# Oblique and verb word order relates to speakers' thought patterns 斜格要素と動詞の語順と話者の思考パターンとの関係

Terumasa EHARA
江原暉将
Ehara NLP Research Laboratory
江原自然言語処理研究室
eharate @ gmail.com
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\begin{abstract}
The author has quantitatively investigated the relationship between the word order of languages and speakers' thought patterns. In this paper, it will be focused on oblique $(\mathrm{X})$ and verb $(\mathrm{V})$ word order feature $(\mathrm{X}: \mathrm{V})$. There are not many languages for which the word order feature values for $\mathrm{X}: \mathrm{V}$ was revealed. So, I first expand the feature values for $\mathrm{X}: \mathrm{V}$ using the Universal Dependency Database. After expansion, correlation coefficients between nine word order features including $\mathrm{X}: \mathrm{V}$ and four quantitative metrics reflecting thought patterns (thought pattern metrics) are calculated. Results show that $\mathrm{X}: \mathrm{V}$ has significant correlations with all four thought pattern metrics, although object $(\mathrm{O})$ and verb $(\mathrm{V})$ word order feature $(\mathrm{O}: \mathrm{V})$, which is considered the most important word order feature, has significant correlations with only two thought pattern metrics.
\end{abstract
## 1 Introduction

The author has quantitatively investigated the relationship between the word order of languages and speakers' thought patterns. Word order (feature) means the order of dependent and head in the dependency grammar. Word order feature values are head initial or head final (sometimes no dominant order). Although I have focused on the word order of object $(\mathrm{O})$ and verb $(\mathrm{V})$ $(\mathrm{O}: \mathrm{V})$, which is considered the most important word order feature, I focus on the word order of oblique (X) and verb (V) $(\mathrm{X}: \mathrm{V})$ in this paper. The reasons why oblique is focused on are the following: "Place" which is emphasized in the linguistics of place [1] is expressed by oblique elements in the sentences. The "Topic" element in the topic prominent languages [2] (other than subject or object) is oblique.

There are not many languages for which the word order feature value for $\mathrm{X}: \mathrm{V}$ was revealed. For example, in WALS [3], O:V feature values (OV, VO, or No dominant order (NDO)) are described for 1,519 languages out of 2,679 languages (57\%). On the other hand, $\mathrm{X}: \mathrm{V}$ feature values (XV, VX, or NDO) are described only for 499 languages (19\%). In section 2, I will expand the X:V feature values using the Universal Dependency Database [4].

Four quantitative metrics reflecting thought patterns, which have been used in the previous papers, are also used in this paper. They are the following: The ratio of suicide rate and homicide rate (S/H_rate) $)^{i}$ [5], the entropy of the distribution of $\mathrm{Y}$ chromosome DNA haplogroup (Y_DNA) [6], the entropy of the distribution of Mitochondrial DNA sub-haplogroup (Mt_DNA) [7] and the ratio of $\mathrm{S}$ allele in serotonin transponder gene (S_ratio) [8]. These metrics are called thought pattern metrics. Details of these metrics should be referred to the references. In section 3, we examine the relation between the four thought pattern metrics and the nine word order features including $\mathrm{X}: \mathrm{V}$.
^{\mathrm{i}} \mathrm{S} / \mathrm{H} \_$rate $=\log _{10} \mathrm{~S} \_$rate $-\log _{10} \mathrm{H} \_$rate, where S_rate is the suicide rate and $\mathrm{H} \_$rate is the homicide rate.
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## 2 Expansion of $X: V$ feature values

We expand $\mathrm{X}: \mathrm{V}$ feature values using the Universal Dependency Database ${ }^{\mathrm{ii}}$ (UDD) [4]. UDD describes the dependency relations for 138 languages and the dependency relation between oblique and verb is described as the "obl" relation.

Table 1 Comparison of $r$ value obtained from the UDD and WALS feature value

 & Japanese & 1 & $\mathrm{XV}$ \\

In obl relations, I select the relations in which the part of speech of the dependent (X) is the noun (NOUN), pronoun (PNON) or proper noun (PROPN), and the part of speech of the head $(\mathrm{V})$ is the verb (VERB), adjective (ADJ) or adverb (ADV), as the $\mathrm{X}: \mathrm{V}$ relations. For data of each language, I define head final ratio $r=$ $n_{X V} /\left(n_{X V}+n_{V X}\right)$ where $n_{X V}$ is the number of $X V$ word order data and $n_{V X}$ is the number of VX word order data. These values for 138 languages are shown in the Appendix.

Table 1 shows $r$ values and $X: V$ feature values for 42 languages for which $X: V$ feature values are described in WALS and $r$ values are available by the UDD. The $X: V$ feature values in $r \leq 0.33$ are all VX in WALS, and $X: V$ feature values in $0.73 \leq \mathrm{r}$ are all $\mathrm{XV}$. No conflict between WALS and UDD exists.

