Holistic Reinforcement Learning: The Role of Structure and Attention

Angela Radulescu, Yael Niv, Ian Ballard

Research output: Contribution to journalReview articlepeer-review

52 Scopus citations


Compact representations of the environment allow humans to behave efficiently in a complex world. Reinforcement learning models capture many behavioral and neural effects but do not explain recent findings showing that structure in the environment influences learning. In parallel, Bayesian cognitive models predict how humans learn structured knowledge but do not have a clear neurobiological implementation. We propose an integration of these two model classes in which structured knowledge learned via approximate Bayesian inference acts as a source of selective attention. In turn, selective attention biases reinforcement learning towards relevant dimensions of the environment. An understanding of structure learning will help to resolve the fundamental challenge in decision science: explaining why people make the decisions they do.

Original languageEnglish (US)
Pages (from-to)278-292
Number of pages15
JournalTrends in Cognitive Sciences
Issue number4
StatePublished - Apr 2019

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Neuropsychology and Physiological Psychology
  • Cognitive Neuroscience


  • Bayesian inference
  • approximate inference
  • category learning
  • corticostriatal circuits
  • dopamine
  • representation learning
  • rule learning
  • striatum


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