Abstract

How universal is human conceptual structure? The way concepts are organized in the human brain may reflect distinct features of cultural, historical, and environmental background in addition to properties universal to human cognition. Semantics, or meaning expressed through language, provides direct access to the underlying conceptual structure, but meaning is notoriously difficult to measure, let alone parameterize. Using cross-linguistic dictionaries, we provide here an empirical measure of semantic proximity between concepts and analyze the structure of a network derived from it. Across languages carefully selected from a phylogenetically and geographically stratified sample of genera, translations of words re- veal cases where a particular language uses a single polysemous word to express concepts represented by distinct words in another. We use the frequency of polysemy linking two concepts as a measure of their semantic proximity, and represent the pattern of such linkages by a weighted network. This network is highly uneven and fragmented: certain concepts are far more prone to polysemy than others, and there emerge naturally interpretable clusters that are loosely connected to each other. Furthermore, the networks of different language groups exhibit consistent structures, largely independent of geography, environment, and literacy. We therefore conclude the conceptual structure connecting basic vocabulary studied is primarily due to universal features of human cognition and language use.

Biography

Hyejin Youn is an Assistant Professor of Management & Organization Department at the Kellogg School of Management, and a core faculty at NICO, the Northwestern Institute on Complex Systems. She is also Royal Society of Arts fellow, and an external fellow at London Mathematical Laboratory, London, UK. Prior to joining Kellogg, she worked at University of Oxford, Harvard University, and MIT Media Lab, and Santa Fe Institute, as a research fellow. Hyejin received her PhD in Physics from Korea Advanced Institute of Science and Technology (KAIST). Her research interests are to understand the interplay between technological innovation and socio-economic systems (urbanisation, economic diversity and specialisation, invention activity, future of work). Her highly interdisciplinary approach often results in broad collaborations ranging from mathematicians, computer scientists, economists, sociologists, anthropologists, to archeologists. Her work has been published in general audience such as Nature communication, and PNAS, as well as top specialized journals such Physics Review Letter, and Evolutionary Anthropology, and has been featured in The Econonmist, Forbes, The Guardian, WIRED, Scientific America, MIT Technonlogy Review, among other major global media outlets. Her goal is to develop a theoretical, yet empirically grounded, framework that will enable us to turn the increasing volumes of data into scientific insights and well-designed policies, an approach known as computational social science. The mathematical tools and computational methods that are used include scaling theory, spatial analysis (including percolation theory, information theory and fractal dimension analysis), statistics, and network theory.