Signed and unsigned reward prediction errors dynamically enhance learning and memory

Nina Rouhani, Yael Niv

Research output: Contribution to journalArticlepeer-review

Abstract

Memory helps guide behavior, but which experiences from the past are priori-tized? Classic models of learning posit that events associated with unpredictable outcomes as well as, paradoxically, predictable outcomes, deploy more attention and learning for those events. Here, we test reinforcement learning and subsequent memory for those events, and treat signed and unsigned reward prediction errors (RPEs), experienced at the reward-predictive cue or reward outcome, as drivers of these two seemingly contradictory signals. By fitting reinforcement learning models to behavior, we find that both RPEs contribute to learning by modulat-ing a dynamically changing learning rate. We further characterize the effects of these RPE signals on memory, and show that both signed and unsigned RPEs enhance memory, in line with midbrain dopamine and locus-coeruleus modulation of hippocampal plasticity, thereby reconciling separate findings in the literature.

Original languageEnglish (US)
Article numbere61077
JournaleLife
Volume10
DOIs
StatePublished - Mar 2021

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

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