M. Speekenbrink

Generalization guides human exploration in vast decision spaces

This study investigates how humans navigate vast decision spaces using generalization, finding that Gaussian Process function learning with an optimistic Upper Confidence Bound strategy explains human search behavior.

Generalization and search in risky environments

The paper studies how people search for rewards in risky environments and finds that their behavior aligns with a Gaussian process function learning algorithm that prioritizes safety.

Compositional Inductive Biases in Function Learning

The paper shows humans prefer compositional structures in function learning, finding them more predictable and memorable than non-compositional ones.

Putting bandits into context: How function learning supports decision making

The paper investigates how participants learn and make decisions in uncertain environments using contextual multi-armed bandit tasks, finding that Gaussian process learning strategies are effective in various contexts.