## 3 Relation between word order features and thought pattern metrics

I use nine word order features shown in Table 2 including $\mathrm{X}: V$. Feature values are obtained mainly from WALS and several values are from Yamamoto [9]. Yamamoto does not describe the $X: V$ feature values, so $\mathrm{X}: \mathrm{V}$ feature values are complemented by the UDD. $\mathrm{X}: \mathrm{V}$ feature values of languages that $r<0.5$ are set as VX and $X: V$ feature values of languages that $0.5 \leq \mathrm{r}$ are set as $\mathrm{XV}$. No feature values other than $\mathrm{X}: V$ are complemented by the UDD.

To calculate the correlation between word order features and thought pattern metrics, head initial feature values are quantified to -1 , and head final feature values are quantified to 1 . Other feature values (such as NDO) are not used (regarded as No Data).

Table 2 Word order features used in the research

Table 3 shows Spearman's rank correlation coefficients, two-sided probabilities, and the number of data between nine word order feature values (after quantification) and


Table 3 Correlation coefficients between word order features and thought pattern metrics

(a) Spearman's rank correlation coefficient

(b) Two-sided Probability

(c) Number of data

(a) S/H_rate

(c) Mt_DNA

(b) Y_DNA

(d) S_ratio

Figure 1 Scattering diagrams between $X: V$ feature value (after quantification) and four thought pattern metrics

The cells colored pink are correlation coefficients that are significant at a significance level of $5 \%$, and all of them are positive. This shows that head initial language

iii All data used in this paper are open on the site: http://www.ne.jp/asahi/eharate/eharate/ronbun.files/NLP 2024.xlsx. speakers tend to score low on all four metrics and head final language speakers tend to score high on all four metrics.

iv https://sites.google.com/view/fukuimasayasu (accessed on 11 Feb. 2023)

Word order features which are significanct with all four thought pattern metrics are $\mathrm{X}: \mathrm{V}$ and $\mathrm{A}: \mathrm{N} . \mathrm{O}: \mathrm{V}$ has no significance in S/H_rate and Mt_DNA.

Figure 1 shows scattering diagrams of $\mathrm{X}: \mathrm{V}$ feature values (after quantification) and four thought pattern metrics values. In all scattering diagrams, data points are distributed from the lower left to the upper right, reflecting the positive correlation coefficients.

## 4 Conclusion

Spearman's rank correlation coefficients are calculated between nine word order features and four thought pattern metrics. Results show correlation coefficients that are significant with a significance level of $5 \%$ are all positive. The word order features that are significant for all four thought pattern metrics are the word order of oblique and verb $(\mathrm{X}: \mathrm{V})$, and the word order of adjective and noun $(A: N)$. The word order of object and verb $(\mathrm{O}: \mathrm{V})$, which we have focused on in previous studies, has no significance concerning the two metrics.

The relation between the four thought pattern metrics and the thought patterns themselves are mapped as shown in Table 4.

Table 4 Relation between four thought pattern metrics and the thought patterns themselves

From Table 4, it can be seen that head initial language speakers tend to have extrapunitive, competitive and optimistic thought patterns, while head final language speakers tend to have intropunitive, cooperative and pessimistic thought patterns.
^{v}$ For the place name, the narrow place name precedes and the wide place name follows; for the person's name, the personal name precedes and the family name follows.
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## 5 Related works and discussions

The relationship between language and thought is well known as the Sapir-Whorf hypothesis [11][12]. Although the relationship between the lexical structure of languages and thought patterns is extensively studied (for example [13][14]), there are few studies on the relationship between syntax, particularly word order, and thought patterns. A few examples, which are all qualitative studies, are the following:

Kido [15] claims that speakers who use narrow to wide word order in compound nouns expressing place names or person's names ${ }^{v}$ tend to think with subject-oriented logic, and speakers who use the opposite word order tend to think with place-oriented logic.

Nishimitsu [16] claims that speakers of prepositional languages (PN order) tend to have an impulsive cognitive style and speakers of postpositional languages (NP order) tend to have a reflective cognitive style.

Shibata [17] claims that speakers who use lower digit to higher digit word order in compound numerals ${ }^{v i}$ tend to have zoom-out type cognition and speakers who use opposite word order tend to have zoom-in type cognition.

In a bird language, it is said that important elements precede and unimportant elements follow [18]. If this is true in human languages, speakers of VX word order languages give more importance to their action $(\mathrm{V})$ than to the surrounding environment $(\mathrm{X})$. Therefore, they first decide their action (subject) and then think about the environment (place), so they have impulsive and zoomout type of cognition. On the other hand, speakers of XV word order languages give more importance to the surrounding environment $(\mathrm{X})$ than to their action (V). Therefore, they first think about the environment (place) and then decide their action (subject), so they have the reflective, zoom-in type of cognition.


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## Appendix Head final ratio $r$ for $X: V$ obtained from UDD

 & 0.15 \\

 & 2 & 295 & 0.99 \\

