The computational form of craving is a selective multiplication of economic value.

Craving is thought to be a specific desire state that biases choice toward the desired object, be it chocolate or drugs. A vast majority of people report having experienced craving of some kind. In its pathological form craving contributes to health outcomes in addiction and obesity. Yet despite its ubiquity and clinical relevance we still lack a basic neurocomputational understanding of craving. Here, using an instantaneous measure of subjective valuation and selective cue exposure, we identify a behavioral signature of a food craving-like state and advance a computational framework for understanding how this state might transform valuation to bias choice. We find desire induced by exposure to a specific high-calorie, high-fat/sugar snack good is expressed in subjects' momentary willingness to pay for this good. This effect is selective but not exclusive to the exposed good; rather, we find it generalizes to nonexposed goods in proportion to their subjective attribute similarity to the exposed ones. A second manipulation of reward size (number of snack units available for purchase) further suggested that a multiplicative gain mechanism supports the transformation of valuation during laboratory craving. These findings help explain how real-world food craving can result in behaviors inconsistent with preferences expressed in the absence of craving and open a path for the computational modeling of craving-like phenomena using a simple and repeatable experimental tool for assessing subjective states in economic terms.

Impulsivity and Medical Care Utilization in Veterans Treated for Substance Use Disorder.

BACKGROUND: Impulsivity has been defined by acting rashly during positive mood states (positive urgency; PU) or negative mood states (negative urgency; NU) and by excessive de-valuation of deferred rewards. These behaviors reflect a "live in the now" mentality that is not only characteristic of many individuals with severe substance use disorder (SUD) but also impedes medical treatment compliance and could result in repeated hospitalizations or other poor health outcomes. Purpose/objectives: We sought preliminary evidence that impulsivity may relate to adverse health outcomes in the veteran population. Impulsivity measured in 90 veterans receiving inpatient or outpatient SUD care at a Veterans Affairs Medical Center was related to histories of inpatient/residential care costs, based on VA Health Economics Resource Center data. Results: We found that positive urgency, lack of persistence and lack of premeditation, but not sensation-seeking or preference for immediate or risky rewards, were significantly higher in veterans with a history of one or more admissions for VA-based inpatient or residential health care that either included (n = 30) or did not include (n = 29) an admission for SUD care. Among veterans with a history of inpatient/residential care for SUD, NU and PU, but not decision-making behavior, correlated with SUD care-related costs. Conclusions/Importance: In veterans receiving SUD care, questionnaire-assessed trait impulsivity (but not decision-making) related to greater care utilization within the VA system. This suggests that veterans with high impulsivity are at greater risk for adverse health outcomes, such that expansion of cognitive interventions to reduce impulsivity may improve their health.

A distinct inferential mechanism for delusions in schizophrenia.

Delusions, a core symptom of psychosis, are false beliefs that are rigidly held with strong conviction despite contradictory evidence. Alterations in inferential processes have long been proposed to underlie delusional pathology, but previous attempts to show this have failed to yield compelling evidence for a specific relationship between inferential abnormalities and delusional severity in schizophrenia. Using a novel, incentivized information-sampling task (a modified version of the beads task), alongside well-characterized decision-making tasks, we sought a mechanistic understanding of delusions in a sample of medicated and unmedicated patients with schizophrenia who exhibited a wide range of delusion severity. In this novel task, participants chose whether to draw beads from one of two hidden jars or to guess the identity of the hidden jar, in order to minimize financial loss from a monetary endowment, and concurrently reported their probability estimates for the hidden jar. We found that patients with higher delusion severity exhibited increased information seeking (i.e. increased draws-to-decision behaviour). This increase was highly specific to delusion severity as compared to the severity of other psychotic symptoms, working-memory capacity, and other clinical and socio-demographic characteristics. Delusion-related increases in information seeking were present in unmedicated patients, indicating that they were unlikely due to antipsychotic medication. In addition, after adjusting for delusion severity, patients as a whole exhibited decreased information seeking relative to healthy individuals, a decrease that correlated with lower socioeconomic status. Computational analyses of reported probability estimates further showed that more delusional patients exhibited abnormal belief updating characterized by stronger reliance on prior beliefs formed early in the inferential process, a feature that correlated with increased information seeking in patients. Other decision-making parameters that could have theoretically explained the delusion effects, such as those related to subjective valuation, were uncorrelated with both delusional severity and information seeking among the patients. In turn, we found some preliminary evidence that subjective valuation (rather than belief updating) may explain group differences in information seeking unrelated to delusions. Together, these results suggest that abnormalities in belief updating, characterized by stronger reliance on prior beliefs formed by incorporating information presented earlier in the inferential process, may be a core computational mechanism of delusional ideation in psychosis. Our results thus provide direct empirical support for an inferential mechanism that naturally captures the characteristic rigidity associated with delusional beliefs.

