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

Impact of provoked stress on model-free and model-based reinforcement learning in individuals with alcohol use disorder

Background

From both clinical and theoretical perspectives, understanding the functionality of evaluative reinforcement learning mechanisms (Model-Free, MF, and Model-Based, MB) under provoked stress, particularly in Alcohol Use Disorder (AUD), is crucial yet underexplored. This study aims to evaluate whether individuals with AUD who do not seek treatment show a greater tendency towards retrospective behaviors (MF) rather than prospective and deliberative simulations (MB) compared to controls. Additionally, it examines the impact of induced social stress on these decision-making processes.

Methods

A cohort comprising 117 participants, including 55 individuals with AUD and 62 controls, was examined. Acute social stress was induced through the socially evaluated cold pressor task (SECPT), followed by engagement in a Two-Step Markov task to assess MB and MF learning tendencies. We measured hypothalamic-pituitary-adrenal axis stress response using salivary cortisol levels.

Results

Both groups showed similar baseline cortisol levels and responses to the SECPT. Our findings indicate that participants with AUD exhibit a reduced reliance on MB strategies compared to those without AUD. Furthermore, stress decreases reliance on MB strategies in healthy participants, but this effect is not observed in those with AUD.

Conclusion

An atypical pattern of stress modulation impacting the balance between MB and MF reinforcement learning was identified in individuals with AUD who are not seeking treatment. Potential explanations for these findings and their clinical implications are explored.

Computational Mechanisms Underlying Multi-Step Planning Deficits in Methamphetamine Use Disorder

Current theories suggest individuals with methamphetamine use disorder (iMUDs) have difficulty considering long-term outcomes in decision-making, which could contribute to risk of relapse. Aversive interoceptive states (e.g., stress, withdrawal) are also known to increase this risk. The present study analyzed computational mechanisms of planning in iMUDs, and examined the potential impact of an aversive interoceptive state induction. A group of 40 iMUDs and 49 healthy participants completed two runs of a multi-step planning task, with and without an anxiogenic breathing resistance manipulation. Computational modeling revealed that iMUDs had selective difficulty identifying the best overall plan when this required enduring negative short-term outcomes - a mechanism referred to as aversive pruning. Increases in reported craving before and after the induction also predicted greater aversive pruning in iMUDs. These results highlight a novel mechanism that could promote poor choice in recovering iMUDs and create vulnerability to relapse.

Early and late contingent negative variation (CNV) reflect different aspects of deficits in schizophrenia

Abnormal reward processing and psychomotor slowing are well-known in schizophrenia (SZ). As a slow frontocentral potential, contingent negative variation (CNV) is associated with anticipatory attention, motivation and motor planning. The present study aims to evaluate the early and late amplitude and latencies of CNV in patients with SZ compared to healthy controls during a reward processing task and to show its association with clinical symptoms. We recruited 21 patients with SZ and 22 healthy controls to compare early and late CNV amplitude and latency values during a Monetary Incentive Delay (MID) Task between groups. Patients' symptom severity, levels of negative symptoms and depressive symptoms were assessed. Clinical features of the patients were further examined for their relation with CNV components. In conclusion, we found decreased early CNV amplitudes in SZ during the reward condition. They also displayed diminished and shortened late CNV responses for incentive cues, specifically at the central location. Furthermore, early CNV amplitudes exhibited a significant correlation with positive symptoms. Both CNV latencies were linked with medication dosage and the behavioural outcomes of the MID task. We revealed that early and late CNV exhibit different functions in neurophysiology and correspond to various facets of the deficits observed in patients. Our findings also emphasized that slow cortical potentials are indicative of deficient motivational processes as well as impaired reaction preparation in SZ. To gain a deeper understanding of the cognitive and motor impairments associated with psychosis, future studies must compare the effects of CNV in the early and late phases.

Recent Opioid Use Impedes Range Adaptation in Reinforcement Learning in Human Addiction

BACKGROUND

Drugs like opioids are potent reinforcers thought to co-opt value-based decisions by overshadowing other rewarding outcomes, but how this happens at a neurocomputational level remains elusive. Range adaptation is a canonical process of fine-tuning representations of value based on reward context. Here, we tested whether recent opioid exposure impacts range adaptation in opioid use disorder, potentially explaining why shifting decision making away from drug taking during this vulnerable period is so difficult.

