The involuntary nature of binge drinking: goal directedness and awareness of intention

Neural correlates of model-based behavior in internet gaming disorder and alcohol use disorder

Background

An imbalance between model-based and model-free decision-making systems is a common feature in addictive disorders. However, little is known about whether similar decision-making deficits appear in internet gaming disorder (IGD). This study compared neurocognitive features associated with model-based and model-free systems in IGD and alcohol use disorder (AUD).

Method

Participants diagnosed with IGD (n = 22) and AUD (n = 22), and healthy controls (n = 30) performed the two-stage task inside the functional magnetic resonance imaging (fMRI) scanner. We used computational modeling and hierarchical Bayesian analysis to provide a mechanistic account of their choice behavior. Then, we performed a model-based fMRI analysis and functional connectivity analysis to identify neural correlates of the decision-making processes in each group.

Results

The computational modeling results showed similar levels of model-based behavior in the IGD and AUD groups. However, we observed distinct neural correlates of the model-based reward prediction error (RPE) between the two groups. The IGD group exhibited insula-specific activation associated with model-based RPE, while the AUD group showed prefrontal activation, particularly in the orbitofrontal cortex and superior frontal gyrus. Furthermore, individuals with IGD demonstrated hyper-connectivity between the insula and brain regions in the salience network in the context of model-based RPE.

Discussion and Conclusions

The findings suggest potential differences in the neurobiological mechanisms underlying model-based behavior in IGD and AUD, albeit shared cognitive features observed in computational modeling analysis. As the first neuroimaging study to compare IGD and AUD in terms of the model-based system, this study provides novel insights into distinct decision-making processes in IGD.

What is the Intention to Move and When Does it Occur?

In 1983 Benjamin Libet and colleagues published a paper apparently challenging the view that the conscious intention to move precedes the brain's preparation for movement. The experiment initiated debates about the nature of intention, the neurophysiology of movement, and philosophical and legal understanding of free will and moral responsibility. Here we review the concept of "conscious intention" and attempts to measure its timing. Scalp electroencephalographic activity prior to movement, the Bereitschaftspotential, clearly begins prior to the reported onset of conscious intent. However, the interpretation of this finding remains controversial. Numerous studies show that the Libet method for determining intent, W time, is not accurate and may be misleading. We conclude that intention has many different aspects, and although we now understand much more about how the brain makes movements, identifying the time of conscious intention is still elusive.

Computational Mechanisms of Addiction and Anxiety: A Developmental Perspective

A central goal of computational psychiatry is to identify systematic relationships between transdiagnostic dimensions of psychiatric symptomatology and the latent learning and decision-making computations that inform individuals' thoughts, feelings, and choices. Most psychiatric disorders emerge prior to adulthood, yet little work has extended these computational approaches to study the development of psychopathology. Here, we lay out a roadmap for future studies implementing this approach by developing empirically and theoretically informed hypotheses about how developmental changes in model-based control of action and Pavlovian learning processes may modulate vulnerability to anxiety and addiction. We highlight how insights from studies leveraging computational approaches to characterize the normative developmental trajectories of clinically relevant learning and decision-making processes may suggest promising avenues for future developmental computational psychiatry research.

The modulation of acute stress on model-free and model-based reinforcement learning in gambling disorder

BACKGROUND AND AIMS

Experiencing acute stress is common in behavioral addictions such as gambling disorder. Additionally, like most substance-induced addictions, aberrant decision-making wherein a reactive habit-induced response (conceptualized as a Model-free [MF] in reinforcement learning) suppresses a flexible goal-directed response (conceptualized as a Model-based [MB]) is also common in gambling disorder. In the current study we investigated the influence of acute stress on the balance between habitual response and the goal-directed system.

