100%
100%
100%
28
flower
100%
100%
100%
96%
100%
100%
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29
hat
62%
65%
71%
48%
77%
94%
30
house
100%
100%
100%
100%
100%
100%
31
knife
100%
100%
100%
100%
100%
100%
32
mat (flat)
100%
100%
100%
100%
100%
100%
33
mat (rolled)
100%
100%
96%
100%
100%
100%
34
matches
76%
97%
96%
87%
100%
100%
35
moon
91%
100%
100%
91%
100%
100%
36
necklace
67%
57%
71%
48%
100%
94%
37
oil drum
100%
96%
100%
91%
100%
100%
38
oil drums
100%
100%
96%
91%
100%
100%
39
phone
95%
91%
100%
96%
100%
100%
40
pig alive
100%
100%
100%
100%
100%
100%
41
pig roasted
100%
96%
100%
100%
77%
100%
42
rat
100%
100%
100%
100%
100%
100%
43
rat swarm
95%
100%
100%
96%
100%
100%
44
rifle
95%
91%
96%
96%
100%
100%
45
road
95%
96%
83%
100%
100%
100%
46
road (jungle path)
81%
96%
100%
91%
100%
100%
47
spade
100%
100%
100%
100%
100%
100%
48
sugarcane cut
100%
100%
100%
83%
100%
100%
49
sugarcane growing
100%
96%
96%
91%
100%
100%
50
sun
71%
83%
67%
61%
77%
94%
51
tea
5%
13%
25%
22%
77%
77%
52
tomato
100%
100%
100%
96%
100%
100%
53
tomatoes
100%
100%
100%
100%
77%
100%
54
tomatoes on plant
100%
100%
96%
100%
100%
100%
55
truck
100%
100%
100%
100%
100%
100%
56
wound/sore
95%
100%
96%
96%
100%
100%
57
yam dug up
91%
100%
100%
96%
100%
100%
58
yam tubers
95%
100%
96%
96%
100%
94%
59
yam vine
100%
100%
96%
96%
100%
94%
60
yams cooked
81%
78%
96%
96%
77%
88%
Table 3.
Percentage accuracies for all 60 images presented in the naming task within the card sort experiment, across
all languages. Omitted items are italicised.
Free sort.
Overall, descriptive statistics showed that the largest number of categories for
the free sort were made by Merei participants and the fewest categories were made by Iaai
participants (see Table 4). ANOVAs were calculated using the
rstatix
package (KASSAMBARA,
2020). There was a significant difference in the number of categories made for the free sort
task between languages,
F
(5, 113) = 2.91,
p
= .017, ηp2 = .11. Pairwise comparisons comparing
individual languages indicated that Iaai made significantly fewer categories compared to
Merei (
p
= .003), Lewo (
p
= .003), and Vatlongos (
p
= .004), with all other comparisons being
non-significant (smallest
p
= .052). This showed that fewer categories were produced by
speakers of languages with larger classifier inventories (Iaai) compared to speakers with
smaller classifier inventories (Merei and Lewo), contradicting Hypothesis 1.
Free sort
Structured sort
Language
M
SD
M
SD
Merei (2)
21.48
11.31
14.86
14.02
Lewo (3)
21.30
9.28
5.22
5.02
Vatlongos (4)
21.13
10.23
10.67
10.04
North Ambrym (5)
18.09
7.86
7.13
5.79
Nêlêmwa (17)
15.33
9.76
12.08
5.87
Iaai (19)
12.00
8.16
12.13
4.01
Table 4.
Means and standard deviations for the numbers of categories made for the free sort and structured sort
tasks across all languages. Number of classifiers included in analysis after exclusion of pictures with 75% accuracy
in naming task shown in brackets.
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Structured sort.
