Temporal discounting and smoking cessation: choice consistency predicts nicotine abstinence in treatment-seeking smokers

Model-based experimental manipulation of probabilistic behavior in interpretable behavioral latent variable models

Introduction

Interpretable latent variable models that probabilistically link behavioral observations to an underlying latent process have increasingly been used to draw inferences on cognition from observed behavior. The latent process usually connects experimental variables to cognitive computation. While such models provide important insights into the latent processes generating behavior, one important aspect has often been overlooked. They may also be used to generate precise and falsifiable behavioral predictions as a function of the modeled experimental variables. In doing so, they pinpoint how experimental conditions must be designed to elicit desired behavior and generate adaptive experiments.

Methods

These ideas are exemplified on the process of delay discounting (DD). After inferring DD models from behavior on a typical DD task, the models are leveraged to generate a second adaptive DD task. Experimental trials in this task are designed to elicit 9 graded behavioral discounting probabilities across participants. Models are then validated and contrasted to competing models in the field by assessing the ouf-of-sample prediction error.

Results

The proposed framework induces discounting probabilities on nine levels. In contrast to several alternative models, the applied model exhibits high validity as indicated by a comparably low prediction error. We also report evidence for inter-individual differences with respect to the most suitable models underlying behavior. Finally, we outline how to adapt the proposed method to the investigation of other cognitive processes including reinforcement learning.

Discussion

Inducing graded behavioral frequencies with the proposed framework may help to highly resolve the underlying cognitive construct and associated neuronal substrates.

A Model Guided Approach to Evoke Homogeneous Behavior During Temporal Reward and Loss Discounting

Background

The tendency to devaluate future options as a function of time, known as delay discounting, is associated with various factors such as psychiatric illness and personality. Under identical experimental conditions, individuals may therefore strongly differ in the degree to which they discount future options. In delay discounting tasks, this inter-individual variability inevitably results in an unequal number of discounted trials per subject, generating difficulties in linking delay discounting to psychophysiological and neural correlates. Many studies have therefore focused on assessing delay discounting adaptively. Here, we extend these approaches by developing an adaptive paradigm which aims at inducing more comparable and homogeneous discounting frequencies across participants on a dimensional scale.

Method

The proposed approach probabilistically links a (common) discounting function to behavior to obtain a probabilistic model, and then exploits the model to obtain a formal condition which defines how to construe experimental trials so as to induce any desired discounting probability. We first infer subject-level models on behavior on a non-adaptive delay discounting task and then use these models to generate adaptive trials designed to evoke graded relative discounting frequencies of 0.3, 0.5, and 0.7 in each participant. We further compare and evaluate common models in the field through out-of-sample prediction error estimates, to iteratively improve the trial-generating model and paradigm.

Results

The developed paradigm successfully increases discounting behavior during both reward and loss discounting. Moreover, it evokes graded relative choice frequencies in line with model-based expectations (i.e., 0.3, 0.5, and 0.7) suggesting that we can successfully homogenize behavior. Our model comparison analyses indicate that hyperboloid models are superior in predicting unseen discounting behavior to more conventional hyperbolic and exponential models. We report out-of-sample error estimates as well as commonalities and differences between reward and loss discounting, demonstrating for instance lower discounting rates, as well as differences in delay perception in loss discounting.

Conclusion

The present work proposes a model-based framework to evoke graded responses linked to cognitive function at a single subject level. Such a framework may be used in the future to measure cognitive functions on a dimensional rather than dichotomous scale.

Delay discounting in suicidal behavior: Myopic preference or inconsistent valuation?

Prior studies sought to explain the predisposition to suicidal behavior in terms of myopic preference for immediate versus delayed reward, generating mixed evidence. Data from gambling and bandit tasks, however, suggest that suboptimal decisions in suicidal individuals are explained by inconsistent valuation rather than myopic preferences. We tested these two alternative hypotheses using a delay discounting task in 622 adults (suicide attempters with depression, suicide ideators with depression, nonsuicidal participants with depression, and healthy controls) recruited across three sites through inpatient psychiatric units, mood disorders clinics, primary care, and advertisements. Multilevel models revealed group differences in valuation consistencies in all three samples, with high-lethality suicide attempters exhibiting less consistent valuation than all other groups in Samples 1 and 3 and less consistent valuation than the healthy controls or participants with depression in Sample 2. In contrast, group differences in preference for immediate versus delayed reward were observed only in Sample 1 and were due to the high-lethality suicide attempters displaying a weaker preference for immediate reward than low-lethality suicide attempters. The findings were robust to confounds such as cognitive functioning and comorbidities. Seemingly impulsive choices in suicidal behavior are explained by inconsistent reward valuation rather than a true preference for immediate reward. In a suicidal crisis, this inconsistency may result in a misestimation of the value of suicide relative to constructive alternatives and deterrents. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Anhedonia in Nicotine Dependence

