Aran Nayebi. Ph.D., Postdoctoral Fellow, MIT

Topic: “Bridging Neural Dynamics To Goal-Directed Behavior Across Timescales”

A core feature of human cognition is our ability to perform goal-directed actions, and to flexibly adapt our plans to meaningfully achieve these goals in a changing environment. Crucially, these abilities are conserved across many species and occur on multiple timescales. They minimally involve rapidly recognizing what the objects in an environment are, predicting their future motions over longer timescales, planning what actions to take, and finally executing them. A precise, computational understanding of how our brains implement these goal-directed behaviors is greatly lacking.

In the past decade, particularly in the domain of rapid object recognition, advancements in artificial intelligence (AI) have yielded performant models that are highly predictive of temporally-averaged neural responses in the ventral visual pathway. However, this functional account crucially falls short of explaining behaviorally-relevant neural dynamics. I therefore extend these methods to study aspects of goal-directed behaviors across three timescales: 1. the role of recurrent processing in rapid object recognition (within 250 ms), 2. visually-grounded mental simulation of future environmental states over longer timescales (within several seconds), and 3. identifying operative rules during task learning (within an organism’s lifetime). Finally, I conclude with future directions towards closing the perception-action loop by building integrative, embodied agents that serve as normative accounts of the interaction of brain areas that enable taking meaningful actions in complex, dynamic environments. The design of these agents would enable the prediction of novel cognitive hypotheses when existing data is scarce, and lead to improved, physically-grounded AI algorithms that can more adeptly and safely interact with the world.

Aran Nayebi is an ICoN Postdoctoral Fellow at MIT, currently working with Robert Yang and Mehrdad Jazayeri. He completed his PhD in Neuroscience at Stanford University, co-advised by Daniel Yamins and Surya Ganguli. His interests lie at the intersection of neuroscience, cognitive science, and artificial intelligence (AI), where he uses tools from AI to better understand how the brain enables adaptive, goal-directed behaviors. His long-term aim is to focus on the sensorimotor loop essential for survival and physical interaction, in order to produce normative accounts of how brain areas collaborate to give rise to complex embodied behaviors, and to build more physically-grounded, common-sense AI algorithms along the way.