Descriptive statistics for the structured sort task showed that Merei
participants made the greatest number of categories, followed by Iaai and Nêlêmwa
participants. Lewo participants made the fewest (see Figure 3). This partially supports
Hypothesis 1. Further, through comparison of the common classifiers across Lewo,
Vatlongos, North Ambrym and Iaai, it was found that more images were placed into the
general classifier (M = 24.67, SD = 11.18) compared to the food (M = 17.67, SD = 9.45), and the
drink classifiers (M = 4.66, SD = 3.03).
Figure 3.
Bar chart showing the mean number of categories made by each language for the structured sort task,
compared to the number of classifiers of the language. Error bars represent the standard deviation. Note -
significant differences between number of categories and classifier inventories are indicated by *** < .001, ** < .01,
* < .05, ns = not significant.
Overall, there was a significant difference in the number of categories made across
languages in the structured sort task,
F
(5, 113) = 3.73,
p
= .004, ηp2 = 14.
Pairwise comparisons
indicated that significantly fewer categories were made in Lewo compared to Merei (
p
<
.001), Vatlongos (
p
= .03), Nêlêmwa (
p
= .025) and Iaai (
p
= .014). Furthermore, significantly
fewer categories were made in North Ambrym compared to Merei (
p
= .003). Additional
t-tests revealed significant differences between the number of categories made and the
number of classifiers tested from each language for all languages except North Ambrym
(see Table 5 for inferential statistics).
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Language
Number of classifiers
t
p
d
4
Merei
2
4.20
< .001
0.92
Lewo
3
2.12
.046
0.44
Vatlongos
4
3.25
.003
0.66
North Ambrym
5
1.76
.092
0.37
Nêlêmwa
17
-2.90
.014
0.84
Iaai
19
-6.85
< .001
1.71
Table 5.
Inferential statistics from the t-tests comparing the number of classifiers with the number of categories
produced in the structured sort for each language.
Additionally, a 3 (classifier: drink, food, general) x 4 (language: Lewo, Vatlongos, North
Ambrym, Iaai) mixed ANOVA investigated whether the number of images placed into the
common classifier categories differed across the relevant languages (Table 1). A
Greenhouse-Geisser sphericity correction was applied due to the assumption of sphericity
not being met as Mauchly’s test was significant, W = 0.373,
p
< .05.
There was a significant main effect of classifier,
F
(1.23, 100.79) = 146.10,
p
< .001, ηp2 = .64.
Pairwise comparisons found that significantly more images were placed into the general
classifier compared to the food (
p
< .001) and drink classifiers (
p
< .001). Also, significantly
more images were placed into the food classifier compared to the drink classifier (
p
< .001).
There was a significant main effect of language on the number of items placed into the
common classifiers,
F
(3, 82) = 22.52,
p
< .001, ηp2 = .45. Pairwise comparisons showed that Iaai
placed significantly fewer images into the common classifiers compared to Lewo (
p
= .01) and
North Ambrym (
p
= .016). All other comparisons were non-significant (smallest
p
= .09).
There was a significant interaction between classifier and language, indicating that the
relative numbers of items placed into the three common classifiers differed across
languages:
F
(3.69, 100.79) = 20.12,
p
< .001, ηp2 = .42. Figure 4 shows that North Ambrym
participants placed the most images into the food classifier and all other languages placed
the most images into the general classifier. All languages placed the fewest images into the
drink classifier. Finally, when comparing the number of images placed into the general
classifier across languages, pairwise comparisons showed that Lewo placed significantly
more images into the general classifier compared to North Ambrym (
p
= .005). Furthermore,
Vatlongos placed more images into the general classifier compared to North Ambrym (
p
<
.001) and Iaai (p = .001). All other comparisons were non-significant (smallest
p =
.059).
4
Cohen’s
d
calculated using the
lsr
package (NAVARRO, 2015).
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Figure 4.
The interaction effect for the mixed ANOVA conducted for the structured sort. Mean number of images
placed into each classifier for the relevant languages.
