Events

Date: 26 November 2025   Time: 11:00 - 12:00

Location: 4.01 Peter Landin

Abstract

All known languages are made up of statistically coherent sequences - words - whose frequency distribution follows a power law known as a Zipfian distribution. Despite the ubiquity of these features across languages their origins are poorly understood. In this talk, I will argue that they arise because they facilitate learning and therefore emerge through the process of cultural transmission of language. I will present a set of results on the learnability sources and consequences of such distributions in human language. We will then summarise results from an experiment in which non-linguistic sequences evolve as they are transmitted from generation to generation of participants. By using insights from infant speech segmentation, we analyse those sequences and observe the emergence of Zipf's law over generations. This work makes a prediction that we should find Zipfian distribution of statistically coherent sequences wherever systems culturally evolve, including in other species. However, so far these features have only been found in humans. In the second part of the talk I will turn to the culturally evolving song of humpback whales and apply the same analytic technique to 8 years of whale recordings. Together with Ellen Garland and Simon Kirby, we show, for the first time in another species, that these characteristic statistical properties are indeed present in whale song. By doing so, we demonstrate a deep commonality between two species separated by tens of millions of years of evolution but united by both having culture.

Bio

Prof. Arnon is a developmental psychologist, linguist and cognitive scientist and the head of the Language Learning and Processing Lab at the Hebrew University of Jerusalem. She is currently a Leverhulme Trust Fellow at the University of Edinburgh. Her research program focuses on understanding human's unique ability to learn, use, and develop language, and more specifically, on understanding how children acquire language, how they differ from adults in doing so, and how learnability pressures shape the emergence and structure of human language. Prof. Arnon has worked extensively on first language acquisition, developing a novel framework for understanding why children are better language learners than adults, with applied implications for human and machine learning (The Starting Big Approach, see Arnon, 2021 for a review). In her current projects, she asks how learning pressures and constraints can explain why languages look the way they do, how language evolved, and how insights from child learning can be used to study non-human communication.