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Investigating the effects of prestige on the diffusion of linguistic variants

Investigating the effects of prestige on the diffusion of linguistic variants Monica Tamariz (monica@ling.ed.ac.uk) Language Evolution and Computation Research Unit, The University of Edinburgh, 3 Charles Street, Edinburgh, EH8 1AD, UK Tao Gong (gtojty@gmail.com) Department of Linguistics, University of Hong Kong Pokfulam Road, Hong Kong Gerhard Jager (gerhard.jaeger@uni-tuebingen.de) Department of Linguistics, University of Tubingen, Wilhelmstrase 19, 72074 Tubingen, Germany Abstract Language, arguably the cognitive capacity that distinguishes humans, is a dynamic complex adaptive system whose structure and evolution is influenced by a host of factors. This paper takes a population dynamics approach to investigate the diffusion of linguistic variants in populations, focusing on the effect of differential prestige of linguistic variants and of speakers. A novel method that combines computer simulation with mathematical modeling is applied to the specific aim of identifying factors that formally constitute selective pressures on variant diffusion. Of the factors studied, only the intrinsic prestige of variants is found to pose selective pressure, while speakers’ prestige merely modulates variant spread. Keywords: Language evolution, Price equation, Polya urns, population dynamics. Introduction Language, arguably the cognitive capacity that defines humans, is a dynamic complex adaptive system (Beckner et al. 2009) whose structure and evolution is influenced by a host of factors. We apply the principles of population genetics (Fisher, 1930; Wright, 1984) to language, and focus on one aspect of language evolution, i.e., the changes in the proportions of linguistic variants in a linguistic community. Such changes are usually achieved via diffusion of various (phonetic, lexical, syntactic, etc.) variants. At the population level, linguistic diffusion (henceforth simply “diffusion”) can be viewed as the shift in the proportions of linguistic variants used by a population over time (Nakamura et al., 2007). Some well-documented examples of diffusion include the Great Vowel Shift in English occurring from the 14th to 16th century (e.g., Wolfe, 1972), other sound changes in modern languages (e.g. Shen, 1997; Labov, 2001), and lexical borrowing among languages (e.g., Bloomfield, 1933; Cheng, 1987). Examining the mechanisms for diffusion can shed light on questions concerning the cognitive capacities for language and the effects of linguistic or socio-cultural constraints on language evolution (Wolfe, 1972; Pinker & Bloom, 1990; Croft, 2000; Hauser et al., 2002; Tomasello, 2008). Empirical studies from historical linguistics and sociolinguistics have revealed that linguistic, individual learning and socio-cultural factors could all affect diffusion (e.g., Labov, 1994, 2001; Shore, 1995; Fisiak, 1995; Croft, 2000), and recently, mathematical analysis and computer simulation have been used to quantitatively analyze the effects of these factors on diffusion. By quantifying the contact patterns and constraints within or across populations, mathematical analysis helps to predict the influence of these factors (e.g., Nowak et al., 2002; Abram & Strogatz, 2003; Wang et al., 2004; Dall’asta et al., 2006; Kalampokis et al., 2007; Minett & Wang, 2008); by simulating individual behaviors during linguistic interactions, computer modeling helps to trace how interactions among individuals spur the origin of a common set of form-meaning mappings (e.g., Steels, 1995; Ke et al., 2002), how processing constraints lead to linguistic regularities (e.g., Kirby, 2002; Gong et al., 2009), and how social connections affect diffusion within and across communities (e.g., Nettle, 1999; Ke et al., 2008). Diffusion can be driven by chance factors; then the process is called drift and follows the neutral model of evolution (Kimura, 1968). It can also be driven by selection, in which case, a feature of the linguistic variants (e.g. ease of pronunciation, cognitive salience, social prestige) increases its fitness, i.e., makes it more likely to be used and learned, and therefore diffused among speakers, than alternative variants. The present study focuses on three factors, namely variant prestige, model bias and transmission error, and seeks to establish whether each of them poses selective pressure on the evolution of linguistic variants. How these factors relate to linguistic behavior is illustrated next: First, linguistic variants possess feature values which may affect the probability with which the variants carrying them are adopted and used, in other words, they affect the variant’s prestige. A physical feature, such as the ease in perception, for instance, could confer high prestige to a certain variant (Labov, 1994), meaning that it will be more likely to be produced than other variants. Cheng (1987) describes lexical variants borrowed from other languages as high- prestige when they are more salient to hearers than the existing variants for the same meaning. Second, in human society, ordinary people preferentially copy from individuals (models) of higher social, political, or economic status (Labov, 1963; Johnstone, 2010); this is called