Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning.

ABSTRACT: Often the world is structured such that distinct sensory contexts signify the same abstract rule set. Learning from feedback thus informs us not only about the value of stimulus-action associations but also about which rule set applies. Hierarchical clustering models suggest that learners discover structure in the environment, clustering distinct sensory events into a single latent rule set. Such structure enables a learner to transfer any newly acquired information to other contexts linked to the same rule set, and facilitates re-use of learned knowledge in novel contexts. Here, we show that humans exhibit this transfer, generalization and clustering during learning. Trial-by-trial model-based analysis of EEG signals revealed that subjects' reward expectations incorporated this hierarchic

SUBMITTER: Collins AGE 

PROVIDER: S-EPMC5595218 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature