Experimental Data in Baboons (Papio Papio), 2016-2019

Dautriche, Isabelle (2021). Experimental Data in Baboons (Papio Papio), 2016-2019. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-855099

As anyone who has learnt a foreign language or travelled abroad will have noticed, languages differ in the sounds they employ, the names they give to things, and the rules of grammar. However, linguists have long observed that, beneath this surface diversity, all human languages share a number of fundamental structural similarities. Most obviously, all languages use sounds, all languages have words, and all languages have a grammar. More subtly and more surprisingly, similarities can also be observed in more fine-grained linguistic features: for instance, George Zipf famously observed that, across multiple languages, short words tend also to be more frequent, and in my own recent work I have shown that languages prefer to use words that sound alike (e.g., cat, mat, rat, bat, fat, ...). Why do all languages exhibit these shared features? This project aims to tackle exactly this key question by studying how languages are shaped by the human mind. In particular, I will explore how the way we learn language and use it to communicate drives the emergence of important features of lexicons, the set of all words in a language. To simulate the process of language change and evolution in the lab, I will use an experimental paradigm where an artificial language is passed between learners (language learning), and used by individuals to communicate with each other (language use). This paradigm has been successfully applied in previous research showing that key structural features of language can be explained as a consequence of repeated learning and use; my contribution will be to apply the same methods to study the evolution of the lexicon. I will then use two complementary techniques to evaluate the ecological validity of these results. First, do the artificial lexicons obtained after repeated learning and communication match the structure of lexicons found in real human languages? We will assess this by analyzing real natural language corpora using computational methods. Second, are these lexicons easily learnable by young children, the primary conduit of natural language transmission in the wild? This will be assessed using methods from developmental psychology to study word learning in toddlers. The present project requires an unprecedented integration of techniques and concepts from language evolution, computational linguistics and developmental psychology, three fields that have so far worked independently to understand the structure of language. The outcomes of the project will be of vital interest for all these communities, and will provide insights into the foundational properties found in all human languages, as well as the nature of the constraints underlying language processing and language acquisition. This project will provide a springboard for my future work at the intersection of computational and experimental approaches to language and cognitive development.

Data description (abstract)

Using a pattern extraction task, we show that baboons, like humans, have a learning bias that helps them discover connected patterns more easily than disconnected ones—i.e., they favor rules like “contains between 40% and 80% red” over rules like “contains around 30% red or 100% red.” The task was made as similar as possible to a task previously run on humans, which was argued to reveal a bias that is responsible for shaping the lexicons of human languages, both content words (nouns and adjec- tives) and logical words (quantifiers). The current baboon result thus suggests that the cognitive roots responsible for regularities across the content and logical lexicons of human languages are present in a similar form in other species.

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