Our goal is to understand the cognitive and computational basis of visual intelligence.
How do we leverage cognitive science approaches with deep neural network models together, to understand how machines are learning, where they are failing, and to inform and improve our own cognitive models of visual intelligence?
How does the human brain transform patterns of light into meaningful representations of the world — e.g. of objects and agents, interacting in places?
How do attention and memory access these representations? And how does vision interface with and constrain higher level cognition — e.g. social and physical inferences, aesthetic judgments and curiosity.
We approach these questions using behavioral studies, brain imaging, and neurostimulation methods, and complement these empirical techniques with computational modeling, leveraging recent advances in the field of artificial intelligence and machine learning.