Eric Schulz

This study shows humans learn better from slowly changing reward-predicting features in reinforcement learning tasks, suggesting an inductive bias toward slow features.

The paper investigates how the hippocampus enables interpolation into new states during category abstraction by analyzing neural representations in the neocortex and entorhinal cortex.

The paper examines context-dependent choice in real-world settings using restaurant rating data, finding that valuations and choices are influenced by the context of available options.

The paper shows how hippocampal cognitive maps adapt to guide reward generalization by shifting emphasis from temporal to spatial relationships as a task progresses.

This study investigates human multi-task reinforcement learning by comparing participant behavior to algorithms in a two-step decision-making task with changing rewards.

The paper tests if computational principles of spatial search apply to conceptual search using a Gaussian Process model and finds similar but distinct generalization patterns.