METHODS

Participants who had recently (<90 days) used opioids (n = 34) or who had abstained from opioid use for ≥ 90 days (n = 20) and comparison control participants (n = 44) completed a reinforcement learning task designed to induce robust contextual modulation of value. Two models were used to assess the latent process that participants engaged while making their decisions: 1) a Range model that dynamically tracks context and 2) a standard Absolute model that assumes stationary, objective encoding of value.

RESULTS

Control participants and ≥90-days-abstinent participants with opioid use disorder exhibited choice patterns consistent with range-adapted valuation. In contrast, participants with recent opioid use were more prone to learn and encode value on an absolute scale. Computational modeling confirmed the behavior of most control participants and ≥90-days-abstinent participants with opioid use disorder (75%), but a minority in the recent use group (38%), was better fit by the Range model than the Absolute model. Furthermore, the degree to which participants relied on range adaptation correlated with duration of continuous abstinence and subjective craving/withdrawal.

CONCLUSIONS

Reduced context adaptation to available rewards could explain difficulty deciding about smaller (typically nondrug) rewards in the aftermath of drug exposure.

Dysfunctional feedback processing in male methamphetamine abusers: Evidence from neurophysiological and computational approaches

Methamphetamine use disorder (MUD) as a major public health risk is associated with dysfunctional neural feedback processing. Although dysfunctional feedback processing in people who are substance dependent has been explored in several behavioral, computational, and electrocortical studies, this mechanism in MUDs requires to be well understood. Furthermore, the current understanding of latent components of their behavior such as learning speed and exploration-exploitation dilemma is still limited. In addition, the association between the latent cognitive components and the related neural mechanisms also needs to be explored. Therefore, in this study, the underlying neurocognitive mechanisms of feedback processing of such impairment, and age/gender-matched healthy controls are evaluated within a probabilistic learning task with rewards and punishments. Mathematical modeling results based on the Q-learning paradigm suggested that MUDs show less sensitivity in distinguishing optimal options. Additionally, it may be worth noting that MUDs exhibited a slight decrease in their ability to learn from negative feedback compared to healthy controls. Also through the lens of underlying neural mechanisms, MUDs showed lower theta power at the medial-frontal areas while responding to negative feedback. However, other EEG measures of reinforcement learning including feedback-related negativity, parietal-P300, and activity flow from the medial frontal to lateral prefrontal regions, remained intact in MUDs. On the other hand, the elimination of the linkage between value sensitivity and medial-frontal theta activity in MUDs was observed. The observed dysfunction could be due to the adverse effects of methamphetamine on the cortico-striatal dopamine circuit, which is reflected in the anterior cingulate cortex activity as the most likely region responsible for efficient behavior adjustment. These findings could help us to pave the way toward tailored therapeutic approaches.

A primer on the use of computational modelling to investigate affective states, affective disorders and animal welfare in non-human animals

Objective measures of animal emotion-like and mood-like states are essential for preclinical studies of affective disorders and for assessing the welfare of laboratory and other animals. However, the development and validation of measures of these affective states poses a challenge partly because the relationships between affect and its behavioural, physiological and cognitive signatures are complex. Here, we suggest that the crisp characterisations offered by computational modelling of the underlying, but unobservable, processes that mediate these signatures should provide better insights. Although this computational psychiatry approach has been widely used in human research in both health and disease, translational computational psychiatry studies remain few and far between. We explain how building computational models with data from animal studies could play a pivotal role in furthering our understanding of the aetiology of affective disorders, associated affective states and the likely underlying cognitive processes involved. We end by outlining the basic steps involved in a simple computational analysis.

