Bridging Minds and Machines
Our research aims to advance our understanding of the computational basis of cognition and to use insights from cognitive science and neuroscience to build more interpretable, flexible, and cognitively grounded AI systems.
To achieve this we combine behavioral experiments, real-world developmental data, neural recordings, computational modeling, and machine learning.
Specifically, we analyze behavior in memory and learning tasks, study infant egocentric visual experience and eye movements, work with neural data from in vitro and in vivo single-unit recordings, construct neural-level computational models that account for both behavior and neural activity, and integrate those models into trainable systems that can be evaluated on the same tasks used in animal studies and on machine learning benchmarks. This work draws on a range of insights from cognitive science and neuroscience, including continual, curriculum-based self-supervised learning from temporal and spatial regularities in the natural world (such as slow-changing sensory structure), and structured, navigable mental maps of physical and abstract spaces that support memory retrieval and planning.
Recent advances in Large Language Models (LLMs) and Vision-Language Models (VLMs) have also created an opportunity to study whether analogous structured representations emerge in foundation models. These comparisons reveal where model representations align with human cognition and which inductive biases current AI systems still lack, helping guide the development of more interpretable, sample-efficient, and robust architectures by combining foundation models with cognitive models.
See our Projects to learn more.