Prior findings indicate that trait anhedonia enhances the likelihood of becoming a tobacco smoker, and preliminary evidence suggests that smoking abstinence leads to anhedonic states in some individuals and situations, and nicotine administration reduces anhedonic states. Nevertheless, many vital questions exist concerning relationships between anhedonia and nicotine dependence, including situational and individual difference factors that may moderate the strength of these associations. This chapter provides a critical review of the literature assessing relationships of anhedonia to nicotine dependence and the effects of acute nicotine through the lenses of the Research Domain Criteria's (RDoC) Positive Valence Systems (NIMH, RDoC changes to the matrix (CMAT) workgroup update: proposed positive valence domain revisions. A report by the national advisory mental health council workgroup on changes to the research domain criteria matrix, 2018) and the Situation x Trait Affective Response (STAR) model of nicotine's effects and nicotine dependence (Gilbert, Smoking individual differences, psychopathology, and emotion. Taylor and Francis, Washington, DC, 1995; Gilbert, Hum Psychopharmacol Clin Exp 12:S89-S102, 1997). The effects of nicotine and nicotine withdrawal on subjective, behavioral, and brain indices vary across the three RDoC Positive Valences Systems (Reward Responsiveness, Reward Learning, and Reward Valuation) in a manner that supports the research and potential clinical utility of using RDoC criteria and the STAR model to guide research and clinical innovation. We provide a revision of the STAR model that incorporates the three RDoC Positive Valence Systems with evidence that nicotine's effects on hedonic and affective processes vary as a function of the dominance/salience of (1) situational hedonic and affective cues and task/active coping cues, and (2) state executive functioning level/capacity and state reward sensitivity such that these effects of nicotine are maximal during states of suboptimal cognitive functioning and reward sensitivity, combined with low situational stimulus salience and low task-related cues/demands.

Exploring the impacts of implicit context association and arithmetic booster in impulsivity reduction

People have a higher preference for immediate over delayed rewards, and it is suggested that such an impulsive tendency is governed by one's ability to simulate future rewards. Consistent with this view, recent studies have shown that enforcing individuals to focus on episodic future thoughts reduces their impulsivity. Inspired by these reports, we hypothesized that administration of a simple cognitive task linked to future thinking might effectively modulate individuals' delay discounting. Specifically, we used one associative memory task targeting intervention of context information, and one working memory task targeting enhancement of individual's ability to construct a coherent future event. To measure whether each type of cognitive task reduces individuals' impulsivity, a classic intertemporal choice task was used to quantify individuals' baseline and post-intervention impulsivity. Across two experiments and data from 216 healthy young adult participants, we observed that the impacts of intervention tasks were inconsistent. Still, we observed a significant task repetition effect such that the participants showed more patient choices in the second impulsivity assessment. In conclusion, there was no clear evidence supporting that our suggested intervention tasks reduce individuals' impulsivity, and that the current results call attention to the importance of taking into account task repetition effects in studying the impacts of cognitive training and intervention.

Precision Preventive Medicine of Relapse in Smoking Cessation: Can MRI Inform the Search of Intermediate Phenotypes?

Simple Summary

Addiction to tobacco is a serious health and economical problem because it is one of the most addictive and the most consumed substance in the world. Although well documented, and despite the desire of numerous smokers to quit, maintenance of abstinence is a daily challenge for most of them. The heterogeneity in achieving this maintenance raises the question of potential differences in brain reactivity. An emerging field of research has been interested in brain markers helping to identify individuals who are the most likely to relapse. Using brain imaging techniques such as Magnetic Resonance Imaging (MRI), one can hope it will be possible to offer tailored care for each patient.

Abstract

Chronic tobacco smoking remains a major health problem worldwide. Numerous smokers wish to quit but most fail, even if they are helped. The possibility of identifying neuro-biomarkers in smokers at high risk of relapse could be of incredible progress toward personalized prevention therapy. Our aim is to provide a scoping review of this research topic in the field of Magnetic Resonance Imaging (MRI) and to review the studies that investigated if MRI defined markers predicted smoking cessation treatment outcome (abstainers versus relapsers). Based on the available literature, a meta-analysis could not be conducted. We thus provide an overview of the results obtained and take stock of methodological issues that will need to be addressed to pave the way toward precision medicine. Based on the most consistent findings, we discuss the pivotal role of the insula in light of the most recent neurocognitive models of addiction.

Changes in Loss Sensitivity During Treatment in Concurrent Disorders Inpatients: A Computational Model Approach to Assessing Risky Decision-Making

BACKGROUND

Recent studies have employed computational modeling to characterize deficits in aspects of decision-making not otherwise detected using traditional behavioral task outcomes. While prospect utility-based modeling has shown to differentiate decision-making patterns between users of different drugs, its relevance in the context of treatment has yet to be examined. This study investigated model-based decision-making as it relates to treatment outcome in inpatients with co-occurring mental health and substance use disorders.

METHODS

50 patients (Mage = 38.5, SD = 11.4; 16F) completed the Cambridge Gambling Task (CGT) within 2 weeks of admission (baseline) and 6 months into treatment (follow-up), and 50 controls (Mage = 31.9, SD = 10.0; 25F) completed CGT under a single outpatient session. We evaluated 4 traditional CGT outputs and 5 decisional processes derived from the Cumulative Model. Psychiatric diagnoses and discharge data were retrieved from patient health records.

RESULTS

Groups were similar in age, sex, and premorbid IQ. Differences in years of education were included as covariates across all group comparisons. All patients had ≥1 mental health diagnosis, with 80% having >1 substance use disorder. On the CGT, patients showed greater Deliberation Time and Delay Aversion than controls. Estimated model parameters revealed higher Delayed Reward Discounting, and lower Probability Distortion and Loss Sensitivity in patients relative to controls. From baseline to follow-up, patients (n = 24) showed a decrease in model-derived Loss Sensitivity and Color Choice Bias. Lastly, poorer Quality of Decision-Making and Choice Consistency, and greater Color Choice Bias independently predicted higher likelihood of treatment dropout, while none were significant in relation to treatment length of stay.

CONCLUSION

This is the first study to assess a computational model of decision-making in the context of treatment for concurrent disorders. Patients were more impulsive and slower to deliberate choice than controls. While both traditional and computational outcomes predicted treatment adherence in patients, findings suggest computational methods are able to capture treatment-sensitive aspects of decision-making not accessible via traditional methods. Further research is needed to confirm findings as well as investigate the relationship between model-based decision-making and post-treatment outcomes.