Abstract: Successful engagement with the physical world involves perceiving the underlying structure, predicting how things unfold, and planning actions accordingly. This rich understanding and reasoning about our physical environment, or ‘intuitive physics’, develops early in infancy and is a core component of human cognition. While it seems easy for us to understand and navigate unfamiliar situations in the physical world, current machine learning systems are still far from achieving human-like generalizable performance. Can we look to the human brain for potential solutions? In this talk, I will present: i) a set of non-invasive neuroimaging (functional Magnetic Resonance Imaging or fMRI) studies characterizing the brain basis of physical reasoning in humans, ii) the first single-neuron level evidence for physical reasoning in the human brain using intracranial recordings, and iii) ongoing work benchmarking latest AI models on various physical reasoning tasks. Together, the results and methodology will help in not only understanding the neural mechanisms of physical reasoning but also discovering ways to bridge the reasoning gap between humans and AI. 

About the Speaker: RT Pramod is a computational cognitive neuroscientist at Massachusetts Institute of Technology whose research explores the neural and computational basis of physical scene understanding and reasoning. His work combines behavioral experiments, computational modeling and neuroimaging to understand human cognition. Pramod obtained his Ph.D. from the Indian Institute of Science (IISc) working on compositional representations underlying visual object perception. His research interests include visual perception, intuitive physics, human and machine cognition.