Benchmarking Mental Health Status Using Passive Sensor Data: Protocol for a Prospective Observational Study

BACKGROUND

Computational psychiatry has the potential to advance the diagnosis, mechanistic understanding, and treatment of mental health conditions. Promising results from clinical samples have led to calls to extend these methods to mental health risk assessment in the general public; however, data typically used with clinical samples are neither available nor scalable for research in the general population. Digital phenotyping addresses this by capitalizing on the multimodal and widely available data created by sensors embedded in personal digital devices (eg, smartphones) and is a promising approach to extending computational psychiatry methods to improve mental health risk assessment in the general population.

OBJECTIVE

Building on recommendations from existing computational psychiatry and digital phenotyping work, we aim to create the first computational psychiatry data set that is tailored to studying mental health risk in the general population; includes multimodal, sensor-based behavioral features; and is designed to be widely shared across academia, industry, and government using gold standard methods for privacy, confidentiality, and data integrity.

METHODS

We are using a stratified, random sampling design with 2 crossed factors (difficulties with emotion regulation and perceived life stress) to recruit a sample of 400 community-dwelling adults balanced across high- and low-risk for episodic mental health conditions. Participants first complete self-report questionnaires assessing current and lifetime psychiatric and medical diagnoses and treatment, and current psychosocial functioning. Participants then complete a 7-day in situ data collection phase that includes providing daily audio recordings, passive sensor data collected from smartphones, self-reports of daily mood and significant events, and a verbal description of the significant daily events during a nightly phone call. Participants complete the same baseline questionnaires 6 and 12 months after this phase. Self-report questionnaires will be scored using standard methods. Raw audio and passive sensor data will be processed to create a suite of daily summary features (eg, time spent at home).

RESULTS

Data collection began in June 2022 and is expected to conclude by July 2024. To date, 310 participants have consented to the study; 149 have completed the baseline questionnaire and 7-day intensive data collection phase; and 61 and 31 have completed the 6- and 12-month follow-up questionnaires, respectively. Once completed, the proposed data set will be made available to academic researchers, industry, and the government using a stepped approach to maximize data privacy.

CONCLUSIONS

This data set is designed as a complementary approach to current computational psychiatry and digital phenotyping research, with the goal of advancing mental health risk assessment within the general population. This data set aims to support the field's move away from siloed research laboratories collecting proprietary data and toward interdisciplinary collaborations that incorporate clinical, technical, and quantitative expertise at all stages of the research process.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/53857.

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.

The utility of a latent-cause framework for understanding addiction phenomena

Computational models of addiction often rely on a model-free reinforcement learning (RL) formulation, owing to the close associations between model-free RL, habitual behavior and the dopaminergic system. However, such formulations typically do not capture key recurrent features of addiction phenomena such as craving and relapse. Moreover, they cannot account for goal-directed aspects of addiction that necessitate contrasting, model-based formulations. Here we synthesize a growing body of evidence and propose that a latent-cause framework can help unify our understanding of several recurrent phenomena in addiction, by viewing them as the inferred return of previous, persistent "latent causes". We demonstrate that applying this framework to Pavlovian and instrumental settings can help account for defining features of craving and relapse such as outcome-specificity, generalization, and cyclical dynamics. Finally, we argue that this framework can bridge model-free and model-based formulations, and account for individual variability in phenomenology by accommodating the memories, beliefs, and goals of those living with addiction, motivating a centering of the individual, subjective experience of addiction and recovery.

Hybrid computational model depicts the contribution of non-significant lobes of human brain during the perception of emotional stimuli

Emotions are synchronizing responses of human brain while executing cognitive tasks. Earlier studies had revealed strong correlation between specific lobes of the brain to different types of emotional valence. In the current study, a comprehensive three-dimensional mapping of human brain for executing emotion specific tasks had been formulated. A hybrid computational machine learning model customized from Custom Weight Allocation Model (CWAM) and defined as Custom Rank Allocation Model (CRAM). This regression-based hybrid computational model computes the allocated tasks to different lobes of the brain during their respective executive stage. Event Related Potentials (ERP) were obtained with significant effect at P1, P2, P3, N170, N2, and N4. These ERPs were configured at Pz, Cz, F3, and T8 regions of the brain with maximal responses; while regions like Cz, C4 and F4 were also found to make effective contributions to elevate the responses of the brain, and thus these regions were configured as augmented source regions of the brain. In another circumstance of frequent -deviant - equal (FDE) presentation of the emotional stimuli, it was observed that the brain channels C3, C4, P3, P4, O1, O2, and Oz were contributing their emotional quotient to the overall response of the brain regions; whereas, the interaction effect was found presentable at O2, Oz, P3, P4, T8 and C3 regions of brain. The proposed computational model had identified the potential neural pathways during the execution of emotional task.