Neural mechanisms of extinguishing drug and pleasant cue associations in human addiction: role of the VMPFC.

The neurobiological mechanisms that underlie the resistance of drug cue associations to extinction in addiction remain unknown. Fear extinction critically depends on the ventromedial prefrontal cortex (VMPFC). Here, we tested if this same region plays a role in extinction of non-fear, drug and pleasant cue associations. Eighteen chronic cocaine users and 15 matched controls completed three functional MRI scans. Participants first learned to associate an abstract cue (the conditioned stimulus, CS) with a drug-related (CSD+ ) or pleasant (CSP+ ) image. Extinction immediately followed where each CS was repeatedly presented without the corresponding image. Participants underwent a second identical session 24 hours later to assess retention of extinction learning. Results showed that like fear extinction, non-fear-based extinction relies on the VMPFC. However, extinction-related changes in the VMPFC differed by cue valence and diagnosis. In controls, VMPFC activation to the CSD+ (which was unpleasant for participants) gradually increased as in fear extinction, while it decreased to the CSP+ , consistent with a more general role of the VMPFC in flexible value updating. Supporting a specific role in extinction retention, we further observed a cross-day association between VMPFC activation and skin conductance, a classic index of conditioned responses. Finally, cocaine users showed VMPFC abnormalities for both CSs, which, in the case of the CSD+ , correlated with craving. These data suggest a global deficit in extinction learning in this group that may hinder extinction-based treatment efforts. More broadly, these data show that the VMPFC, when functionally intact, supports extinction learning in diverse contexts in humans.

Impaired neural response to negative prediction errors in cocaine addiction.

Learning can be guided by unexpected success or failure, signaled via dopaminergic positive reward prediction error (+RPE) and negative reward-prediction error (-RPE) signals, respectively. Despite conflicting empirical evidence, RPE signaling is thought to be impaired in drug addiction. To resolve this outstanding question, we studied as a measure of RPE the feedback negativity (FN) that is sensitive to both reward and the violation of expectation. We examined FN in 25 healthy controls; 25 individuals with cocaine-use disorder (CUD) who tested positive for cocaine on the study day (CUD+), indicating cocaine use within the past 72 h; and in 25 individuals with CUD who tested negative for cocaine (CUD-). EEG was acquired while the participants performed a gambling task predicting whether they would win or lose money on each trial given three known win probabilities (25, 50, or 75%). FN was scored for the period in each trial when the actual outcome (win or loss) was revealed. A significant interaction between prediction, outcome, and group revealed that controls showed increased FN to unpredicted compared with predicted wins (i.e., intact +RPE) and decreased FN to unpredicted compared with predicted losses (i.e., intact -RPE). However, neither CUD subgroup showed FN modulation to loss (i.e., impaired -RPE), and unlike CUD+ individuals, CUD- individuals also did not show FN modulation to win (i.e., impaired +RPE). Thus, using FN, the current study directly documents -RPE deficits in CUD individuals. The mechanisms underlying -RPE signaling impairments in addiction may contribute to the disadvantageous nature of excessive drug use, which can persist despite repeated unfavorable life experiences (e.g., frequent incarcerations).

Gene x abstinence effects on drug cue reactivity in addiction: multimodal evidence.

Functional polymorphisms in the dopamine transporter gene (DAT1 or SLC6A3) modulate responsiveness to salient stimuli, such that carriers of one 9R-allele of DAT1 (compared with homozygote carriers of the 10R-allele) show heightened reactivity to drug-related reinforcement in addiction. Here, using multimodal neuroimaging and behavioral dependent variables in 73 human cocaine-addicted individuals and 47 healthy controls, we hypothesized and found that cocaine-addicted carriers of a 9R-allele exhibited higher responses to drug cues, but only among individuals who had used cocaine within 72 h of the study as verified by positive cocaine urine screens (a state characterized by intense craving). Importantly, this responsiveness to drug cues was reliably preserved across multimodal imaging and behavioral probes: psychophysiological event-related potentials, self-report, simulated cocaine choice, and fMRI. Because drug cues contribute to relapse, our results identify the DAT1R 9R-allele as a vulnerability allele for relapse especially during early abstinence (e.g., detoxification).

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.

The role of social connection in opioid use disorder treatment engagement.

