Background: Human and animal work suggests a shift from goal-directed to habitual decision-making in addiction. However, the evidence for this in human alcohol dependence is as yet inconclusive. Methods: Twenty-six healthy controls and 26 recently detoxified alcohol-dependent patients underwent behavioral testing with a 2-step task designed to disentangle goal-directed and habitual response patterns. Results: Alcohol-dependent patients showed less evidence of goal-directed choices than healthy controls, particularly after losses. There was no difference in the strength of the habitual component. The group differences did not survive controlling for performance on the Digit Symbol Substitution Task. Conclusion: Chronic alcohol use appears to selectively impair goal-directed function, rather than promoting habitual responding. It appears to do so particularly after nonrewards, and this may be mediated by the effects of alcohol on more general cognitive functions subserved by the prefrontal cortex. © 2014 S. Karger AG, Basel

In detoxified alcohol-dependent patients, alcohol-related stimuli can promote relapse. However, to date, the mechanisms by which contextual stimuli promote relapse have not been elucidated in detail. One hypothesis is that such contextual stimuli directly stimulate the motivation to drink via associated brain regions like the ventral striatum and thus promote alcohol seeking, intake and relapse. Pavlovian-to-Instrumental-Transfer (PIT) may be one of those behavioral phenomena contributing to relapse, capturing how Pavlovian conditioned (contextual) cues determine instrumental behavior (e.g. alcohol seeking and intake). We used a PIT paradigm during functional magnetic resonance imaging to examine the effects of classically conditioned Pavlovian stimuli on instrumental choices in n = 31 detoxified patients diagnosed with alcohol dependence and n = 24 healthy controls matched for age and gender. Patients were followed up over a period of 3 months. We observed that (1) there was a significant behavioral PIT effect for all participants, which was significantly more pronounced in alcohol-dependent patients; (2) PIT was significantly associated with blood oxygen level-dependent (BOLD) signals in the nucleus accumbens (NAcc) in subsequent relapsers only; and (3) PIT-related NAcc activation was associated with, and predictive of, critical outcomes (amount of alcohol intake and relapse during a 3 months follow-up period) in alcohol-dependent patients. These observations show for the first time that PIT-related BOLD signals, as a measure of the influence of Pavlovian cues on instrumental behavior, predict alcohol intake and relapse in alcohol dependence.

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Individuals differ in how they learn from experience. In Pavlovian conditioning paradigms, where cues predict reinforcer delivery at a different goal location, some animals-so-called signtrackers-come to approach the cue, whereas others, called goal-trackers, approach the goal. In sign-trackers, model-free phasic dopaminergic reward prediction errors underlie learning, which renders stimuli 'wanted'. Goal-trackers do not rely on dopamine for learning and are thought to use model-based learning. We demonstrate this double dissociation in 128 male humans using eye-tracking, pupillometry and fMRI informed by computational models of sign-and goal-tracking. We show that sign-trackers exhibit a neural reward prediction error signal that is not detectable in goal-trackers. Model-free value only guides gaze and pupil dilation in sign-trackers. Goal-trackers instead exhibit a stronger model-based neural state prediction error signal. This model-based construct determines gaze and pupil dilation more in goal-trackers. Main Learning from reinforcements involves multiple processes with distinct computational, neural and behavioral signatures. Consider a simple classical Pavlovian conditioning paradigm, where a cue (conditioned stimulus, CS) becomes predictive of a reward (unconditioned stimulus, US) by being repeatedly presented before the US. In so-called model-free reinforcement learning, learning occurs via reward prediction errors (RPE 1) which quantify the difference between the value of the US that actually arrives and a current prediction of that value made on the basis of the CS. Integration of the experienced RPEs allows the predictions made by the CS, called a 'cached value' to become accurate. An alternative way of learning involves building a model which has two components: a 'transition structure', which captures the probability that one stimulus is followed by another, and a 'reward structure', which captures the value associated with each particular stimulus. Learning the transition structure 2,3 can occur by integration of a different sort of so-called state prediction errors (SPE 4) which quantify the difference between the stimulus that actually occurs and the probability of this event that was estimated on the basis of the previous stimulus. This type of learning does not conflate transitions and rewards, and is hence more flexible when one of these changes. However, it