Nicholas T. Franklin

The paper introduces a neuro-symbolic model called SEM that organizes continuous experiences into discrete events and generalizes memory, showing human-like event segmentation in video data.

The paper investigates how humans adaptively generalize task structures during reinforcement learning, finding they switch between compositional and conjunctive generalization based on task statistics.

This paper introduces a Bayesian agent that learns to generalize task structures by clustering transition and reward functions independently, showing that performance depends on task domain statistics.