Exploring the steps of learning: computational modeling of initiatory-actions among individuals with attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is characterized by difficulty in acting in a goal-directed manner. While most environments require a sequence of actions for goal attainment, ADHD was never studied in the context of value-based sequence learning. Here, we made use of current advancements in hierarchical reinforcement-learning algorithms to track the internal value and choice policy of individuals with ADHD performing a three-stage sequence learning task. Specifically, 54 participants (28 ADHD, 26 controls) completed a value-based reinforcement-learning task that allowed us to estimate internal action values for each trial and stage using computational modeling. We found attenuated sensitivity to action values in ADHD compared to controls, both in choice and reaction-time variability estimates. Remarkably, this was found only for first-stage actions (i.e., initiatory actions), while for actions performed just before outcome delivery the two groups were strikingly indistinguishable. These results suggest a difficulty in following value estimation for initiatory actions in ADHD.

An opponent striatal circuit for distributional reinforcement learning

Machine learning research has achieved large performance gains on a wide range of tasks by expanding the learning target from mean rewards to entire probability distributions of rewards - an approach known as distributional reinforcement learning (RL)1. The mesolimbic dopamine system is thought to underlie RL in the mammalian brain by updating a representation of mean value in the striatum2,3, but little is known about whether, where, and how neurons in this circuit encode information about higher-order moments of reward distributions4. To fill this gap, we used high-density probes (Neuropixels) to acutely record striatal activity from well-trained, water-restricted mice performing a classical conditioning task in which reward mean, reward variance, and stimulus identity were independently manipulated. In contrast to traditional RL accounts, we found robust evidence for abstract encoding of variance in the striatum. Remarkably, chronic ablation of dopamine inputs disorganized these distributional representations in the striatum without interfering with mean value coding. Two-photon calcium imaging and optogenetics revealed that the two major classes of striatal medium spiny neurons - D1 and D2 MSNs - contributed to this code by preferentially encoding the right and left tails of the reward distribution, respectively. We synthesize these findings into a new model of the striatum and mesolimbic dopamine that harnesses the opponency between D1 and D2 MSNs5-15 to reap the computational benefits of distributional RL.

Model-based and model-free mechanisms in methamphetamine use disorder

People with methamphetamine use disorder (MUD) struggle to shift their behaviour from methamphetamine-orientated habits to goal-oriented choices. The model-based/model-free framework is well suited to understand this difficulty by unpacking the computational mechanisms that support experienced-based (model-free) and goal-directed (model-based) choices. We aimed to examine whether 1) participants with MUD differed from controls on behavioural proxies and/or computational mechanisms of model-based/model-free choices; 2) model-based/model-free decision-making correlated with MUD symptoms; and 3) model-based/model-free deficits improved over six weeks in the group with MUD. Participants with MUD and controls with similar age, IQ and socioeconomic status completed the Two-Step Task at treatment commencement (MUD n = 30, Controls n = 31) and six weeks later (MUD n = 23, Controls n = 26). We examined behavioural proxies of model-based/model-free decisions using mixed logistic regression, and their underlying mechanisms using computational modelling. At a behavioural level, participants with MUD were more likely to switch their choices following rewarded actions, although this pattern improved at follow up. At a computational level, groups were similar in their use of model-based mechanisms, but participants with MUD were less likely to apply model-free mechanisms and less likely to repeat rewarded actions. We did not find evidence that individual differences in model-based or model-free parameters were associated with greater severity of methamphetamine dependence, nor did we find that group differences in computational parameters changed between baseline and follow-up assessment. Decision-making challenges in people with MUD are likely related to difficulties in pursuing choices previously associated with positive outcomes.