METHODS

A sample of N = 116 problem gamblers (PG) and healthy controls (HC) performed an acute stress task - the Socially Evaluated Cold pressure task (SECPT) - or a control task. Self-reported stress and salivary cortisol were collected as measures of acute stress. Following the SECPT, participants performed the Two-Step Markov Task to account for the relative contribution of MB and MF strategies. Additionally, verbal working memory and IQ measures were collected to account for their mediating effects on the orchestration between MB/MF and the impact of stress.

RESULTS

Both groups had comparable baseline and stress-induced cortisol response to the SECPT. Non-stressed PG displayed lower MB learning than HC. MANOVA and regression analyses showed a deleterious effect of stress-induced cortisol response on the orchestration between MB and MF learning in HC but not in PG. These effects remained when controlling for working memory and IQ.

DISCUSSION AND CONCLUSIONS

We found an abnormal pattern of modulation of stress on the orchestration between MB and MF learning among PG. Several interpretations and future research directions are discussed.

Slower Learning Rates from Negative Outcomes in Substance Use Disorder over a 1-Year Period and Their Potential Predictive Utility

Computational modelling is a promising approach to parse dysfunctional cognitive processes in substance use disorders (SUDs), but it is unclear how much these processes change during the recovery period. We assessed 1-year follow-up data on a sample of treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 83) that were previously assessed at baseline within a prior computational modelling study. Relative to healthy controls (HCs; N = 48), these participants were found at baseline to show altered learning rates and less precise action selection while completing an explore-exploit decision-making task. Here we replicated these analyses when these individuals returned and re-performed the task 1 year later to assess the stability of baseline differences. We also examined whether baseline modelling measures could predict symptoms at follow-up. Bayesian and frequentist analyses indicated that: (a) group differences in learning rates were stable over time (posterior probability = 1); and (b) intra-class correlations (ICCs) between model parameters at baseline and follow-up were significant and ranged from small to moderate (.25 ≤ ICCs ≤ .54). Exploratory analyses also suggested that learning rates and/or information-seeking values at baseline were associated with substance use severity at 1-year follow-up in stimulant and opioid users (.36 ≤ rs ≤ .43). These findings suggest that learning dysfunctions are moderately stable during recovery and could correspond to trait-like vulnerability factors. In addition, computational measures at baseline had some predictive value for changes in substance use severity over time and could be clinically informative.

Dynamic structural equation modeling of the relationship between alcohol habit and drinking variability

BACKGROUND

A hyper-engaged habit system may be common in alcohol use disorders (AUDs). Regarding drinking patterns, habit may be expressed as higher levels of drinking autoregression, where previous day drinking is correlated with next day drinking. This study utilized dynamic structural equation models (DSEM) with intensive longitudinal data to understand whether alcohol habit relates to drinking autoregression and variable levels of alcohol consumption.

METHODS

Participants were adult drinkers (N = 313) who completed baseline self-report assessments of past 30-day alcohol consumption and alcohol habit. Alcohol habit was measured by the Self Report Habit Index (SRHI). Thirty-day coding of the Timeline Followback assessed total daily drinking and any daily heavy drinking.

RESULTS

The DSEM model for daily drinking found a weak but significant autoregressive data structure. Alcohol habit was related to increased mean drinking but did not strengthen the autoregressive effect of drinks per day. Higher alcohol habit was associated with higher levels of drinks per day person-specific variability. This pattern was replicated with the DSEM model for heavy drinking. Alcohol habit did not impact the autoregressive effect of heavy drinking but was associated with higher levels of heavy drinking.

CONCLUSIONS

While both drinks per day and heavy drinking showed a significant autoregressive structure, evidence of alcohol habit did not strengthen this effect. Alcohol habit did impact drinking variability; higher alcohol habit is associated with greater levels of drinking variability and higher mean drinking. Strategies to regulate drinking variability, including heavier drinking occasions, could target AUD habit.

