"Big" Sounds Bigger in More Widely Spoken Languages

The emergence of word order from a social network perspective

The distribution of word order across languages is skewed with SOV order (e.g., researchers sentences write) and SVO order (e.g., researchers write sentences) being >100 times more common than OSV order (sentences researchers write). It is commonly assumed that cross-linguistic preferences reflect cognitive biases, but it is unknown why some languages exhibit dispreferred patterns, or why languages settle on a specific pattern out of several preferred ones. This paper tests whether larger communities are more likely to rely on cross-linguistically preferred patterns as a way to overcome the greater communicative challenges they encounter. Participants played a communication game in large groups, small groups, or alone. Results support the hypothesis that community size influences word order as well as suggest that SOV and SVO orders are prevalent for different reasons with SVO specifically addressing communicative pressures. The studies thus show how community structure can give rise to cross-linguistic preferences, when these preferences can be overridden, and suggest how language might change with changes in social structure.

Japanese Sound-Symbolic Words for Representing the Hardness of an Object Are Judged Similarly by Japanese and English Speakers

Contrary to the assumption of arbitrariness in modern linguistics, sound symbolism, which is the non-arbitrary relationship between sounds and meanings, exists. Sound symbolism, including the “Bouba–Kiki” effect, implies the universality of such relationships; individuals from different cultural and linguistic backgrounds can similarly relate sound-symbolic words to referents, although the extent of these similarities remains to be fully understood. Here, we examined if subjects from different countries could similarly infer the surface texture properties from words that sound-symbolically represent hardness in Japanese. We prepared Japanese sound-symbolic words of which novelty was manipulated by a genetic algorithm (GA). Japanese speakers in Japan and English speakers in both Singapore and the United States rated these words based on surface texture properties (hardness, warmness, and roughness), as well as familiarity. The results show that hardness-related words were rated as harder and rougher than softness-related words, regardless of novelty and countries. Multivariate analyses of the ratings classified the hardness-related words along the hardness-softness dimension at over 80% accuracy, regardless of country. Multiple regression analyses revealed that the number of speech sounds /g/ and /k/ predicted the ratings of the surface texture properties in non-Japanese countries, suggesting a systematic relationship between phonetic features of a word and perceptual quality represented by the word across culturally and linguistically diverse samples.