Instrumental and Pavlovian Mechanisms in Alcohol Use Disorder

Doñamayor, Nuria; Ebrahimi, Claudia; Garbusow, Maria; Wedemeyer, Friederike; Schlagenhauf, Florian; Heinz, Andreas

doi:10.1007/s40429-020-00333-9

Cited by 11 publications

(8 citation statements)

References 314 publications

(457 reference statements)

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“…Such habitual response tendencies have been argued to underlie ongoing alcohol intake despite negative outcomes in AUDP ( 51 , 52 ). However, in humans, evidence for this assumption is mixed ( 34 , 53 , 54 ). In the reversal learning paradigm, increases in perseverative responding after reversals or decreases in punishment learning rates potentially reflect habitual response tendencies.…”

Section: Discussionmentioning

confidence: 99%

Computational analysis of probabilistic reversal learning deficits in male subjects with alcohol use disorder

et al. 2022

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BackgroundAlcohol use disorder is characterized by perseverative alcohol use despite negative consequences. This hallmark feature of addiction potentially relates to impairments in behavioral flexibility, which can be measured by probabilistic reversal learning (PRL) paradigms. We here aimed to examine the cognitive mechanisms underlying impaired PRL task performance in patients with alcohol use disorder (AUDP) using computational models of reinforcement learning.MethodsTwenty-eight early abstinent AUDP and 27 healthy controls (HC) performed an extensive PRL paradigm. We compared conventional behavioral variables of choices (perseveration; correct responses) between groups. Moreover, we fitted Bayesian computational models to the task data to compare differences in latent cognitive variables including reward and punishment learning and choice consistency between groups.ResultsAUDP and HC did not significantly differ with regard to direct perseveration rates after reversals. However, AUDP made overall less correct responses and specifically showed decreased win–stay behavior compared to HC. Interestingly, AUDP showed premature switching after no or little negative feedback but elevated proneness to stay when accumulation of negative feedback would make switching a more optimal option. Computational modeling revealed that AUDP compared to HC showed enhanced learning from punishment, a tendency to learn less from positive feedback and lower choice consistency.ConclusionOur data do not support the assumption that AUDP are characterized by increased perseveration behavior. Instead our findings provide evidence that enhanced negative reinforcement and decreased non-drug-related reward learning as well as diminished choice consistency underlie dysfunctional choice behavior in AUDP.

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Section: Discussionmentioning

confidence: 99%

Computational analysis of probabilistic reversal learning deficits in male subjects with alcohol use disorder

et al. 2022

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“…Moreover, studying each effect in isolation can inform clinical practice by helping to understand which mechanism is at play in the maladaptive behavior and should, thus, be tackled. In line with this, several studies suggest that an alteration of general transfer contributes to relapse in maladaptive behaviors (for a review, see Doñamayor et al, 2021) like drug addiction and alcohol use disorder (Sommer et al, 2017(Sommer et al, , 2020. The selective involvement of general transfer in maladaptive cue-guided choice suggests that treatments should focus on modifying the motivational aspects of the outcomes involved in the maladaptive conduct.…”

Section: Discussionmentioning

confidence: 89%

General Pavlovian-to-instrumental transfer in humans: Evidence from Bayesian inference

Degni

Dalbagno

Starita

et al. 2022

Front. Behav. Neurosci.

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When repeatedly paired with rewarding outcomes (i.e., Pavlovian conditioning), environmental cues may acquire predictive and motivational significance and later enhance instrumental responding for the same (i.e., outcome-specific transfer) or motivationally similar (i.e., general transfer) outcomes. Although outcome-specific and general Pavlovian-to-Instrumental Transfer (PIT) are characterized by different neural substrates and behavioral mechanisms, general transfer has never been studied in isolation from outcome-specific transfer in humans. The first aim of the present study was to test whether the general transfer effect could emerge in isolation and independently of outcome-specific transfer. Our results showed that general transfer can be elicited without the concurrent presence of outcome-specific transfer, supporting the idea that outcome-specific and general transfer can be studied independently of each other. The second aim of the present study was to clarify whether the affordance-like properties of the outcomes can affect the general transfer. In fact, a critical difference in current studies on general transfer concerns the use of cues associated with outcomes for which an action was previously learned (or not) during the instrumental training. This apparently minor difference affects the affordance-like properties of the outcome and may also be transferred to the cue, in turn impacting general transfer. Results revealed a general transfer of the same magnitude regardless of whether cues were associated with reward earned or not during instrumental conditioning. These findings increase the current knowledge on the incentive motivational mechanism behind general transfer, indicating that it is independent of the motor features of the outcome.

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“…It is not known, however, if the relative contribution of model-free and model-based mechanisms that are recruited in an individual during Pavlovian autoshaping procedures are predictive of their relative contribution during instrumental procedures, such as in the MSDM task (Sebold et al, 2016). If true, this could suggest that the computational mechanisms underlying learning are not unique to Pavlovian or instrumental mechanisms but may represent a common reinforcement-learning framework within the brain that could be useful for restoring the learning mechanisms that are abnormal in disease states (Doñamayor et al, 2021; Groman et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Conserved reward-mediated, reinforcement-learning mechanisms in Pavlovian and instrumental tasks

Afshar

Cinotti

Khamassi

et al. 2022

Preprint

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Model-free and model-based computations are argued to distinctly update action values that guide decision-making processes. It is not known, however, if these model-free and model-based reinforcement learning mechanisms recruited in operationally based, instrumental tasks parallel those engaged by Pavlovian based behavioral procedures. Recently, computational work has suggested that individual differences in the attribution of incentive salience to reward predictive cues, i.e., sign- and goal-tracking behaviors, are also governed by variations in model-free and model-based value representations that guide behavior. Moreover, it is not appreciated if these systems that are characterized computationally using model-free and model-based algorithms are conserved across tasks for individual animals. In the current study, we used a within-subject design to assess sign-tracking and goal-tracking behaviors using a Pavlovian conditioned approach task, and, then characterized behavior using an instrumental multi-stage decision-making (MSDM) task. We hypothesized that both Pavlovian and instrumental learning processes may be driven by common reinforcement-learning mechanisms. Our data confirm that sign-tracking behavior was associated with greater reward-mediated, model-free reinforcement learning and that it was also linked to model-free reinforcement learning in the MSDM task. Computational analyses revealed that Pavlovian model-free updating was correlated with model-free reinforcement learning in the MSDM task. These data provide key insights into the computational mechanisms mediating associative learning that could have important implications for normal and abnormal states.

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Instrumental and Pavlovian Mechanisms in Alcohol Use Disorder

Cited by 11 publications

References 314 publications

Computational analysis of probabilistic reversal learning deficits in male subjects with alcohol use disorder

Computational analysis of probabilistic reversal learning deficits in male subjects with alcohol use disorder

General Pavlovian-to-instrumental transfer in humans: Evidence from Bayesian inference

Conserved reward-mediated, reinforcement-learning mechanisms in Pavlovian and instrumental tasks

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