We introduce an experimental paradigm for studying the cumulative cultural evolution of language. In doing so we provide the first experimental validation for the idea that cultural transmission can lead to the appearance of design without a designer. Our experiments involve the iterated learning of artificial languages by human participants. We show that languages transmitted culturally evolve in such a way as to maximize their own transmissibility: over time, the languages in our experiments become easier to learn and increasingly structured. Furthermore, this structure emerges purely as a consequence of the transmission of language over generations, without any intentional design on the part of individual language learners. Previous computational and mathematical models suggest that iterated learning provides an explanation for the structure of human language and link particular aspects of linguistic structure with particular constraints acting on language during its transmission. The experimental work presented here shows that the predictions of these models, and models of cultural evolution more generally, can be tested in the laboratory.cultural transmission ͉ iterated learning ͉ language evolution T he emergence of human language has been cited by Maynard Smith and Szathmary (1) as the most recent of a small number of highly significant evolutionary transitions in the history of life on earth. The reason they give for including language in this list is that language enables an entirely new system for information transmission: human culture. Language is unique in being a system that supports unlimited heredity of cultural information, allowing our species to develop a unique kind of open-ended adaptability.Although this feature of language as a carrier of cultural information obviously is important, we have argued that there is a second sense in which language is an evolutionary milestone: each utterance has a dual purpose, carrying semantic content but also conveying information about its own construction (2-5). Upon hearing a sentence, a language learner uses the structure of that sentence to make new inferences about the language that produced it. This process allows learners to reverse-engineer the language of their speech community from the utterances they hear. Language thus is both a conveyer of cultural information (in Maynard Smith and Szathmary's sense) and is itself culturally transmitted. This cultural transmission makes language an evolutionary system in its own right (2-3), suggesting another approach to the explanation of linguistic structure. Crucially, language also represents an excellent test domain for theories of cultural evolution in general, because the acquisition and processing of language are relatively well understood, and because language has an interesting, nontrivial, but well documented structure. § During the past 10 years a wide range of computational and mathematical models have looked at a particular kind of cultural evolution termed ''iterated learning'' (4-13).Iterated ...

Language exhibits striking systematic structure. Words are composed of combinations of reusable sounds, and those words in turn are combined to form complex sentences. These properties make language unique among natural communication systems and enable our species to convey an open-ended set of messages. We provide a cultural evolutionary account of the origins of this structure. We show, using simulations of rational learners and laboratory experiments, that structure arises from a trade-off between pressures for compressibility (imposed during learning) and expressivity (imposed during communication). We further demonstrate that the relative strength of these two pressures can be varied in different social contexts, leading to novel predictions about the emergence of structured behaviour in the wild.

Human languages may be shaped not only by the (individual psychological) processes of language acquisition, but also by population-level processes arising from repeated language learning and use. One prevalent feature of natural languages is that they avoid unpredictable variation. The current work explores whether linguistic predictability might result from a process of iterated learning in simple diffusion chains of adults. An iterated artificial language learning methodology was used, in which participants were organized into diffusion chains: the first individual in each chain was exposed to an artificial language which exhibited unpredictability in plural marking, and subsequent learners were exposed to the language produced by the previous learner in their chain. Diffusion chains, but not isolate learners, were found to cumulatively increase predictability of plural marking by lexicalising the choice of plural marker. This suggests that such gradual, cumulative population-level processes offer a possible explanation for regularity in language.

Cross-situational learning is a mechanism for learning the meaning of words across multiple exposures, despite exposure-by-exposure uncertainty as to the word's true meaning. We present experimental evidence showing that humans learn words effectively using cross-situational learning, even at high levels of referential uncertainty. Both overall success rates and the time taken to learn words are affected by the degree of referential uncertainty, with greater referential uncertainty leading to less reliable, slower learning. Words are also learned less successfully and more slowly if they are presented interleaved with occurrences of other words, although this effect is relatively weak. We present additional analyses of participants' trial-by-trial behavior showing that participants make use of various cross-situational learning strategies, depending on the difficulty of the word-learning task. When referential uncertainty is low, participants generally apply a rigorous eliminative approach to cross-situational learning. When referential uncertainty is high, or exposures to different words are interleaved, participants apply a frequentist approximation to this eliminative approach. We further suggest that these two ways of exploiting cross-situational information reside on a continuum of learning strategies, underpinned by a single simple associative learning mechanism.