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We study how linguistic structure evolves using experiments with human participants and computer models. The central question in most of our work is how cognitive biases interact with cultural evolution mechanisms (i.e., interaction between individuals and transmission across generations) to shape the structures we see in human languages. To do this we are developing novel methods to study emerging communication systems in the lab and with computational agents. Most AI language models are trained through exposure to large amounts of data. However, human language evolved in a much more dynamic setting. We use our knowledge of human language evolution to improve the dynamics of neural-agent language simulations.
Members
Yuqing Zhang [PhD researcher]
My main research focuses on simulating the evolution of linguistic structure using neural agents. Specifically, I want to develop methods to simulate the cultural evolution of linguistic structure through neural network agent-based computational modeling, and to explore the nature of learners' bias (whether it's statistically learned or internal cognitive constraints).
More info on her website!
Tom Kouwenhoven [PhD researcher]
It is a familiar phenomenon: you ask the assistant on your phone to call your mother, but it calls a friend instead. I investigate how humans and Artificial Intelligence (AI) can better communicate with each other, so that these kinds of situations will no longer occur in the future.
Tom defended his thesis on October 30th, 2025, his thesis can be found here!
Yuchen Lian [PhD researcher]
My research explores the emergence of language universals using neural agent simulations. Currently, I’m focusing on the word order/case-marking trade-off in natural languages, which is one of the most well-known language universals. I have developed a cutting-edge framework called NeLLCom, which stands for Neural-agent Language Learning and Communication. Basic model training procedures such as supervised learning and rewards fine-tuning based on communicative success are implemented there. Taking inspiration from Language Evolution research, NeLLCom also carries out various other learning paradigms, e.g. iterated learning and group communication.
Yuchen defended her thesis on October 30th, 2025, her thesis can be found here!
Ross Towns [PhD researcher]
I am a PhD candidate studying the roles of cooperation, social cognition and community structure in the emergence of linguistic systems, in order to make inferences about the evolution of language in early humans. To do this, I use a variety of methods, including lab experiments and computational models, drawing on findings from cognitive science, linguistics and evolutionary anthropology.
Thesis projects
We always have a group of MSc and BSc students who are working on their thesis with us. If you are interesting in joining and are curious to see what a thesis project could look like, please check the LIACS thesis repository for examples. Occasionally, thesis projects and studies conducted during my courses result in publications at conferences or in journals, see for example:
A project on Visual Entailment: Pitta et al. (2025) [LUHME workshop @ EACL]
A project on the Black Stories riddle game in humans and LLMs: Smid et al. (2025) [CogSci]
A project on swarm simulations and connectedness to nature: Piliouras et al. (2022) [GALA]
A project on prosociality and cross-modal associations: Sommer et al. (2022) [JCoLE]
A project on using a drone to impact pacing behaviour in 1500m running: van Son et al. (2022) [ECSS]
A project on lexical semantics in Natural Language Understanding: Barbouch et al. (2022) [CLIN Journal]
Collaborators
Arianna Bisazza [University of Groningen]
Seana Coulson [University of California, San Diego]
Tyler Marghetis [University of California, Merced]
Eduard Fosch-Villaronga [Leiden University, e-Law]
Rebecca Schaefer [Leiden University, FSW]
Creative Intelligence Lab
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