A meta-analysis of Libet-style experiments

In the seminal Libet experiment (Libet et al., 1983), unconscious brain activity preceded the self-reported, conscious intention to move. This was repeatedly interpreted as challenging the view that (conscious) mental states cause behavior and, prominently, as challenging the existence of free will. Extensive discussions in philosophy, psychology, neuroscience, and jurisprudence followed, but further empirical findings were heterogeneous. However, a quantitative review of the literature summarizing the evidence of Libet-style experiments is lacking. The present meta-analysis fills this gap. The results revealed a temporal pattern that is largely consistent with the one found by Libet and colleagues. Remarkably, there were only k = 6 studies for the time difference between unconscious brain activity and the conscious intention to move - the most crucial time difference regarding implications about conscious causation and free will. Additionally, there was a high degree of uncertainty associated with this meta-analytic effect. We conclude that some of Libet et al.'s findings appear more fragile than anticipated in light of the substantial scientific work that built on them.

Impulsivity traits and awareness of motor intention in Parkinson's disease: a proof-of-concept study

INTRODUCTION

In patients with Parkinson's disease (PD), impulsivity is still a matter of investigation. It has been hypothesized that impulsive personality traits may favour impulse control disorder (ICD) onset during dopaminergic therapy. In healthy subjects, a relationship between the awareness of motor intention and impulsive personality traits assessed by the Barratt impulsivity scale (BIS-11) has been reported. The aim of this study was to evaluate the relationship between the awareness of voluntary action and impulsivity traits in PD.

METHODS

Twenty-eight PD patients (stages I-III on the Hoehn and Yahr scale) underwent an impulsivity trait assessment by the BIS-11 scale and a task based on the Libet's clock. Participants were requested to perform a self-initiated movement and report the time they first feel their intention to move (W-judgement) or the time of the actual movement (M-judgement).

RESULTS

In patients with higher BIS-11 scores, the time lag between the W-judgement and the actual movement was significantly lower than in patients with lower BIS-11. No difference emerged in the M-judgement.

CONCLUSION

Data suggest that also in PD patients, the impulsive personality trait is related to a "delayed" awareness of motor intention and therefore to a shorter interval to allow a conscious "veto" of the impending action. Characterization of the temporal profile of awareness of motor intention could prove useful in identifying PD patients at risk of developing ICDs during dopaminergic treatment.

Model-Based and Model-Free Control Predicts Alcohol Consumption Developmental Trajectory in Young Adults: A 3-Year Prospective Study

BACKGROUND

A shift from goal-directed toward habitual control has been associated with alcohol dependence. Whether such a shift predisposes to risky drinking is not yet clear. We investigated how goal-directed and habitual control at age 18 predict alcohol use trajectories over the course of 3 years.

METHODS

Goal-directed and habitual control, as informed by model-based (MB) and model-free (MF) learning, were assessed with a two-step sequential decision-making task during functional magnetic resonance imaging in 146 healthy 18-year-old men. Three-year alcohol use developmental trajectories were based on either a consumption score from the self-reported Alcohol Use Disorders Identification Test (assessed every 6 months) or an interview-based binge drinking score (grams of alcohol/occasion; assessed every year). We applied a latent growth curve model to examine how MB and MF control predicted the drinking trajectory.

RESULTS

Drinking behavior was best characterized by a linear trajectory. MB behavioral control was negatively associated with the development of the binge drinking score; MF reward prediction error blood oxygen level-dependent signals in the ventromedial prefrontal cortex and the ventral striatum predicted a higher starting point and steeper increase of the Alcohol Use Disorders Identification Test consumption score over time, respectively.

CONCLUSIONS

We found that MB behavioral control was associated with the binge drinking trajectory, while the MF reward prediction error signal was closely linked to the consumption score development. These findings support the idea that unbalanced MB and MF control might be an important individual vulnerability in predisposing to risky drinking behavior.

Instrumental and Pavlovian Mechanisms in Alcohol Use Disorder

Purpose of Review

Current theories of alcohol use disorders (AUD) highlight the importance of Pavlovian and instrumental learning processes mainly based on preclinical animal studies. Here, we summarize available evidence for alterations of those processes in human participants with AUD with a focus on habitual versus goal-directed instrumental learning, Pavlovian conditioning, and Pavlovian-to-instrumental transfer (PIT) paradigms.