OBJECTIVE: Medications for opioid use disorder (OUD or MOUD) treatment combining pharmacotherapy with psychosocial support are effective for managing OUD. However, treatment engagement remains a challenge, with retention rates ∼30%-50%. Although social connection has been identified as important to recovery, it remains unclear whether and how social factors can bolster participation in treatment. METHOD: Individuals receiving MOUD at three outpatient treatment programs (N = 82) and healthy community controls (N = 62) completed validated measures assessing social connection including (a) size, diversity, and embeddedness of social networks; (b) perceived social support and criticism within familial relationships; and (c) subjective social status. For those receiving MOUD, we also examined how aspects of social connection related to opioid (re)use and treatment engagement (medication adherence, group, and individual meeting attendance) assessed over ∼8 weeks/person. RESULTS: Compared to controls, individuals receiving MOUD had smaller and less diverse and embedded social networks (Cohen's d > 0.4), and despite similar levels of perceived social support (d = 0.02), reported higher levels of social criticism (d = 0.6) and lower subjective social status (d = 0.5). Within the MOUD group, higher social network indices correlated specifically with higher therapeutic group attendance (Rs > 0.30), but not medication adherence, while higher levels of perceived criticism correlated with more frequent opioid use (R = 0.23). Results were mostly robust to control for sociodemographic variables, psychological distress/COVID-19, and treatment duration, but differed by MOUD type/program. CONCLUSIONS: These findings highlight the potential importance of assessing an individual's social capital, promoting positive social connection, and continuing to assess the implementation and value of psychosocial support in MOUD treatment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

Major depression symptom severity associations with willingness to exert effort and patch foraging strategy.

Individuals with major depressive disorder (MDD) can experience reduced motivation and cognitive function, leading to challenges with goal-directed behavior. When selecting goals, people maximize 'expected value' by selecting actions that maximize potential reward while minimizing associated costs, including effort 'costs' and the opportunity cost of time. In MDD, differential weighing of costs and benefits are theorized mechanisms underlying changes in goal-directed cognition and may contribute to symptom heterogeneity. We used the Effort Foraging Task to quantify cognitive and physical effort costs, and patch leaving thresholds in low effort conditions (hypothesized to reflect perceived opportunity cost of time) and investigated their shared versus distinct relationships to clinical features in participants with MDD (N=52, 43 in-episode) and comparisons (N=27). Contrary to our predictions, none of the decision-making measures differed with MDD diagnosis. However, each of the measures were related to symptom severity, over and above effects of ability (i.e., performance). Greater anxiety symptoms were selectively associated with lower cognitive effort cost (i.e. greater willingness to exert effort). Anhedonia symptoms were associated with increased physical effort costs. Finally, greater physical anergia was related to decreased patch leaving thresholds. Markers of effort-based decision-making may inform understanding of MDD heterogeneity. Increased willingness to exert cognitive effort may contribute to anxiety symptoms such as rumination and worry. The association of decreased leaving thresholds with symptom severity is consistent with reward rate-based accounts of reduced vigor in MDD. Future research should address subtypes of depression with or without anxiety, which may relate differentially to cognitive effort decisions.

Comparing experience- and description-based economic preferences across 11 countries.

Recent evidence indicates that reward value encoding in humans is highly context dependent, leading to suboptimal decisions in some cases, but whether this computational constraint on valuation is a shared feature of human cognition remains unknown. Here we studied the behaviour of n = 561 individuals from 11 countries of markedly different socioeconomic and cultural makeup. Our findings show that context sensitivity was present in all 11 countries. Suboptimal decisions generated by context manipulation were not explained by risk aversion, as estimated through a separate description-based choice task (that is, lotteries) consisting of matched decision offers. Conversely, risk aversion significantly differed across countries. Overall, our findings suggest that context-dependent reward value encoding is a feature of human cognition that remains consistently present across different countries, as opposed to description-based decision-making, which is more permeable to cultural factors.

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 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.

Outcome context-dependence is not WEIRD: Comparing reinforcement- and description-based economic preferences worldwide.

Recent evidence indicates that reward value encoding in humans is highly context-dependent, leading to suboptimal decisions in some cases. But whether this computational constraint on valuation is a shared feature of human cognition remains unknown. To address this question, we studied the behavior of individuals from across 11 countries of markedly different socioeconomic and cultural makeup using an experimental approach that reliably captures context effects in reinforcement learning. Our findings show that all samples presented evidence of similar sensitivity to context. Crucially, suboptimal decisions generated by context manipulation were not explained by risk aversion, as estimated through a separate description-based choice task (i.e., lotteries) consisting of matched decision offers. Conversely, risk aversion significantly differed across countries. Overall, our findings suggest that context-dependent reward value encoding is a hardcoded feature of human cognition, while description-based decision-making is significantly sensitive to cultural factors.

Structural integrity of the prefrontal cortex modulates electrocortical sensitivity to reward.

