LLMs have profoundly transformed the way research is conducted and develops across a wide range of disciplines, including linguistics, philosophy, psychology, and the social sciences. Beyond their technical performance, these systems raise new questions about language, cognition, interpretation, and the production of knowledge itself.
This new online seminar, jointly organized by Caroline Rossi (U. Grenoble Alpes / MIAI) and Thierry Poibeau (ENS-PSL / PR[AI]RIE-PSAI) aims to explore recent research in these areas. It will provide a forum for discussing both empirical and theoretical work, bringing together perspectives from different fields to better understand the implications of LLMs for the study of language and mind. The seminar also seeks to foster dialogue between researchers who use these models in practice and those who critically examine their assumptions, limitations, and broader impact.
The first speaker will be Steven Piantadosi, from Berkeley. The next speakers will include Adele Goldberg (Princeton), Eloïse Boisseau (AMU, Marseille), and Dallas Card (U. Michigan).
The seminar will take place approximately once a month. The full schedule for the coming months will be announced shortly.
Neuroscience, behavior, and what’s in-between
Steven T. Piantadosi, UC Berkeley (Psychology) & Helen Wills Neuroscience Institute
I’ll present an overview of a forthcoming book about how we can link neuroscience to cognition and behavior. Drawing on several little-known results in early computer science, I’ll describe how patterns in behavior can rigorously imply the existence of particular unobserved states and structures. This provides a foundation for linking behavioral regularities to what must be present in neural implementations. The resulting states are often re-describable in abstract terms more familiar to cognitive science, like “sets”,
“numbers”, “stacks”, etc. I’ll highlight the implementation of “stacks”, commonly used for grammars, and show how to characterize the space of possible neural implementations, including with subsystems/circuits operating in serial and parallel. The approach provides a set of concrete hypotheses, a guide for neural data analysis, and points towards a method for understanding structure in modern AI systems, including LLMs. I’ll conclude by suggesting a Marr-like framework in which the bridges between levels can be made rigorous, connecting behavior, high-level theorizing, and neural implementation.
Steven T. Piantadosi is a professor at UC Berkeley in Psychology and the Helen Wills Neuroscience Institute, where he heads the Computation and Language Lab. He has a PhD from MIT in Brain and Cognitive Sciences and undergraduate degrees in mathematics and linguistics. His work spans neural and cognitive research, with a focus on understanding how children come to know language, math, and abstract concepts. He often uses computational methods, including machine learning, cognitive modeling, mathematical analysis, and Bayesian data analysis. His research methods also include anthropological fieldwork, experimental work with children, and collaboration to study non-human primates and human neuroscience.
