Nicotine is the primary addictive substance in tobacco, which stimulates neural pathways mediating reward processing. However, pure biochemical explanations are not sufficient to account for the difficulty in quitting and remaining smoke-free among smokers, and in fact cognitive factors are now considered to contribute critically to addiction. Using model-based functional neuroimaging, we show that smokers’ prior beliefs about nicotine specifically impact learning signals defined by principled computational models of mesolimbic dopamine systems. We further demonstrate that these specific changes in neural signaling are accompanied by measurable changes in smokers’ choice behavior. Our findings suggest that subjective beliefs can override the physical presence of a powerful drug like nicotine by modulating learning signals processed in the brain’s reward system.
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 model-based 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.