The P300 is a known ERP component assessing stimulus value, including the value of a monetary reward. In parallel, the incentive value of reinforcers relies on the PFC, a major cortical projection region of the mesocortical reward pathway. Here we show a significant positive correlation between P300 response to money (vs. no money) with PFC gray matter volume in the OFC, ACC, and dorsolateral and ventrolateral PFC in healthy control participants. In contrast, individuals with cocaine use disorders showed compromises in both P300 sensitivity to money and PFC gray matter volume in the ventrolateral PFC and OFC and their interdependence. These results document for the first time the importance of gray matter structural integrity of subregions of PFC to the reward-modulated P300 response.

Structural and behavioral correlates of abnormal encoding of money value in the sensorimotor striatum in cocaine addiction.

Abnormalities in frontostriatal systems are thought to be central to the pathophysiology of addiction, and may underlie the maladaptive processing of the highly generalizable reinforcer, money. Although abnormal frontostriatal structure and function have been observed in individuals addicted to cocaine, it is less clear how individual variability in brain structure is associated with brain function to influence behavior. Our objective was to examine frontostriatal structure and neural processing of money value in chronic cocaine users and closely matched healthy controls. A reward task that manipulated different levels of money was used to isolate neural activity associated with money value. Gray matter volume measures were used to assess frontostriatal structure. Our results indicated that cocaine users had an abnormal money value signal in the sensorimotor striatum (right putamen/globus pallidus) that was negatively associated with accuracy adjustments to money and was more pronounced in individuals with more severe use. In parallel, group differences were also observed in both the function and gray matter volume of the ventromedial prefrontal cortex; in the cocaine users, the former was directly associated with response to money in the striatum. These results provide strong evidence for abnormalities in the neural mechanisms of valuation in addiction and link these functional abnormalities with deficits in brain structure. In addition, as value signals represent acquired associations, their abnormal processing in the sensorimotor striatum, a region centrally implicated in habit formation, could signal disadvantageous associative learning in cocaine addiction.

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.

A neuroeconomic signature of opioid craving: How fluctuations in craving bias drug-related and nondrug-related value.

How does craving bias decisions to pursue drugs over other valuable, and healthier, alternatives in addiction? To address this question, we measured the in-the-moment economic decisions of people with opioid use disorder as they experienced craving, shortly after receiving their scheduled opioid maintenance medication and ~24 h later. We found that higher cravers had higher drug-related valuation, and that moments of higher craving within-person also led to higher drug-related valuation. When experiencing increased opioid craving, participants were willing to pay more for personalized consumer items and foods more closely related to their drug use, but not for alternative "nondrug-related" but equally desirable options. This selective increase in value with craving was greater when the drug-related options were offered in higher quantities and was separable from the effects of other fluctuating psychological states like negative mood. These findings suggest that craving narrows and focuses economic motivation toward the object of craving by selectively and multiplicatively amplifying perceived value along a "drug relatedness" dimension.

Differential brain glucose metabolic patterns in antipsychotic-naïve first-episode schizophrenia with and without auditory verbal hallucinations.

BACKGROUND: Auditory verbal hallucinations (AVHs) are a core symptom of schizophrenia. Previous reports on neural activity patterns associated with AVHs are inconsistent, arguably owing to the lack of an adequate control group (i.e., patients with similar characteristics but without AVHs) and neglect of the potential confounding effects of medication. METHODS: The current study was conducted in a homogeneous group of patients with schizophrenia to assess whether the presence or absence of AVHs was associated with differential regional cerebral glucose metabolic patterns. We investigated differences between patients with commenting AVHs and patients without AVHs among a group of dextral antipsychotic-naive inpatients with acute first-episode schizophrenia examined with [(18)F]fluoro-deoxyglucose positron emission tomography (FDG-PET) at rest. Univariate and multivariate approaches were used to establish between-group differences. RESULTS: We included 9 patients with AVHs and 7 patients without AVHs in this study. Patients experiencing AVHs during FDG uptake had significantly higher metabolic rates in the left superior and middle temporal cortices, bilateral superior medial frontal cortex and left caudate nucleus (cluster level p < 0.005, family wise error-corrected, and bootstrap ratio > 3.3, respectively). Additionally, the multivariate method identified hippocampal-parahippocampal, cerebellar and parietal relative hypoactivity during AVHs in both hemispheres (bootstrap ratio < -3.3). LIMITATIONS: The FDG-PET imaging technique does not provide information regarding the temporal course of neural activity. The limited sample size may have increased the risk of false-negative findings. CONCLUSION: Our results indicate that AVHs in patients with schizophrenia may be mediated by an alteration of neural pathways responsible for normal language function. Our findings also point to the potential role of the dominant caudate nucleus and the parahippocampal gyri in the pathophysiology of AVHs. We discuss the relevance of phenomenology-based grouping in the study of AVHs.

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.