Neural mechanisms of intertemporal and risky decision-making in individuals with internet use disorder: A perspective from directed functional connectivity

Background and aims

The intertemporal and risk decision-making impairments are vital cognitive mechanisms in internet use disorder (IUD). However, the underlying neural mechanisms for these two decision-making dysfunctions in individuals with IUD remain unclear.

Methods

This study employed Functional Near-Infrared Spectroscopy (fNIRS) to record changes in blood oxygen concentration in the prefrontal cortex of individuals with IUD during intertemporal and risk decision-making tasks.

Results

The findings revealed that the intertemporal decision-making deficits in IUD group were primarily associated with reduced activation in the left dorsolateral prefrontal cortex (dlPFC) and orbitofrontal cortex (OFC) and FC from the left dlPFC to the right dlPFC. On the other hand, risk decision-making impairments were linked to decreased OFC activation and weakened functional connectivity from the left dlPFC to the right dlPFC and OFC.

Discussions and Conslusions

These results suggested that while there were common neural mechanisms underlying intertemporal and risk decision-making impairments in individuals with IUD, specific neural foundations existed for each type of dysfunction.

Reduced neural encoding of utility prediction errors in cocaine addiction

Influential accounts of addiction posit alterations in adaptive behavior driven by deficient dopaminergic prediction errors (PEs), signaling the discrepancy between actual and expected reward. Dopamine neurons encode these error signals in subjective terms, calibrated by individual risk preferences, as "utility" PEs. It remains unclear, however, whether people with drug addiction have PE deficits or their computational source. Here, using an analogous task to prior single-unit studies with known expectancies, we show that fMRI-measured PEs similarly reflect utility PEs. Relative to control participants, people with chronic cocaine addiction demonstrate reduced utility PEs in the dopaminoceptive ventral striatum, with similar trends in orbitofrontal cortex. Dissecting this PE signal into its subcomponent terms attributed these reductions to weaker striatal responses to received reward/utility, whereas suppression of activity with reward expectation was unchanged. These findings support that addiction may fundamentally disrupt PE signaling and reveal an underappreciated role for perceived reward value in this mechanism.

Adaptive Design Optimization as a Promising Tool for Reliable and Efficient Computational Fingerprinting

A key challenge in understanding mental (dys)functions is their etiological and functional heterogeneity, and several multidimensional assessments have been proposed for their comprehensive characterization. However, such assessments require lengthy testing, which may hinder reliable and efficient characterization of individual differences due to increased fatigue and distraction, especially in clinical populations. Computational modeling may address this challenge as it often provides more reliable measures of latent neurocognitive processes underlying observed behaviors and captures individual differences better than traditional assessments. However, even with a state-of-the-art hierarchical modeling approach, reliable estimation of model parameters still requires a large number of trials. Recent work suggests that Bayesian adaptive design optimization (ADO) is a promising way to address these challenges. With ADO, experimental design is optimized adaptively from trial to trial to extract the maximum amount of information about an individual's characteristics. In this review, we first describe the ADO methodology and then summarize recent work demonstrating that ADO increases the reliability and efficiency of latent neurocognitive measures. We conclude by discussing the challenges and future directions of ADO and proposing development of ADO-based computational fingerprints to reliably and efficiently characterize the heterogeneous profiles of psychiatric disorders.

From Computation to Clinic

Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from clinic to computation, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from computation to clinic, remains rare. In addition, consensus regarding specific barriers to forward translation-and on the best strategies to overcome these barriers-is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to 1) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect, and 2) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost-effectiveness analyses).

Reduced sensitivity but intact motivation to monetary rewards and reversal learning in obesity

BACKGROUND

Obesity has been linked to altered reward processing but little is known about which components of reward processing including motivation, sensitivity and learning are impaired in obesity. We examined whether obesity compared to healthy weight controls is associated with differences in distinct subdomains of reward processing. To this end, we used two established paradigms, namely the Effort Expenditure for Rewards task (EEfRT) and the Probabilistic Reversal Learning Task (PRLT).

METHODS

30 individuals with obesity (OBS) and 30 healthy weight control subjects (HC) were included in the study. Generalized estimating equation models were used to analyze EEfRT choice behavior. PRLT data was analyzed using both conventional behavioral variables of choices and computational models.

