In economics and psychology, delay discounting is often used to characterize how individuals choose between a smaller immediate reward and a larger delayed reward. People with higher delay discounting rate (DDR) often choose smaller but more immediate rewards (a "today person"). In contrast, people with a lower discounting rate often choose a larger future rewards (a "tomorrow person"). Since the ability to modulate the desire of immediate gratification for long term rewards plays an important role in our decision-making, the lower discounting rate often predicts better social, academic and health outcomes. In contrast, the higher discounting rate is often associated with problematic behaviors such as alcohol drug abuse, pathological gambling and credit card default. Thus, research on understanding and moderating delay discounting has the potential to produce substantial societ al benefits.
There is a rich body of research in economics and behavior science that investigates the relationship between DDR and real-world human behaviors such as drug abuse @cite_26 , an obese @cite_18 , pathological gambling @cite_7 , drinking @cite_19 , smoking @cite_14 , addiction to the internet @cite_27 and credit card default @cite_24 . Such a study often involves a small number of participants (e.g. a few dozens or a few hundreds). Experiment data are often obtained using questionnaires, surveys or interviews. Statistical analysis such as correlation or regression analysis are often employed to study the relationship between different test variables (e.g., DDR and smoking habits).
@cite_18 @cite_14 @cite_26 @cite_7 @cite_24 @cite_19 @cite_27
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Rationale: Impulsivity is implicated in drug dependence. Recent studies show problems with alcohol and opioid dependence are associated with rapid discounting of the value of delayed outcomes. Furthermore, discounting may be particularly steep for the drug of dependence. Objectives: We determined if these findings could be extended to the behavior of cigarette smokers. In particular, we compared the discounting of hypothetical monetary outcomes by current, never, and ex-smokers of cigarettes. We also examined discounting of delayed hypothetical cigarettes by current smokers. Methods: Current cigarette smokers (n=23), never-smokers (n=22) and ex-smokers (n=21) indicated preference for immediate versus delayed money in a titration procedure that determined indifference points at various delays. The titration procedure was repeated with cigarettes for smokers. The degree to which the delayed outcomes were discounted was estimated with two non-linear decay models: an exponential model and a hyperbolic model. Results: Current smokers discounted the value of delayed money more than did the comparison groups. Never- and ex-smokers did not differ in their discounting of money. For current smokers, delayed cigarettes lost subjective value more rapidly than delayed money. The hyperbolic equation provided better fits to the data than did the exponential equation for 74 out of 89 comparisons. Conclusions: Cigarette smoking, like other forms of drug dependence, is characterized by rapid loss of subjective value for delayed outcomes, particularly for the drug of dependence. Never- and ex-smokers could discount similarly because cigarette smoking is associated with a reversible increase in discounting or due to selection bias. Background: Behavioral interventions such as psychotherapy are leading, evidence-based practices for a variety of problems (e.g., substance abuse), but the evaluation of provider fidelity to behavioral interventions is limited by the need for human judgment. The current study evaluated the accuracy of statistical text classification in replicating human-based judgments of provider fidelity in one specific psychotherapy—motivational interviewing (MI). Method: Participants (n = 148) came from five previously conducted randomized trials and were either primary care patients at a safety-net hospital or university students. To be eligible for the original studies, participants met criteria for either problematic drug or alcohol use. All participants received a type of brief motivational interview, an evidence-based intervention for alcohol and substance use disorders. The Motivational Interviewing Skills Code is a standard measure of MI provider fidelity based on human ratings that was used to evaluate all therapy sessions. A text classification approach called a labeled topic model was used to learn associations between human-based fidelity ratings and MI session transcripts. It was then used to generate codes for new sessions. The primary comparison was the accuracy of model-based codes with human-based codes. Results: Receiver operating characteristic (ROC) analyses of model-based codes showed reasonably strong sensitivity and specificity with those from human raters (range of area under ROC curve (AUC) scores: 0.62 – 0.81; average AUC: 0.72). Agreement with human raters was evaluated based on talk turns as well as code tallies for an entire session. Generated codes had higher reliability with human codes for session tallies and also varied strongly by individual code. Conclusion: To scale up the evaluation of behavioral interventions, technological solutions will be required. The current study demonstrated preliminary, encouraging findings regarding the utility of statistical text classification in bridging this methodological gap. Research and clinical expertise indicates that impulsivity is an underlying feature of pathological gambling. This study examined the extent to which impulsive behavior, defined by the rate of discounting delayed monetary rewards, varies with pathological gambling severity, assessed by the South Oaks Gambling Screen (SOGS). Sixty-two pathological gamblers completed a delay discounting task, the SOGS, the Eysenck impulsivity scale, the Addiction Severity Index (ASI), and questions about gambling and substance use at intake to outpatient treatment for pathological gambling. In the delay discounting task, participants chose between a large delayed reward (US @math 1-$999) across a range of delays (6h to 25 years). The rate at which the delayed reward was discounted (k value) was derived for each participant and linear regression was used to identify the variables that predicted k values. Age, gender, years of education, substance abuse treatment history, and cigarette smoking history failed to significantly predict k values. Scores on the Eysenck impulsivity scale and the SOGS both accounted for a significant proportion of the variance in k values. The predictive value of the SOGS was 1.4 times that of the Eysenck scale. These results indicate that of the measures tested, gambling severity was the best single predictor of impulsive behavior in a delay discounting task in this sample of pathological gamblers. Longitudinal behavioral data generally contains a significant amount of structure. In this work, we identify the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the social network of a population. We represent this behavioral structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with 79 accuracy for our test subjects. Additionally, we demonstrate the potential for this dimensionality reduction technique to infer community affiliations within the subjects’ social network by clustering individuals into a “behavior space” spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96 classification accuracy of community affiliations within the population-level social network. Additionally, the distance between individuals in the behavior space can be used as an estimate for relational ties such as friendship, suggesting strong behavioral homophily amongst the subjects. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate phones, and communication of 100 subjects at MIT over the course of 9 months. As wearable sensors continue to generate these types of rich, longitudinal datasets, dimensionality reduction techniques such as eigenbehaviors will play an increasingly important role in behavioral research.