Comparison of free sort and structured sort
Overall, a greater number of categories
were made in the free sort (
M
= 18.82,
SD
= 9.91) compared to the structured sort (
M
= 10.01,
SD
= 8.98). Table 6 shows that all languages apart from Iaai made more categories in the
free sort compared to the structured sort. This difference was significant for Lewo,
Vatlongos, and North Ambrym but was non-significant for Merei, Nêlêmwa, and Iaai. This
only partially supports Hypothesis 2, which states that similar numbers of categories will be
made across both tasks.
Language
t-test
t
p
d
Merei
-1.68
.100
0.52
Lewo
-7.31
< .001
2.16
Vatlongos
-3.58
< .001
1.03
North Ambrym
-5.38
< .001
1.59
Nêlêmwa
-0.99
.336
0.40
Iaai
0.05
.957
0.02
Table 6.
Inferential statistics from the t tests for the comparison of the number of categories made in the free sort
task and the structured sort task.
Note
-
significant results are shown in bold.
Furthermore, a 2 (task: free and structured sort) x 6 (language: Merei, Lewo, Vatlongos,
North Ambrym, Nêlêmwa, Iaai) mixed ANOVA was conducted on the number of categories
made. There was a significant main effect of task on the number of categories made,
F
(1,
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113) = 60.97,
p
< .001, ηp2 = .35, but there was no significant main effect of language on the
number of categories made,
F
(5, 113) = 2.01,
p
= .075, ηp2 = .08. A significant interaction
between task and language was revealed,
F
(5, 113) = 5.52,
p
< .001, ηp2 = .20. Figure 5 shows
that all languages made fewer categories in the structured sort apart from Iaai, who made
a similar number of categories in both tasks.
Figure 5.
Graph showing the interaction effect for all languages for the mixed ANOVA, comparing the mean number
of categories made across the free sort and structured sort.
Thematic analysis of category labels in the free sort
. Five themes emerged from the
qualitative exploration of the labels assigned to the piles produced by participants within the
free sort. These five themes are presented below in order of their prevalence and significance.
The influence of classifiers.
One facet of the responses that emerged is that
participants did not solely create their piles explicitly in accordance with the classifiers of
their language. The majority of participants did not associate their piles with a classifier
and many simply labelled the piles literally, with the label stating the items contained
within. For example, one speaker of Merei constructed a pile containing images 56 and
57 (each consisting of oil drums) and labelled it simply as ‘drum’. This manner of labelling
piles literally in referring to the items contained within was the most frequent method of
labelling during the free sort. However, some participants did create groups based on
classifiers, particularly speakers of Vatlongos. For example, one participant created four
piles, each corresponding to a classifier:
nganak be sam xil
‘everything is yours (land
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classifier); DEM COP CL.LAND.2SG,
nganak be nam
‘this is yours (general classifier); DEM COP
CL.GENERAL.2SG,
am
‘yours (food classifier); CL.FOOD.2SG, and
mam
‘yours (drink classifier);
CL.DRINK.2SG. A different speaker of Vatlongos created six piles, each labelled with an
associated classifier. Despite this, even the participants who exhibited more influence of
classifiers on their groupings often included at least some labels that were literal
descriptions of a pile’s contents.
Item usage/utility
. While the previous method of labelling was the most common,
another frequently used method of classification described the piles in respect to the utility
of the items contained within. For example, one speaker of Merei created several piles that
follow the format of: ‘things that x’. Their pile entitled ‘things for work’ included the images
of the rifle, the machete, the spade, and truck, while their pile ‘things for playing’ included
the football and chewing gum. Similarly, one speaker of North Ambrym created a pile
entitled ‘for playing’, which included the football. However, the same participant also
created a pile entitled ‘for work’, which was quite dissimilar to that of the Merei speaker
outlined above. This pile contained items such as the road and the oil drums. Indeed,
groupings based around work were common. One speaker of Vatlongos included the
machete and the spade in a ‘work’ pile, while a Nêlêmwa speaker included many items
within a similarly titled pile including the oil drums, machete, spade, and truck. Other
manifestations of this ‘things that x’ label include ‘for cooking’, which was given by a speaker
of North Ambrym, and ‘for eating’ given by a speaker of Lewo. All of these piles share the
theme of labelling according to the usage or utility of the items contained within.