RESULTS

Our findings from the different tasks speak in favor of a hyposensitivity to non-food rewards in obesity. OBS did not make fewer overall hard task selections compared to HC in the EEfRT suggesting generally intact non-food reward motivation. However, in highly rewarding trials (i.e.,trials with high reward magnitude and high reward probability),OBSmadefewer hard task selections compared to normal weight subjects suggesting decreased sensitivity to highly rewarding non-food reinforcers. Hyposensitivity to non-food rewards was also evident in OBS in the PRLT as evidenced by lower win-stay probability compared to HC. Our computational modelling analyses revealed decreased stochasticity but intact reward and punishment learning rates in OBS.

CONCLUSIONS

Our findings provide evidence for intact reward motivation and learning in OBS but lower reward sensitivity which is linked to stochasticity of choices in a non-food context. These findings might provide further insight into the mechanism underlying dysfunctional choices in obesity.

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.

Individual differences in computational psychiatry: a review of current challenges

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice.

Cardiac sensitivity to rewards in cognitively inflexible nonclinical participants

Background

In psychopathologies characterized by compulsive decision-making, core impairments include cognitive inflexibility and excessive sensitivity to rewards. It has been posited that traits shared by nonclinical individuals and psychiatric patients could help explain the pathogenesis of compulsive decision-making.

Methods

To investigate whether cognitive inflexibility predisposes nonclinical individuals to poor choices and hyper-reactivity to reward, we recruited people with high and low scores for cognitive persistence and used the Iowa Gambling Task to assess decision-making and cardiac reactivity to monetary gains/losses.

Results

As is frequently observed in psychophysiological research, the data indicated discrepancies among self-reports, behavior, and physiology. Cognitive inflexibility was not related to worse performance; however, monetary gains, in line with the literature, prompted marked cardiac accelerations. Consistent with our research goal, only inflexible participants showed large cardiac accelerations during the largest monetary wins.

Discussion

Taken together, the data confirm an association between cognitive persistence and physiological reward sensitivity in a nonclinical population. The findings are in line with recent theories on the development of compulsive behaviors that consider cognitive inflexibility as a transdiagnostic impairment and predisposing factor for excessive reactivity to rewards, and might act both as a preexisting individual trait and drug-induced deficit.

Computational reinforcement learning, reward (and punishment), and dopamine in psychiatric disorders

In the DSM-5, psychiatric diagnoses are made based on self-reported symptoms and clinician-identified signs. Though helpful in choosing potential interventions based on the available regimens, this conceptualization of psychiatric diseases can limit basic science investigation into their underlying causes. The reward prediction error (RPE) hypothesis of dopamine neuron function posits that phasic dopamine signals encode the difference between the rewards a person expects and experiences. The computational framework from which this hypothesis was derived, temporal difference reinforcement learning (TDRL), is largely focused on reward processing rather than punishment learning. Many psychiatric disorders are characterized by aberrant behaviors, expectations, reward processing, and hypothesized dopaminergic signaling, but also characterized by suffering and the inability to change one's behavior despite negative consequences. In this review, we provide an overview of the RPE theory of phasic dopamine neuron activity and review the gains that have been made through the use of computational reinforcement learning theory as a framework for understanding changes in reward processing. The relative dearth of explicit accounts of punishment learning in computational reinforcement learning theory and its application in neuroscience is highlighted as a significant gap in current computational psychiatric research. Four disorders comprise the main focus of this review: two disorders of traditionally hypothesized hyperdopaminergic function, addiction and schizophrenia, followed by two disorders of traditionally hypothesized hypodopaminergic function, depression and post-traumatic stress disorder (PTSD). Insights gained from a reward processing based reinforcement learning framework about underlying dopaminergic mechanisms and the role of punishment learning (when available) are explored in each disorder. Concluding remarks focus on the future directions required to characterize neuropsychiatric disorders with a hypothesized cause of underlying dopaminergic transmission.

