Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Conference Paper

Searching for rewards in graph-structured spaces

There are no MPG-Authors in the publication available
External Resource
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Wu, C., Schulz, E., & Gershman, S. (2019). Searching for rewards in graph-structured spaces. In Conference on Cognitive Computational Neuroscience (CCN 2019) (pp. 814-817).

Cite as: https://hdl.handle.net/21.11116/0000-0005-D5E1-C
How do people generalize and explore structured spaces? We study human behavior on a multi-armed bandit task, where rewards are influenced by the connectivity structure of a graph. A detailed predictive model comparison shows that a Gaussian Process regression model using a diffusion kernel is able to best describe participant choices, and also predict judgments about expected reward and confidence. This model unifies psychological models of function learning with the Successor Representation used in reinforcement learning, thereby building a bridge between different models of generalization.