Sourcing of items
. Another theme within the labels of piles was labelling according to
where items may be sourced from. For instance, one speaker of Lewo included the labels
‘something from the store’, ‘something from the garden’, and ‘something from the sea’. The
first of these piles included items such as the truck and the chewing gum, items that would
need to be purchased. Similarly, one speaker of Merei gave the label ‘all things from the
store’, and also included the images of the oil drums and the plastic chair. In contrast,
‘something from the garden’ consisted primarily of food that would be grown, such as yams
and tomatoes. ‘Something from the sea’ included the images of fish. Similarly, one Iaai
speaker included the label ‘the fruits from the earth’, which contained many of the items
outlined above in relation to the ‘something from the garden’ pile, but also several of the
images relating to coconuts. This categorisation based on the manner in which something
is acquired was frequent and consistent across languages.
Local or abroad
. One particular distinction that several participants made was between
things that could be found locally and things that needed to be sourced abroad. For
example, one speaker of Nêlêmwa, labelled a pile containing the football, the phone, the
chewing gum and the truck as being ‘objects with no name in Nêlêmwa’. Another Nêlêmwa
speaker included these items and more (the plastic chair, the rifle and the matches) as being
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‘things that have been introduced/imported’. This theme was also present in the other
languages. One Iaai participant used this as their only basis of categorisation creating two
piles that were labelled ‘things that exist in Iaai’ and ‘foreign stuff’, respectively. This
distinction between ‘indigenous’ and ‘foreign’ items was often conceptualised by referring
to foreign items with some variation of the label ‘white people’. This was demonstrated by
three participants from North Ambrym, two Lewo speakers, and a speaker of Merei, who all
created labels relating to the possessions of ‘white people’ or objects that ‘white people’
make. These labels include ‘things of white people’ produced by one Lewo speaker (for the
phone, the truck, the plastic chair, and the oil drums), and ‘metal of the white man’, a label
given by a North Ambrym speaker to a pile including the phone, the rifle, the machete, and
the truck. Other items that were labelled within this theme were matches, tea, and tomato.
Natural/unnatural and the manifestation of God
. Two related concepts that arose
within the labels were the distinction between the ‘natural’ and ‘unnatural’ things, and the
manifestation of God. These concepts were often related and so combine to form one
theme. As an example, one Iaai speaker uses the label: ‘the natural resources’ for a pile
including all of the biological items such as the fish, coconuts, tomatoes, pigs, etc. This
distinction between natural and unnatural is often made through the lens of religion, with
one Merei speaker exemplifying this by including two piles entitled ‘God makes life (things
with life)’ and ‘without life’. The former grouping contained primarily biological objects, such
as dog, rat, coconut, fish, and yams, while the latter group contained inanimate objects such
as the mats and football. This tendency was also clear from a Iaai speaker who grouped
many of the images depicting manmade objects under the labels ‘things made by men’ and
‘the things that men built (artefacts)’. This speaker distinguishes these piles from ‘the
animals of God’ containing all the animals, and the things of God’, containing objects such
as coconut palms and the sun and the moon. The sun and the moon, while often labelled
literally, were also connected to the divine by participants. Speakers across Iaai, Merei and
Lewo produced labels such as ‘the creation of the sky’, ‘the things of God’, ‘power of God’
and ‘God creates’ in relation to the sun and the moon.