Altered striatal-opercular intrinsic connectivity reflects decreased aversion to losses in alcohol use disorder

The persistence of addictive behaviours despite their adverse consequences highlights decreased punishment sensitivity as a facet of decision-making impairments in Alcohol Use Disorder (AUD). This attitude departs from the typical loss aversion (LA) pattern, i.e. the stronger sensitivity to negative than positive outcomes, previously associated with striatal and limbic-somatosensory responsiveness in healthy individuals. Consistent evidence highlights decreased LA as a marker of disease severity in AUD, but its neural bases remain largely unexplored. AUD-specific modulations of frontolateral activity by LA were previously related to the higher executive demands of anticipating losses than gains, but the relationship between LA and executive/working-memory performance in AUD is debated. Building on previous evidence of overlapping neural bases of LA during decision-making and at rest, we investigated a possible neural signature of altered LA in AUDs, and its connections with executive skills, in terms of complementary facets of resting-state functioning. In patients, smaller LA than controls, unrelated to executive performance, reflected reduced connectivity within striatal and medial temporal networks, and altered connectivity from these regions to the insular-opercular cortex. AUD-specific loss-related modulations of intrinsic connectivity thus involved structures previously associated both with drug-seeking and with coding the trade-off between appetitive and aversive motivational drives. These findings fit the hypothesis that altered striatal coding of choice-related incentive value, and interoceptive responsiveness to prospective outcomes, enhance neural sensitivity to drug-related stimuli in addictions. LA and its neural bases might prove useful markers of AUD severity and effectiveness of rehabilitation strategies targeting the salience of negative choice outcomes.

Computational models of exploration and exploitation characterise onset and efficacy of treatment in methamphetamine use disorder

People with Methamphetamine Use Disorder (PwMUD) spend substantial time and resources on substance use, which hinders their ability to explore alternate reinforcers. Gold‐standard behavioural treatments attempt to remedy this by encouraging action towards non‐drug reinforcers, but substance use often persists. We aimed to unravel the mechanistic drivers of this behaviour by applying a computational model of explore/exploit behaviour to decision‐making data (Iowa Gambling Task) from 106 PwMUD and 48 controls. We then examined the longitudinal link between explore/exploit mechanisms and changes in methamphetamine use 6 weeks later. Exploitation parameters included reinforcement sensitivity and inverse decay (i.e., number of past outcomes used to guide choices). Exploration parameters included maximum directed exploration value (i.e., value of trying novel actions). The Timeline Follow Back measured changes in methamphetamine use. Compared to controls, PwMUD showed deficits in exploitative decision‐making, characterised by reduced reinforcement sensitivity, U = 3065, p = 0.009, and less use of previous choice outcomes, U = 3062, p = 0.010. This was accompanied by a behavioural pattern of frequent shifting between choices, which appeared consistent with random exploration. Furthermore, PwMUD with greater reductions of methamphetamine use at 6 weeks had increased directed exploration (β = 0.22, p = 0.045); greater use of past choice outcomes (β = −0.39, p = 0.002) and greater choice consistency (β = −0.39, p = 0.002). Therefore, limited computational exploitation and increased behavioural exploration characterise PwMUD's presentation to treatment, while increased directed exploration, use of past choice outcomes and choice consistency predict greater reductions of methamphetamine use.

A review of mathematical modeling of addiction regarding both (neuro-) psychological processes and the social contagion perspectives

Addiction is a complex biopsychosocial phenomenon, impacted by biological predispositions, psychological processes, and the social environment. Using mathematical and computational models that allow for surrogative reasoning may be a promising avenue for gaining a deeper understanding of this complex behavior. This paper reviews and classifies a selection of formal models of addiction focusing on the intra- and inter-individual dynamics, i.e., (neuro) psychological models and social models. We find that these modeling approaches to addiction are too disjoint and argue that in order to unravel the complexities of biopsychosocial processes of addiction, models should integrate intra- and inter-individual factors.

