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Record W2302211062 · doi:10.1111/acer.13004

The Behavioral Economics and Neuroeconomics of Alcohol Use Disorders

2016· review· en· W2302211062 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAlcoholism Clinical and Experimental Research · 2016
Typereview
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare HamiltonHomewood Research Institute
FundersNational Institute on Drug AbuseNational Institute on Alcohol Abuse and AlcoholismNational Institutes of Health
KeywordsNeuroeconomicsBehavioral economicsPsychologyPsychological interventionAlcohol use disorderReinforcementPsychosocialAlcohol dependenceRandomized controlled trialDiscountingClinical psychologyCognitive psychologyPsychiatryAlcoholSocial psychologyMedicineEconomicsMicroeconomics

Abstract

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BACKGROUND: Behavioral economics and neuroeconomics bring together perspectives and methods from psychology, economics, and cognitive neuroscience to understand decision making and choice behavior. Extending an operant behavioral theoretical framework, these perspectives have increasingly been applied to understand the alcohol use disorders (AUDs), and this review surveys the theory, methods, and findings from this approach. The focus is on 3 key behavioral economic concepts: delay discounting (i.e., preferences for smaller immediate rewards relative to larger delayed rewards), alcohol demand (i.e., alcohol's reinforcing value), and proportionate alcohol-related reinforcement (i.e., relative amount of psychosocial reinforcement associated with alcohol use). FINDINGS: Delay discounting has been linked to AUDs in both cross-sectional and longitudinal studies and has been investigated cross-sectionally using neuroimaging. Alcohol demand and proportionate alcohol-related reinforcement have both been robustly associated with drinking and alcohol misuse cross-sectionally, but not over time. Both have also been found to predict treatment response to brief interventions. Alcohol demand has also been used to enhance the measurement of acute motivation for alcohol in laboratory studies. Interventions that focus on reducing the value of alcohol by increasing alternative reinforcement and response cost have been found to be efficacious, albeit in relatively small numbers of randomized controlled trials (RCTs). Mediators and moderators of response to these interventions have not been extensively investigated. FUTURE DIRECTIONS: The application of behavioral economics and neuroeconomics to AUDs has given rise to an extensive body of empirical work, although significant gaps in knowledge remain. In particular, there is a need for more longitudinal investigations to clarify the etiological roles of these behavioral economic processes, especially alcohol demand and proportionate alcohol reinforcement. Additional RCTs are needed to extend and generalize the findings for reinforcement-based interventions and to investigate mediators and moderators of treatment success for optimization. Applying neuroeconomics to AUDs remains at an early stage and has been primarily descriptive to date, but has high potential for important translational insights into the future. The same is true for using these behavioral economic indicators to understand genetic influences on AUDs.

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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.402
GPT teacher head0.552
Teacher spread0.150 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it