Belief about nicotine selectively modulates value and reward prediction error signals in smokers

Xiaosi Gu, Terry Lohrenz, Ramiro Salas, Philip R. Baldwin, Alireza Soltani, Ulrich Kirk, Paul M. Cinciripini, P. Read Montague

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


Little is known about how prior beliefs impact biophysically described processes in the presence of neuroactive drugs, which presents a profound challenge to the understanding of the mechanisms and treatments of addiction. We engineered smokers' prior beliefs about the presence of nicotine in a cigarette smoked before a functional magnetic resonance imaging session where subjects carried out a sequential choice task. Using a model-based approach, we show that smokers' beliefs about nicotine specifically modulated learning signals (value and reward prediction error) defined by a computational model of mesolimbic dopamine systems. Belief of "no nicotine in cigarette" (compared with "nicotine in cigarette") strongly diminished neural responses in the striatum to value and reward prediction errors and reduced the impact of both on smokers' choices. These effects of belief could not be explained by global changes in visual attention and were specific to value and reward prediction errors. Thus, by modulating the expression of computationally explicit signals important for valuation and choice, beliefs can override the physical presence of a potent neuroactive compound like nicotine. These selective effects of belief demonstrate that belief can modulate modelbased parameters important for learning. The implications of these findings may be far ranging because belief-dependent effects on learning signals could impact a host of other behaviors in addiction as well as in other mental health problems.

Original languageEnglish
Pages (from-to)2539-2544
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number8
StatePublished - 24 Feb 2015
Externally publishedYes


  • Belief
  • Dopamine
  • FMRI
  • Nicotine addiction
  • Reinforcement learning


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