Reinforcement learning detuned in addiction: integrative and translational approaches

Suboptimal decision-making strategies have been proposed to contribute to the pathophysiology of addiction. Decision-making, however, arises from a collection of computational components that can independently influence behavior. Disruptions in these different components can lead to decision-making deficits that appear similar behaviorally, but differ at the computational, and likely the neurobiological, level. Here, we discuss recent studies that have used computational approaches to investigate the decision-making processes underlying addiction. Studies in animal models have found that value updating following positive, but not negative, outcomes is predictive of drug use, whereas value updating following negative, but not positive, outcomes is disrupted following drug self-administration. We contextualize these findings with studies on the circuit and biological mechanisms of decision-making to develop a framework for revealing the biobehavioral mechanisms of addiction.

Computational Psychiatry Needs Time and Context

Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry.

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

BACKGROUND

Alcohol 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.

METHODS

Twenty-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.

RESULTS

AUDP 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.

CONCLUSION

Our 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.

The neurobiology of drug addiction: cross-species insights into the dysfunction and recovery of the prefrontal cortex

A growing preclinical and clinical body of work on the effects of chronic drug use and drug addiction has extended the scope of inquiry from the putative reward-related subcortical mechanisms to higher-order executive functions as regulated by the prefrontal cortex. Here we review the neuroimaging evidence in humans and non-human primates to demonstrate the involvement of the prefrontal cortex in emotional, cognitive, and behavioral alterations in drug addiction, with particular attention to the impaired response inhibition and salience attribution (iRISA) framework. In support of iRISA, functional and structural neuroimaging studies document a role for the prefrontal cortex in assigning excessive salience to drug over non-drug-related processes with concomitant lapses in self-control, and deficits in reward-related decision-making and insight into illness. Importantly, converging insights from human and non-human primate studies suggest a causal relationship between drug addiction and prefrontal insult, indicating that chronic drug use causes the prefrontal cortex damage that underlies iRISA while changes with abstinence and recovery with treatment suggest plasticity of these same brain regions and functions. We further dissect the overlapping and distinct characteristics of drug classes, potential biomarkers that inform vulnerability and resilience, and advancements in cutting-edge psychological and neuromodulatory treatment strategies, providing a comprehensive landscape of the human and non-human primate drug addiction literature as it relates to the prefrontal cortex.

A Proof-of-Concept Ecological Momentary Assessment Study of Day-Level Dynamics in Value-Based Decision-Making in Opioid Addiction

BACKGROUND

Drug addiction is thought to be characterized by risky and impulsive behavior despite harmful consequences. Whether these aspects of value-based decision-making in people with addiction are stable and trait-like, and the degree to which they vary within-person and are sensitive to changes in psychological state, remains unknown. In this pilot study, we examined the feasibility of distinguishing these state- vs. trait-like components by probing day-level dynamics of risk and time preferences in patients with opioid use disorder (OUD) as they engaged with their natural environment.

METHODS

Twenty-three individuals with OUD receiving outpatient treatment (40% female; M = 45.67 [SD = 13.16] years of age) and twenty-one matched healthy community controls (47% female; M = 49.67 [SD = 14.38] years of age) participated in a 28-day smartphone-based ecological momentary assessment study (1085 person days; M = 24.66, SD = 5.84). Random prompts administered daily assessed subjects' psychological state (e.g., mood) and economic preferences for real delayed and risky monetary rewards.

RESULTS

Subjects demonstrated dynamic decision-making preferences, with 40-53% of the variation in known risk and ambiguity tolerance, and 67% in discounting, attributable to between-person vs. within-person (day-to-day) differences. We found that changes in psychological state were related to changes in risk preferences, with patients preferring riskier offers on days they reported being in a better mood but no differences between groups in aggregate level behavior. By contrast, temporal discounting was increased overall in patients compared to controls and was unrelated to global mood. The study was well-tolerated, but compliance rates were moderate and lower in patients.

CONCLUSION

Our data support the idea that decision-making preferences in drug addiction exhibit substantial within-person variability and that this variability can be well-captured using remote data collection methods. Preliminary findings suggested that aspects of decision-making related to consideration of risk may be more sensitive to within-person change in global psychological state while those related to consideration of delay to reward, despite also being somewhat variable, stably differ from healthy levels. Identifying the cognitive factors that contribute to opioid use risk in a "real-world" setting may be important for identifying unique, time-sensitive targets for intervention.