Area of research
Cognitive Neuroscience · General Decision Sciences
Research interest
Research interests include Computer science, Artificial intelligence, Cognitive science, Reinforcement learning, Psychology, and Inference.
Key-value memory in the brain
The neural architecture of theory-based reinforcement learning
Hippocampal remapping as hidden state inference
Discovery of hierarchical representations for efficient planning
Compositional inductive biases in function learning
Building machines that learn and think like people
Toward the neural implementation of structure learning
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines
Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms
Interplay of approximate planning strategies
Do learning rates adapt to the distribution of rewards?
Novelty and Inductive Generalization in Human Reinforcement Learning
Individual differences in learning predict the return of fear
Discovering hierarchical motion structure
Amortized Inference in Probabilistic Reasoning
eScholarship (California Digital Library) 2014cited by 215position: first
Statistical Computations Underlying the Dynamics of Memory Updating
Multitasking versus multiplexing: Toward a normative account of limitations in the simultaneous execution of control-demanding behaviors
Time representation in reinforcement learning models of the basal ganglia
Design Principles of the Hippocampal Cognitive Map
2014cited by 81position: last