Recent Findings

The balance between habitual and goal-directed control in AUD participants has been studied using outcome devaluation or sequential decision-making procedures, which have found some evidence of reduced goal-directed/model-based control, but little evidence for stronger habitual responding. The employed Pavlovian learning and PIT paradigms have shown considerable differences regarding experimental procedures, e.g., alcohol-related or conventional reinforcers or stimuli.

Summary

While studies of basic learning processes in human participants with AUD support a role of Pavlovian and instrumental learning mechanisms in the development and maintenance of drug addiction, current studies are characterized by large variability regarding methodology, sample characteristics, and results, and translation from animal paradigms to human research remains challenging. Longitudinal approaches with reliable and ecologically valid paradigms of Pavlovian and instrumental processes, including alcohol-related cues and outcomes, are warranted and should be combined with state-of-the-art imaging techniques, computational approaches, and ecological momentary assessment methods.

Habitual Routines and Automatic Tendencies Differential Roles in Alcohol Misuse Among Undergraduates

There is a debate over whether actions that resist devaluation (i.e., compulsive alcohol consumption) are primarily habit- or goal-directed. The incentive habit account of compulsive actions has received support from behavioral paradigms and brain imaging. In addition, the self-reported Creature of Habit Scale (COHS) has been proposed to capture inter-individual differences in habitual tendencies. It is subdivided into two dimensions: routine and automaticity. We first considered a French version of this questionnaire for validation, based on a sample of 386 undergraduates. The relationship between two dimensions of habit and the risk of substance use disorder and impulsive personality traits was also investigated. COHS has good psychometric properties with both features of habits positively associated with an Obsessive-Compulsive Inventory score. Besides, the propensity to rely more on routines was associated with lower levels of alcohol abuse and nicotine use, suggesting that some degree of routine might act as a protective factor against substance use. In contrast, a high automaticity score was associated with an increased risk of harmful alcohol use. These results demonstrate that the COHS is a valid measure of habitual tendencies and represents a useful tool for capturing inter-individual variations in drug use problems in undergraduates.

Computational theory-driven studies of reinforcement learning and decision-making in addiction: What have we learned?

Computational psychiatry provides a powerful new approach for linking the behavioral manifestations of addiction to their precise cognitive and neurobiological substrates. However, this emerging area of research is still limited in important ways. While research has identified features of reinforcement learning and decision-making in substance users that differ from health, less emphasis has been placed on capturing addiction cycles/states dynamically, within-person. In addition, the focus on few behavioral variables at a time has precluded more detailed consideration of related processes and heterogeneous clinical profiles. We propose that a longitudinal and multidimensional examination of value-based processes, a type of dynamic "computational fingerprint", will provide a more complete understanding of addiction as well as aid in developing better tailored and timed interventions.

Alcohol Hangover Does Not Alter the Application of Model-Based and Model-Free Learning Strategies

Frequent alcohol binges shift behavior from goal-directed to habitual processing modes. This shift in reward-associated learning strategies plays a key role in the development and maintenance of alcohol use disorders and seems to persist during (early stages of) sobriety in at-risk drinkers. Yet still, it has remained unclear whether this phenomenon might be associated with alcohol hangover and thus also be found in social drinkers. In an experimental crossover design, n = 25 healthy young male participants performed a two-step decision-making task once sober and once hungover (i.e., when reaching sobriety after consuming 2.6 g of alcohol per estimated liter of total body water). This task allows the separation of effortful model-based and computationally less demanding model-free learning strategies. The experimental induction of alcohol hangover was successful, but we found no significant hangover effects on model-based and model-free learning scores, the balance between model-free and model-based valuation (ω), or perseveration tendencies (π). Bayesian analyses provided positive evidence for the null hypothesis for all measures except π (anecdotal evidence for the null hypothesis). Taken together, alcohol hangover, which results from a single binge drinking episode, does not impair the application of effortful and computationally costly model-based learning strategies and/or increase model-free learning strategies. This supports the notion that the behavioral deficits observed in at-risk drinkers are most likely not caused by the immediate aftereffects of individual binge drinking events.

