Ida Momennejad

The paper shows that neural representations of temporal community structure in hippocampus and precuneus predict inductive reasoning decisions based on learned object relationships.

The paper investigates how predictive representations guide navigation by analyzing neuroimaging data from virtual reality navigation tasks, finding differences in predictive horizons across brain regions.

The paper provides neural evidence for the successor representation in choice evaluation by linking behavioral errors to predictive representations in sensory cortex.

This paper proposes a computational model of anxiety based on pessimistic beliefs about future avoidance, showing how such biases can explain various anxiety-related behaviors and phenomena.

Running successor representations at several discount scales, then taking their derivative through an inverse Laplace transform, is proposed to recover upcoming state order and distance to goal.

The paper uses fMRI to show that offline replay of trajectories and uncertainty sensitivity support planning during reward revaluation.

This paper proposes a framework linking model-based reinforcement learning to model-free mechanisms using successor representations and introduces algorithms that mitigate their limitations.

This study investigates the successor representation (SR) in human reinforcement learning, showing it balances flexibility and efficiency by being insensitive to task sequence changes but responsive to reward changes.