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Smokers' brains compute, but ignore, a fictive error signal in a sequential investment task.

Nature neuroscience | Apr 27, 2008

Addicted individuals pursue substances of abuse even in the clear presence of positive outcomes that may be foregone and negative outcomes that may occur. Computational models of addiction depict the addicted state as a feature of a valuation disease, where drug-induced reward prediction error signals steer decisions toward continued drug use. Related models admit the possibility that valuation and choice are also directed by 'fictive' outcomes (outcomes that have not been experienced) that possess their own detectable error signals. We hypothesize that, in addiction, anomalies in these fictive error signals contribute to the diminished influence of potential consequences. Using a simple investment game and functional magnetic resonance imaging in chronic cigarette smokers, we measured neural and behavioral responses to error signals derived from actual experience and from fictive outcomes. In nonsmokers, both fictive and experiential error signals predicted subjects' choices and possessed distinct neural correlates. In chronic smokers, choices were not guided by error signals derived from what might have happened, despite ongoing and robust neural correlates of these fictive errors. These data provide human neuroimaging support for computational models of addiction and suggest the addition of fictive learning signals to reinforcement learning accounts of drug dependence.

Pubmed ID: 18311134 RIS Download

Mesh terms: Adult | Attention | Behavior, Addictive | Brain Mapping | Cerebral Cortex | Choice Behavior | Female | Field Dependence-Independence | Games, Experimental | Humans | Inhibition (Psychology) | Magnetic Resonance Imaging | Male | Middle Aged | Models, Psychological | Serial Learning | Smoking | Tobacco Use Disorder

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Associated grants

  • Agency: NIDA NIH HHS, Id: R01 DA11723
  • Agency: NIDA NIH HHS, Id: R01 DA011723
  • Agency: NIMH NIH HHS, Id: F32 MH078485
  • Agency: NIMH NIH HHS, Id: T32 MH18882
  • Agency: NINDS NIH HHS, Id: R01 NS045790

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