Addictions NeuroImaging Assessment (ANIA): Towards an integrative framework for alcohol use disorder

Alcohol misuse and addiction are major international public health issues. Addiction can be characterized as a disorder of aberrant neurocircuitry interacting with environmental, genetic and social factors. Neuroimaging in alcohol misuse can thus provide a critical window into underlying neural mechanisms, highlighting possible treatment targets and acting as clinical biomarkers for predicting risk and treatment outcomes. This neuroimaging review on alcohol misuse in humans follows the Addictions Neuroclinical Assessment (ANA) that proposes incorporating three functional neuroscience domains integral to the neurocircuitry of addiction: incentive salience and habits, negative emotional states, and executive function within the context of the addiction cycle. Here we review and integrate multiple imaging modalities focusing on underlying cognitive processes such as reward anticipation, negative emotionality, cue reactivity, impulsivity, compulsivity and executive function. We highlight limitations in the literature and propose a model forward in the use of neuroimaging as a tool to understanding underlying mechanisms and potential clinical applicability for phenotyping of heterogeneity and predicting risk and treatment outcomes.

Reduced model-based decision-making in gambling disorder

Compulsive behaviors (e.g., addiction) can be viewed as an aberrant decision process where inflexible reactions automatically evoked by stimuli (habit) take control over decision making to the detriment of a more flexible (goal-oriented) behavioral learning system. These behaviors are thought to arise from learning algorithms known as “model-based” and “model-free” reinforcement learning. Gambling disorder, a form of addiction without the confound of neurotoxic effects of drugs, showed impaired goal-directed control but the way in which problem gamblers (PG) orchestrate model-based and model-free strategies has not been evaluated. Forty-nine PG and 33 healthy participants (CP) completed a two-step sequential choice task for which model-based and model-free learning have distinct and identifiable trial-by-trial learning signatures. The influence of common psychopathological comorbidities on those two forms of learning were investigated. PG showed impaired model-based learning, particularly after unrewarded outcomes. In addition, PG exhibited faster reaction times than CP following unrewarded decisions. Troubled mood, higher impulsivity (i.e., positive and negative urgency) and current and chronic stress reported via questionnaires did not account for those results. These findings demonstrate specific reinforcement learning and decision-making deficits in behavioral addiction that advances our understanding and may be important dimensions for designing effective interventions.

Drug Cues, Conditioned Reinforcement, and Drug Seeking: The Sequelae of a Collaborative Venture With Athina Markou

Athina Markou spent a research period in my laboratory, then in the Department of Anatomy in Cambridge University, in 1991 to help us establish a cocaine-seeking procedure. Thus we embarked on developing a second-order schedule of intravenous cocaine reinforcement to investigate the neural basis of the pronounced effects of cocaine-associated conditioned stimuli on cocaine seeking. This brief review summarizes the fundamental aspects of cocaine seeking measured using this approach and the importance of the methodology in enabling us to define the neural mechanisms and circuitry underlying conditioned reinforcement and cocaine, heroin, and alcohol seeking. The shift over time and experience of control over drug seeking from a limbic cortical-ventral striatal circuit underlying goal-directed drug seeking to a dorsal striatal system mediating habitual drug seeking are also summarized. The theoretical implications of these data are discussed, thereby revealing the ways in which the outcomes of a collaboration can endure.

The Self-Report Habit Index: Assessing habitual marijuana, alcohol, e-cigarette, and cigarette use

BACKGROUND

Substance use is partially driven by habitual processes that occur automatically in response to environmental cues and may be central to users' identities. This study was designed to validate the Self-Report Habit Index (SRHI) for assessing habitual marijuana, alcohol, cigarette, and e-cigarette use.

