The unique cumulative nature of human culture has often been explained by high-fidelity copying mechanisms found only in human social learning. However, transmission chain experiments in human and non-human primates suggest that cumulative cultural evolution (CCE) might not necessarily depend on high-fidelity copying after all. In this study, we test whether defining properties of CCE can emerge in a non-copying task. We performed transmission chain experiments in Guinea baboons and human children where individuals observed and produced visual patterns composed of four squares on touchscreen devices. In order to be rewarded, participants had to avoid touching squares that were touched by a previous participant. In other words, they were rewarded for innovation rather than copying. Results nevertheless exhibited fundamental properties of CCE: an increase over generations in task performance and the emergence of systematic structure. However, these properties arose from different mechanisms across species: children, unlike baboons, converged in behaviour over generations by copying specific patterns in a different location, thus introducing alternative copying mechanisms into the non-copying task. In children, prior biases towards specific shapes led to convergence in behaviour across chains, while baboon chains showed signs of lineage specificity. We conclude that CCE can result from mechanisms with varying degrees of fidelity in transmission and thus that high-fidelity copying is not necessarily the key to CCE.

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Compositional hierarchical structure is a prerequisite for productive languages; it allows language learners to express and understand an infinity of meanings from finite sources (i.e., a lexicon and a grammar). Understanding how such structure evolved is central to evolutionary linguistics. Previous work combining artificial language learning and iterated learning techniques has shown how basic compositional structure can evolve from the trade-off between learnability and expressivity pressures at play in language transmission. In the present study we show, across two experiments, how the same mechanisms involved in the evolution of basic compositionality can also lead to the evolution of compositional hierarchical structure. We thus provide experimental evidence showing that cultural transmission allows advantages of compositional hierarchical structure in language learning and use to permeate language as a system of behaviour.

Languages exhibit variation at all linguistic levels, from phonology, to the lexicon, to syntax. Importantly, that variation tends to be (at least partially) conditioned on some aspect of the social or linguistic context. When variation is unconditioned, language learners regularise it-removing some or all variants, or conditioning variant use on context. Previous studies using artificial language learning experiments have documented regularising behaviour in learning of lexical, morphological, and syntactic variation. These studies implicitly assume that regularisation reflects uniform mechanisms and processes across linguistic levels. However, studies on natural language learning and pidginisation suggest that morphological and syntactic variation may be treated differently. In particular, there is evidence that morphological variation may be more susceptible to regularisation (Good 2015;Siegel 2006;Slobin 1986). Here we provide the first systematic comparison of the strength of regularisation across these two linguistic levels. In line with previous studies, we find that the presence of a favoured variant can induce different degrees of regularisation. However, when input languages are carefully matchedwith comparable initial variability, and no variant-specific biases-regularisation can be comparable across morphology and word order. This is the case regardless of whether the task is explicitly communicative. Overall, our findings suggest an overarching regularising mechanism at work, with apparent differences among levels likely due to differences in inherent complexity or variant-specific biases. Differences between production and encoding in our tasks further suggests this overarching mechanism is driven by production.

The idea that universal representations of hierarchical structure constrain patterns of linear order is a central to many linguistic theories. In this paper we use Artificial Language Learning techniques to experimentally probe this claim. Specifically, we investigate how a hypothesized hierarchy of φ-features impacts the linearization of person and number affixes by (English-speaking) learners in the lab.