Construction of a narrative
. A frequent occurrence across all six languages was
structuring piles around the telling of a story. In this instance, labels were used to tie the
objects within the pile together through narrative. Sometimes this was done in a whimsical
manner, as in the example ‘the witch is going to fly’ where one Iaai speaker linked the cards
representing the dresses, the basket, the mats, and the moon. Another, simpler example of
such a story was provided by a speaker of Merei, who placed the necklace and the chewing
gum together under the label ‘sister eats chewing gum’. Stories explaining practical,
potentially routine actions were also found. For example, a Lewo participant produced the
label ‘go shoot pig’ in relation to the rifle, the live pig, the cooked pig, the dog, the machete,
and the jungle road. Another example of this more straightforward narrative was the label
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‘person plays with his toys (man puts his hat on, plays football, wears chain, plays on mobile
phone)’ given by a North Ambrym speaker in relation to a pile containing the football,
telephone, basket, necklace and cap.
4. DISCUSSION
The current study aimed to investigate the semantic domains of classifier systems in six
related languages, to reveal their impact on cognition and the relative cognitive efficiency
of each system. The findings will be discussed in relation to our hypotheses and our
overarching aim to investigate whether classifiers provide a structure for cognition within
the languages of focus.
Hypothesis 1 predicted that speakers of languages with larger classifier inventories
(Iaai and Nêlêmwa) would produce a larger number of categories in the free sort and
structured sort tasks, compared to speakers of languages with smaller classifier
inventories (Merei, Lewo, Vatlongos, and North Ambrym). This hypothesis was only
partially supported. Indeed, speakers of Iaai and Nêlêmwa (languages with larger
classifier inventories) did produce more categories than speakers of Lewo, North Ambrym
and Vatlongos (languages with smaller classifier inventories), supporting Hypothesis 1.
However, this was only the case for the structured sort task and not the free sort task (c.f.
Table 4). Additionally, speakers of Merei with only two classifiers produced the largest
number of categories in the structured sort, directly contradicting Hypothesis 1. Thus, for
the structured sort, categorisation may be in part structured by classifiers. As the
classifier inventory increased, the mean number of piles made increased (with the
exception of Vatlongos and Merei). This is consistent with findings from other languages
such as Mandarin that employ rather different classifier systems from the Oceanic type
(JIANG, 2017). The pattern found in Merei may reflect the system’s relative simplicity and
lack of informativeness. As such a system is limited in the inventory size of classifiers, it
may be less likely to influence speakers in a task of this nature. The more classifiers a
system has, the more semantically transparent a system is. Thus systems with smaller
classifier distinctions have potentially more opaque systems than a larger system and
may be less likely to effect cognition.
Moreover, the prediction that similar numbers of categories would be made in the free
sort and the structured sort tasks for all languages (Hypothesis 2) was only partially
supported. Although more categories were made in the free sort compared to the
structured sort for all languages, this difference was only significant for Lewo, Vatlongos
and North Ambrym (c.f Table 6). This pattern suggests that classifiers are not the default
system of categorisation in the context of the free sort at least. As the free sort task required
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participants to sort images into groups with deliberately no guidance on how they might do
this, participants may have categorised the items according to semantic information,
perceptual properties, or taxonomic or thematic relations, in addition to classifier
membership. Such methods of categorisation were evidenced in Mandarin speakers, who
did not use their rather different classifier categories as the basis for categorisation
(SAALBACH; IMAI, 2007). Additionally, the significant difference in the number of categories
made across tasks for Lewo, Vatlongos, and North Ambrym may inform us about the nature
of these types of systems. It is possible that these more moderate systems may be less
optimal for categorisation, in comparison to Iaai, Nêlêmwa, and Merei at the extremes of
classifier inventory size. In combination, these findings suggest that the linguistic relativity
debate does not have a simple or concrete answer, and that the relative influence of
language on cognition appears to vary across different classifier systems.
