Rate My Professor Ari Kahn

Kahn's research examines planning and decision making, investigating how complex behaviors in humans and animals arise from learning and using predictive models of the world. He studies the neural mechanisms of these models, their developmental paths, and their roles in adaptive behavior, employing computational methods including reinforcement learning and network science. Key areas include graph learning, neural representations, successor representations, visuomotor learning, and hierarchical graphs. His publications feature "Network Structure Influences the Strength of Learned Neural Representations" (Nature Communications, 2025, co-authored with Szymula et al.), "Humans Rationally Balance Mental Simulation and Temporally Abstract World Models" (Communications Psychology, 2025), "Trial-by-Trial Learning of Successor Representations in Human Behavior" (PLoS Computational Biology, 2025), "Network Constraints on Learnability of Probabilistic Motor Sequences" (Nature Human Behaviour, 2018), and "Structural Pathways Supporting Swift Acquisition of New Visuomotor Skills" (Cerebral Cortex, 2017). Influential earlier works include "Controllability of Structural Brain Networks" (2015, over 1,000 citations). Awards include the Sackler Colloquium “Brain Produces Mind by Modeling” Travel Award (2019), SIAM Student Travel Award (2018), and Jameson-Hurvich Travel Award (2016). He teaches PSY 333: Judgment & Decision-Making.