NeurIPS 2022 Workshop
Information-Theoretic Principles in Cognitive Systems

December 3, 2022
New Orleans Convention Center

Many cognitive and neural systems can be described in terms of compression and transmission of information given bounded resources. While information theory, as a principled mathematical framework for characterizing such systems, has been widely applied in neuroscience and machine learning, its role in understanding cognition has traditionally been contested. This traditional view has been changing in recent years, with growing evidence that information-theoretic optimality principles underlie a wide range of cognitive functions, including perception, working memory, language, and decision making. In parallel, there has also been a surge of contemporary information-theoretic approaches in machine learning, enabling large-scale neural-network implementation of information-theoretic models.

These scientific and technological developments open up new avenues for progress toward an integrative computational theory of human and artificial cognition, by leveraging information-theoretic principles as bridges between various cognitive functions and neural representations. This workshop aims to explore these new research directions and bring together researchers from machine learning, cognitive science, neuroscience, linguistics, economics, and potentially other fields, who are interested in integrating information-theoretic approaches that have thus far been studied largely independently of each other. In particular, we aim to discuss questions and exchange ideas along the following directions:

Important dates

Invited Speakers

Tatyana Sharpee
Salk Institute, UCSD

Michael Woodford
Columbia University

Jessica Flack
Santa Fe Institute

Ryan Cotterell
ETH Zurich

Stephanie Palmer
University of Chicago