With regard to Hypothesis 3, it was found that Iaai with the largest classifier inventory
displayed smaller variation on both the free sort and the structured sort, compared to Merei
and Vatlongos with their smaller inventories, contradicting this hypothesis. This was shown
through relatively smaller standard deviation scores for the mean number of categories
produced by Iaai participants. This indicates that in Iaai categorisation was more consistent
across participants, and there was lower inter-speaker variation. However, the lowest
variation in the free sort was demonstrated by speakers of North Ambrym. Additionally,
speakers of Lewo showed relatively low variation in the structured sort. This demonstrates
that there are factors other than inventory size contributing to these patterns. Arguably, the
relative cognitive salience of different classifiers may be influenced to some degree by
classifier inventory, but also by informativeness and rigidity. Here we see that
categorisation systems can affect conceptual structure, supporting previous evidence
(BORODITSKY; SCHMIDT; PHILLIPS, 2003), but also that this may be task dependent. This
offers a new perspective on the linguistic relativity debate, illustrating that influences of
language on cognition may be stronger for certain linguistic systems and may vary
depending on the way that language is being used.
In addition, the findings corroborate Hypothesis 4, indicating that North Ambrym, with
a more rigid gender-like system of noun-classifier assignment, exhibited less variation
across speakers in comparison to some languages with more typical classifier-like systems
that allow variable noun assignment (Merei, Vatlongos and Nêlêmwa). Indeed, speakers of
North Ambrym displayed relatively smaller standard deviation scores across both tasks,
indicating smaller variation, in comparison to Merei, Lewo and Vatlongos. North Ambrym’s
standard deviation scores were comparable to Iaai, which has the largest classifier
inventory. This suggests that more informative systems (Iaai) or more rigid gender-like
systems (North Ambrym), provide more structure for categorisation. Thus, these more
extreme systems may be more optimal for categorisation in comparison to more moderate
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systems. This casts new light on the debate about the trade-off between simplicity and
informativeness, in that cognition appears to be facilitated more optimally with either
informative systems with variable assignment or more simple systems with fixed
assignment. Nevertheless, evidence of less optimal systems is equally intriguing. This is
arguably the case for Merei, and also for Vatlongos, which shows a more balanced trade-
off between simplicity and informativeness. Although speakers of North Ambrym
demonstrated relatively smaller inter-speaker variation across the free sort task and the
structured sort task, there was also evidence of variation between the two tasks for this
language. Indeed, speakers of North Ambrym produced significantly more categories in the
free sort compared to the structured sort indicating cross-task variation. This could be
attributed to the explicit instruction to use classifiers in the structured sort, compared to the
more subjective judgement of the task demands in the free sort. These results again support
the idea that there is variability in communicative efficiency in linguistic categories across
languages (KEMP; REGIER, 2012).
Overall, there is mixed evidence for whether classifiers provide a structure for
cognition and guide categorisation. Effects of classifier inventory were evident: Iaai, with
the largest classifier inventory, placed fewer nouns into categories corresponding with
the common classifiers (general, food, drink), compared to Lewo and North Ambrym.
Further, there was a significant effect of language on the number of items placed into the
general classifier (cf. Figure 4). Collectively, these findings make an important
contribution to the debate that language influences cognition (BORODITSKY; SCHMIDT;
PHILLIPS, 2003), providing more concrete directions into how this may occur. Rather than
classifiers having a fixed and universal impact on cognition, their influence appears to be
variable and task dependent.
The qualitative analysis of the piles constructed by participants during the free sort
provides further insight into the extent to which classifiers influence cognition. One notable
element of the free sort pile labels was the lack of explicit reference to classifiers. While
there were incidents of participants directly labelling piles with a corresponding classifier,
participants largely labelled their piles as simple, literal descriptions of the items within. This
may suggest that when categorising objects, the default is not to use classifiers, but rather
to refer to broader characteristics. This fits with the finding that even native speakers of
languages that do not have nominal categorisation systems are privy to distinctions in
language that classifiers reflect. This is indicated by the influence of object similarities
underlying classifier systems, irrespective of the presence of classifiers within a language
(SPEED et al., 2016). Indeed, it may be that classifier systems reflect conceptual structure
rather than shaping it, and this seems to be reflected by many of the labels used by
participants to describe their groupings within the free sort task.
