C. M. Wu

Adaptive mechanisms of social and asocial learning in immersive collective foraging

This study integrates social and asocial learning mechanisms using a virtual foraging task, finding that individual success, not social factors, drives adaptation in both learning types.

Specialization and selective social attention establishes the balance between individual and social learning

The paper investigates how individuals balance individual and social learning in a virtual foraging environment by analyzing specialization and selective social attention.

Similarities and differences in spatial and non-spatial cognitive maps

This study investigates how spatial and conceptual reasoning share and differ in computational principles using a Bayesian model of generalization in multi-armed bandit tasks.

Inference and search on graph-structured spaces

The paper develops a Bayesian model of function learning on graph-structured spaces and shows it better predicts human behavior in reward search tasks than alternative models.

Searching for rewards like a child means less generalization and more directed exploration

This study compares how children and adults search for rewards, finding that children explore more but generalize less, with directed exploration playing a key role.

The Evolutionary Dynamics of Cooperation in Collective Search

This study uses evolutionary simulations to show that unconditional information sharing can be an advantageous strategy in collective search, leading to self-organized groups and improved search efficiency.

Generalization as diffusion: human function learning on graphs

The paper tests a model of human function learning on graphs by having participants predict passenger volumes in a subway network, finding that diffusion-based generalization outperforms heuristic models.

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.