January 21, 2022 – AtticusĀ Geiger

Causal Abstraction and Computational Explanations in Artificial Intelligence Theories of causal abstraction are a bridge between symbolic and connectionist models of computations, allowing for formally precise accounts of when a symbolic computation is implemented by a neural network. I will present on recent work where we both (1) analyze neural networks to determine whether they […]

February 11, 2022 – Anne Collins

Setting the stage for reinforcement learning Reinforcement learning frameworks have contributed tremendously to our better understanding of learning processes in brain and behavior. However, this remarkable success obscures the reality of multiple underlying processes, and in particular hides how executive functions set the frame over which reinforcement learning computations operate. What is a choice? What […]