METHODS

We examined the SRHI's psychometrics in separate samples of adult marijuana (Ns = 189;170), alcohol (Ns = 100;133), cigarette (Ns = 58;371), and e-cigarette (N = 239) users.

RESULTS

A 6-item, single-factor solution evidenced good fit across substances (CFI marijuana/alcohol/cigarettes/e-cigarettes = 0.996/0.997/0.996/0.994, RMSEA = 0.046/0.047/0.067/0.068, SRMR = 0.017/0.017/0.010/0.015) and internal consistency (α = 0.88/0.94/0.95/0.91). The SRHI was scalar invariant for sex and race. However, independent-samples t-tests indicated only that women endorsed stronger habitual e-cigarette use and that men endorsed stronger habitual marijuana use. The SRHI also was scalar invariant by product type in dual-users (cigarettes/e-cigarettes[N = 371]; alcohol/cigarettes [n = 58]), although differences in habit strength only were observed for cigarettes versus e-cigarettes, with dual-users reporting stronger habitual cigarette use. Finally, the SRHI predicted frequency of marijuana, alcohol, cigarette, and e-cigarette use (n_p² [marijuana/alcohol/cigarettes/e-cigarettes] = 0.37/0.48/0.31/0.17) and quantity of alcohol and cigarette use (n_p² = 0.43/0.33).

CONCLUSIONS

The SRHI is a psychometrically sound measure of adults' habitual substance use. The SRHI detected mean differences by sex and substance type and predicted the frequency of using each substance. Future research should determine if the SRHI is appropriate for use with other substances or age groups (e.g., adolescents), how it relates to task-based, behavioral measures of habit strength, and the degree to which habit predicts the development or maintenance of addiction.

The involuntary nature of binge drinking: goal directedness and awareness of intention

Binge drinking represents a public health issue and is a known risk factor in the development of alcohol use disorders. Previous studies have shown behavioural as well as neuroanatomical alterations associated with binge drinking. Here, we address the question of the automaticity or involuntary nature of the behaviour by assessing goal‐directed behaviour and intentionality. In this study, we used a computational two‐step task, designed to discern between model‐based/goal‐directed and model‐free/habitual behaviours, and the classic Libet clock task, to study intention awareness, in a sample of 31 severe binge drinkers (BD) and 35 matched healthy volunteers. We observed that BD had impaired goal‐directed behaviour in the two‐step task compared with healthy volunteers. In the Libet clock task, BD showed delayed intention awareness. Further, we demonstrated that alcohol use severity, as reflected by the alcohol use disorders identification test, correlated with decreased conscious awareness of volitional intention in BD, although it was unrelated to performance on the two‐step task. However, the time elapsed since the last drinking binge influenced the model‐free scores, with BD showing less habitual behaviour after longer abstinence. Our findings suggest that the implementation of goal‐directed strategies and the awareness of volitional intention are affected in current heavy alcohol users. However, the modulation of these impairments by alcohol use severity and abstinence suggests a state effect of alcohol use in these measures and that top‐down volitional control might be ameliorated with alcohol use cessation.

Model-Based Control in Dimensional Psychiatry

We use parallel interacting goal-directed and habitual strategies to make our daily decisions. The arbitration between these strategies is relevant to inflexible repetitive behaviors in psychiatric disorders. Goal-directed control, also known as model-based control, is based on an affective outcome relying on a learned internal model to prospectively make decisions. In contrast, habit control, also known as model-free control, is based on an integration of previous reinforced learning autonomous of the current outcome value and is implicit and more efficient but at the cost of greater inflexibility. The concept of model-based control can be further extended into pavlovian processes. Here we describe and compare tasks that tap into these constructs and emphasize the clinical relevance and translation of these tasks in psychiatric disorders. Together, these findings highlight a role for model-based control as a transdiagnostic impairment underlying compulsive behaviors and representing a promising therapeutic target.