Dopamine transients do not act as model-free prediction errors during associative learning

Melissa J. Sharpe, Hannah M. Batchelor, Lauren E. Mueller, Chun Yun Chang, Etienne J.P. Maes, Yael Niv, Geoffrey Schoenbaum

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


Dopamine neurons are proposed to signal the reward prediction error in model-free reinforcement learning algorithms. This term represents the unpredicted or ‘excess’ value of the rewarding event, value that is then added to the intrinsic value of any antecedent cues, contexts or events. To support this proposal, proponents cite evidence that artificially-induced dopamine transients cause lasting changes in behavior. Yet these studies do not generally assess learning under conditions where an endogenous prediction error would occur. Here, to address this, we conducted three experiments where we optogenetically activated dopamine neurons while rats were learning associative relationships, both with and without reward. In each experiment, the antecedent cues failed to acquire value and instead entered into associations with the later events, whether valueless cues or valued rewards. These results show that in learning situations appropriate for the appearance of a prediction error, dopamine transients support associative, rather than model-free, learning.

Original languageEnglish (US)
Article number106
JournalNature communications
Issue number1
StatePublished - Dec 1 2020

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology
  • General Physics and Astronomy


Dive into the research topics of 'Dopamine transients do not act as model-free prediction errors during associative learning'. Together they form a unique fingerprint.

Cite this