Complex Adaptive Systems and the Origins of Adaptive Structure: What Experiments Can Tell Us

Complex Adaptive Systems and the Origins of Adaptive Structure: 

What Experiments Can Tell Us

Language is a product of both biological and cultural evolution. Clues to the origins of key structural properties of language can be found in the process of cultural transmission between learners. Recent experiments have shown that iterated learning by human participants in the laboratory transforms an initially unstructured artificial language into one containing regularities that make the system more learnable and stable over time. Here, we explore the process of iterated learning in more detail by demonstrating exactly how one type of structure—compositionality—emerges over the course of these experiments. We introduce a method to precisely quantify the increasing ability of a language to systematically encode associations between individual components of meanings and signals over time and we examine how the system as a whole evolves to avoid ambiguity in these associations and generate adaptive structure.

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the very fact that language persists through multiple repeated instances of usage can explain the origins of key structural properties that are universally present in language. Because of this, taking a complex adaptive systems perspective on language lifts the burden of explanation for these properties from a putative richly structured domain-specific substrate, of the sort assumed by much of generative linguistics (e.g., Chomsky, 1965).

Much of the work over the past 20 years or so in modeling the evolution of language has taken this complex adaptive systems perspective (see, e.g., Brighton, Smith, & Kirby, 2005; Kirby, 2002b; Steels, 2003, for review). One particular strand of work has focused on the adaptation of language through a repeated cycle of learning and use within and across generations, where adaptation is taken to mean a process of optimization or fitting of the structure of language to the mechanisms of transmission (Kirby, 1999).

(...) One of the ways in which a language can evolve to become more learnable is by becoming structured.

"alien" language... chain learning

First, by looking at the learning errors made between adjacent generations, it was shown that the languages in both conditions were being acquired significantly more faithfully toward the end of the chains than they were at the beginning. Second, this increase in learnability over time occurred as a result of the languages becoming more structured over time.

Summary

Kirby et al. (2008) found that the languages that emerge through a repeated cycle of learning and production in a laboratory setting show evidence of adaptation to the bottleneck placed on their transmission. Making even minor changes to the way in which language is culturally transmitted can produce radically different types of structures. Given only a bottleneck on transmission preventing a proportion of the language from being seen by the next generation, language can adapt in such a way that ensures that it is stably transmitted to future generations. However, this occurs at the expense of being able to uniquely refer to every meaning. When they introduced the additional pressure of having to use a unique signal for each meaning, the language once again adapted to cope with these new transmission constraints, this time by becoming compositional. Having a compositional system ensures that both signals and meanings survive the bottleneck.

Because the participants could not know which condition they were in, it is impossible that the resulting languages were intentionally designed as adaptive solutions to the transmission bottleneck. Rather, the best explanation for the result is that in these experiments, just as in the computational models, linguistic adaptation is an inevitable consequence of the transmission of linguistic variants under particular constraints on replication. The result is apparent design, but without an intentional designer.

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It seems clear from all of this that, first, cultural transmission alone is capable of explaining the emergence of languages that exhibit that appearance of design and, second, experimental studies of the iterated learning of artificial languages are a potentially useful methodological tool for those interested in studying cultural evolution.

Conclusion

This article has extended previous work on iterated language learning experiments by showing, using data obtained from an earlier study, exactly how compositional structure emerges over time as a result of cultural transmission. Using a recently developed analytical technique that calculates the regularity of mapping between signal and meaning elements (Tamariz & Smith, 2008), we were able to precisely quantify changes in the language’s ability to systematically encode such associations between meaning and signal components. From this we were able to explain the amplification effect the bottleneck seems to
have on systematicity in language, arguing that the sampling of smaller subsets of the language for training input to the next generation tends to make weaker patterns that are not visible at the level of the entire language appear stronger locally.