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

Großkopf, Charlotte M.; Kroemer, Nils B.; Pooseh, Shakoor; Böhme, Franziska; Smolka, Michael N.

doi:10.1007/s00213-020-05688-5

Cited by 9 publications

(11 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lower quality of decision-making and choice consistency, and higher values for color choice bias at baseline were identified as predictors for greater likelihood of unplanned treatment termination (against medical advice). These findings are consistent with prior research reporting the negative influence of inconsistent choice bias (60) and suboptimal quality of decision-making [i.e., rational choice passed on probability; (32)] in lowering the rate of treatment retention among substance use disorder patients. Color choice bias emerged as findings consistent with those reported in the prior study (46).…”

Section: Discussionsupporting

confidence: 91%

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

et al. 2022

View full text Add to dashboard Cite

BackgroundRecent 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.Methods50 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.ResultsGroups 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.ConclusionThis 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.

show abstract

Section: Discussionsupporting

confidence: 91%

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The latter are then used to contrast neural activation toward “hard” as compared to “easy” adaptive trials (i.e., trials close to vs. far from the indifference point) ( 58 ), or to compare immediate and delayed responses with comparable frequency at the indifference points. However, since discounting is described by a continuous monotone function, it may in principle be interesting to study the neural response not only at the indifference point of a subject at which we expect a 0.5 discounting probability [see also ( 59 , 60 )]. By parametrically mapping the individual discounting curves onto behavioral probabilities comparable across subjects, we may construe experimental trials which allow us to examine discounting behavior and its neural correlates dimensionally.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

BackgroundThe 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.MethodThe 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.ResultsThe 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.ConclusionThe 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.

show abstract

“…Based on the consistent finding that substance use disorders are associated with poor abilities to delay gratification (e.g., choose 10 euros immediately over 30 euros in 3 months, also called temporal discounting), Grosskopf et al, (2020) examined if post- versus pre-treatment changes in brain activity during a temporal discounting task is associated with treatment outcome. No significant differences were observed between relapsers and abstainers [ 45 ].…”

Section: Resultsmentioning

confidence: 99%

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

Rabat

Chanraud

Abdallah

et al. 2021

Biology

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 9 publications

References 57 publications

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

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

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

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

Contact Info

Product

Resources

About