A cultural evolutionary model of patterns in semantic change

Abstract

Language change has been described as an unintended effect of language in use (Keller 1994). In this view, change results from the way individuals use their language; the challenge is thus to explain change and its properties in terms of factors operating on the individual level, and population dynamics. An intriguing example of such a phenomenon is the finding that language change shows some highly regular tendencies. This has recently received considerable attention in the literature (Bybee et al. 1994; Heine and Kuteva 2002; Traugott and Dasher 2002; Hopper and Traugott 2003). In unrelated languages, similar words often change in similar ways, along similar ‘‘trajectories’’ of development. This phenomenon is called ‘‘unidirectionality’’, and it is an important part of processes of grammaticalization, items changing from a lexical meaning to a grammatical function. It has been claimed that around 90–99% of all processes of grammaticalization are unidirectional (Haspelmath 1999).

This article explores several mechanisms that may lead to language change, and examines whether they may be responsible for unidirectionality. We use a cultural evolutionary computational model with which the effects of individual behavior on the group level can be measured. By using this approach, regularities in semantic change can be explained in terms of very basic mechanisms and aspects of language use such as the frequency with which particular linguistic items are used. One example is that frequency differences by themselves are a strong enough force for causing unidirectionality. We argue that adopting a cultural evolutionary approach may be useful in the study of language change.

The advantage of using a cultural evolutionary approach is that it models patterns in complex systems such as the language of a population as a result of the interactions between individuals, and by population-level processes of selection and random drift. (p. 363)

This approach is not new in itself: Keller (1994) proposes an approach of this kind in his ‘‘invisible hand theory’’. In his model, group level phenomena are to be reduced to individual behavior, and the notions of mutation on the one hand and spread or propagation of new variants on the other are distinguished. He states that language change is the unintended result of intentional individual behavior. Individuals apply strategies or ‘‘maxims’’ when they use their language, such as ‘‘speak in such a way that you are communicatively successful’’ and ‘‘talk like the others talk’’. Although most of these maxims lead to the creation of linguistic conventions, some maxims can — unintentionally — lead to change, such as ‘‘talk in such a way that you are noticed’’ and ‘‘talk in such a way that you do not spend superfluous energy’’. Haspelmath (1999) uses Keller’s model to explain unidirectionality in grammaticalization. (p. 363)

HEEL BELANGRIJK

Cultural evolution view

Croft (2000) gives a conceptual model of language change as cultural evolution. He explicitly states that actual utterances are the units of cultural transmission, a view that we adopt in this article. He also differentiates between linguistic factors and social factors. Linguistic or cognitive factors give rise to new variants that come into existence by both intentional and unintentional mechanisms such as reanalysis, creativity and economy. Whether these mutations spread through a population depends on social factors, such as the structure of the population and the prestige of speakers. (p. 364)

Agent-based computer models of language change have been used in other studies before, such as Niyogi and Berwick (1997) and Yang (2000). These models simulate syntactic change as a result of imperfect learning. A different approach, which is more comparable to the model presented in this article, is the use of ‘‘language games’’. In these models, change is the result of communication and adjustment of the agents’ knowledge based on the input they receive, and imperfect learning plays no role (e.g., de Boer 2001). In general, most of the studies using computer simulations so far have focused on syntax and phonetics. As Steels (2003) has pointed out, grammaticalization and unidirectionality have received less attention in this line of research. (p. 365)

we take a usage-based approach to language change, in which individuals construct their linguistic knowledge on the basis of the input they receive in communication, in which actual utterances are the units of transmission and in which the locus of mutation is in adult communication (Bybee and Slobin 1982; Croft 2000; Croft and Cruse 2004; Slobin 2005)

(p. 368)

We use a so-called ‘‘agent-based model’’ of cultural evolution. The approach derives its name from the fact that it is a computer simulation of a group of individuals, or agents. The behavior of each agent can be independently controlled, and its effect on the population can be measured (p. 368)

The simple model we present here simulates the semantic evolution of a single random word w in a population of speakers. The meaning of w is represented by a set of senses, which represent concrete uses of w. These senses are positioned on a one dimensional scale with a range of values between 0 and 1. Each value on this scale represents a specific sense of w with nearby values representing similar senses. The left end of the scale (with value 0) is arbitrarily chosen to represent lexical senses and the right end of the scale (with value 1) functional senses (Figure 1).

(p. 369)

Agents construct their linguistic knowledge on the basis of input they receive during communication. Communication in the model is the random selection of two agents from the population, one of which is assigned the role of speaker and one the role of hearer. The speaker selects a specific sense (represented by a value) from its set of senses and transmits it to the hearer. This models the evaluation by the speaker that the word w is applicable in the specific context, given the set of senses of w that the speaker knows. The hearer compares the transmitted sense to its own set of senses, i.e., it evaluates whether the word w is applicable in the context, given its set of senses of w. When this sense is already part of the hearer’s knowledge of w, communication is successful and the communication process comes to an end. However, the speaker can also transmit a sense that is unknown to the hearer, i.e., that is outside the hearer’s range of senses associated with w. In that case, communication fails, and the fact of this failure is understood by not only the hearer, but also the speaker.

(p. 370)

Unsuccessful communication results in a learning process, in which both agents adjust their sets of senses of w. The hearer, confronted with a new sense, will increase its set up to (and including) the uttered sense. The speaker, confronted with unsuccessful communication, realizes that any values beyond the uttered sense will lead to more unsuccessful communication and therefore decreases its set and makes the uttered sense its new limit.

(p. 371)

Apart from the learning process described above, agents also change their linguistic knowledge by mutation. Mutation in the model is a randomly occurring small change in set size. Agents that are selected for communication have a probability m_r to undergo mutation before that communication event. Mutations may be extensions or constrictions on either side of the set. In linguistic reality, possible causes for the former include the need to express something for which there is not yet a signal, and for the latter the need to redress ‘‘semantic overextension’’ or competition by another word.

(p. 371)

The population consists of 100 agents, and the agents have a maximum age of 70 years, after which they are replaced by an agent with age 0. Newborn agents start with an exact copy of the set of senses of a randomly assigned ‘‘parent’’, after which they participate fully in the communication between agents. Note that this ‘‘parent’’ is not the agent that is being replaced (because in such a case there would be no need to add generations in the model). Rather, the transmission of the parent knowledge is a simplification of the acquisition process. This means that any evolution displayed by the model is not due to imperfect learning situations in child language acquisition, but to variation coming about and spreading in adults; in this way we are able to test whether such variation can by itself lead to semantic change. Note that this does not mean that transmission in the model is completely horizontal (i.e., within peer groups only); communication is random between all agents regardless of their age, and therefore transmission can be said to be both horizontal and oblique (Cavalli-Sforza and Feldman 1981). (p. 372)

The simulations show slightly di¤erent behaviors each time they are run, with fluctuations in the average meaning size as the result: specialization and generalization both occur. Basically, the simulations exhibit random drift in the direction of both the upper and lower limit of the meaning set. With meanings drifting in both directions along the scale, there is evolution, but no unidirectionality. (p. 373)

We tested the effect of three factors on this coherency: mutation rate, frequency of use and population structure. Coherency was measured as the average amount of overlap, between agents in that population, of the sets of senses. The greater this overlap, the greater the consensus about the meaning of word 𝑤 (eq. 3 in the appendix). (p. 374)

First, the mutation rate in the population should not be too high. A certain amount of communication is needed for a single mutation to spread through the entire population and to even out the emerged variation between the agents. When the number of communications relative to the mutation rate becomes too low, the individual variation caused by mutation is not transmitted to other individuals often enough, thus causing a lower coherency (p. 374)