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In addition to literal descriptions of the items contained with piles, perhaps one of the
most interesting tendencies among participants was their inclination towards constructing
narratives with their piles. This sometimes enabled participants to provide a story linking
seemingly disparate items within individual piles. This approach also enabled participants
to explain how items were linked by practical usage and this was particularly interesting.
When an individual lists dog, pig alive, pig roasted, and rifle all together, and explains that a
man goes to hunt a pig with his dog, he is explaining something about his culture and his
manner of thinking through the medium of storytelling. Within other themes, we observed
speakers grouping items that ‘the white man made’ or that are ‘fruits of the sea’, again
independent of explicit reference to classifiers, reflecting similarities in form, source, or
function. Although these approaches did not make explicit reference to classifiers, they
often referred to the structure of those items in the daily life of the participant. In doing that,
participants frequently drew on their possessive relationships with objects. This highlights
a fundamental feature of classifiers and signals the relevance of classifier systems even in
the absence of explicit usage. It therefore appears that participants’ categorisation within
the card sort task was influenced by a range of different cultural and linguistic factors. Once
again, this suggests that classifiers may reflect rather than shape cognition, in line with our
quantitative analyses. This qualitative approach provides further context for our data,
adding to the methodological toolkit established within this wider project.
5. CONCLUSION
We have explored further the findings of Franjieh (2016, 2018), who showed differences in
categorisation between the systems of closely related languages. The current study found
that classifiers may influence categorisation only when they are explicit and salient, as was
the case for the structured sort task. It was found that the languages with larger classifier
inventories listed the greatest number of nouns and produced the largest number of
categories in the free sort task. The results suggest that classifiers do not always provide
structure for categorisation, as significantly more categories were made in the free sort
than in the structured sort task. Also, more extreme categorisation systems (whether more
informative or more rigid) seem to be more optimal systems of categorisation, showing
smaller variation across speakers in comparison to more moderate systems. Finally, our
qualitative analyses reveal that participants categorise entities based on a range of
properties, aligning with themes relating to the influence of classifiers in relation to items’
usage, how items are sourced and where they originate from, whether they are natural or
unnatural and how they can be related in terms of narrative. The study illustrates that
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language’s impact on cognition varies across different linguistic systems, and sheds light
on the intricacies of the linguistic relativity debate.
6. ACKNOWLEDGEMENTS
The support of the ESRC (grant: ES/R00837X/1: Optimal categorisation: the origin and
nature of gender from a psycholinguistic perspective) is gratefully acknowledged. This
paper is based on a part of a lecture given in the ABRALIN series, on 14 July 2020, with
co-presenters Sebastian Fedden and Erich Round. We thank Anne-Laure Dotte for her help
in conducting the experiments in New Caledonia, Collin Brown for his contributions to the
thematic analysis. We would like to thank our two reviewers, Angeliki Alvanoudi and
Alexander Yao Cobbinah for their insightful and helpful comments. We especially wish to
thank our 119 consultants in Vanuatu and New Caledonia for their invaluable input. In
particular, we wish to thank Willie Salong from the North Ambrym community; Elder Simeon
Ben from the Vatlongos community; Korau Melio from the Lewo community; Melkio
Wulmele, Adam and Hester Pike from the Merei community; Willion Phadom from the
Nêlêmwa community; and Wejë Bae from the Iaai community. We are grateful to Lisa Mack
and Penny Everson for their help in the preparation of the manuscript. Finally, we wish to
credit Kanauhea Wessels for image #28 in figure 2; Anne-Laure Dotte for images #20 and
#30; Eleanor Ridge for image #60; and Andrew Gray for images #32 and #33.
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