Decision Making Journal Club
Smokers’ brains compute, but ignore, a fictive error signal in a sequential investment task
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.