Imprecise Action Selection in Substance Use Disorder: Evidence for Active Learning Impairments When Solving the Explore-Exploit Dilemma

Background: Substance use disorders (SUDs) are a major public health risk. However, mechanisms accounting for continued patterns of poor choices in the face of negative life consequences remain poorly understood. Methods: We use a computational (active inference) modeling approach, combined with multiple regression and hierarchical Bayesian group analyses, to examine how treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 147) and healthy controls (HCs; N = 54) make choices to resolve uncertainty within a gambling task. A subset of SUDs (n = 49) and HCs (n = 51) propensity-matched on age, sex, and verbal IQ were also compared to replicate larger group findings. Results: Results indicate that: (a) SUDs show poorer task performance than HCs (p=.03, Cohen’s d = .33), with model estimates revealing less precise action selection mechanisms (p=.004, d = .43), a lower learning rate from losses (p=.02, d = .36), and a greater learning rate from gains (p=.04, d = .31); and (b) groups do not differ significantly in goal-directed information seeking. Conclusions: Findings suggest a pattern of inconsistent behavior in response to positive outcomes in SUDs combined with a tendency to attribute negative outcomes to chance. Specifically, individuals with SUDs fail to settle on a behavior strategy despite sufficient evidence of its success. These learning impairments could help account for difficulties in adjusting behavior and maintaining optimal decision making during and after treatment.

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Substance Use in Emerging Adulthood

10.1093/oxfordhb/9780199795574.013.20 ◽

2014 ◽

Author(s):

Judy A. Andrews ◽

Erika Westling

Keyword(s):

Substance Use ◽

Emerging Adulthood ◽

Life Stress ◽

Pubertal Development ◽

Early Life Stress ◽

Peer Pressure ◽

College Attendance ◽

Optimal Decision ◽

Western World ◽

Optimal Decision Making

The prevalence of substance use and substance use disorders (SUDs) and the co-occurrence of SUDs with other mental health disorders peaks in emerging adulthood. This review examines prevalence as a function of gender, race/ethnicity, historical trends, and geographic regions across both the US and Western world. Prospective predictors reviewed include the effects of early life stress, parental factors (including parental use, support, and parenting skills), peer affiliations, internalizing and externalizing behaviors, educational attainment, personality, and timing of pubertal development. Concurrent predictors include assumption of adult roles and college attendance, stress associated with life events, changes in personality, and laws and taxation. Also reviewed are consequences of use, including neurological changes. The peak in prevalence across emerging adulthood may be due to several factors, including freedom from constraint, increased peer pressure, less than optimal decision-making skills, high disinhibition, and increased stress during this developmental period.

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Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma

Drug and Alcohol Dependence ◽

10.1016/j.drugalcdep.2020.108208 ◽

2020 ◽

Vol 215 ◽

pp. 108208 ◽

Cited By ~ 2

Author(s):

Ryan Smith ◽

Philipp Schwartenbeck ◽

Jennifer L. Stewart ◽

Rayus Kuplicki ◽

Hamed Ekhtiari ◽

...

Keyword(s):

Substance Use ◽

Active Learning ◽

Substance Use Disorder ◽

Action Selection ◽

Learning Impairments

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Geospatial Analysis and Mapping Strategies for Fine-Grained and Detailed COVID-19 Data with GIS

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10090602 ◽

2021 ◽

Vol 10 (9) ◽

pp. 602

Author(s):

Angel Miramontes Carballada ◽

Jose Balsa-Barreiro

Keyword(s):

Geospatial Analysis ◽

Optimal Decision ◽

Sensitive Information ◽

Fine Grained ◽

Health Authorities ◽

Public Health Authorities ◽

The Face ◽

Predictive Risk ◽

Optimal Decision Making ◽

The Impact

The unprecedented COVID-19 pandemic is showing dramatic impact across the world. Public health authorities attempt to fight against the virus while maintaining economic activity. In the face of the uncertainty derived from the virus, all the countries have adopted non-pharmaceutical interventions for limiting the mobility and maintaining social distancing. In order to support these interventions, some health authorities and governments have opted for sharing very fine-grained data related with the impact of the virus in their territories. Geographical science is playing a major role in terms of understanding how the virus spreads across regions. Location of cases allows identifying the spatial patterns traced by the virus. Understanding these patterns makes controlling the virus spread feasible, minimizes its impact in vulnerable regions, anticipates potential outbreaks, or elaborates predictive risk maps. The application of geospatial analysis to fine-grained data must be urgently adopted for optimal decision making in real and near-real time. However, some aspects related to process and map sensitive health data in emergency cases have not yet been sufficiently explored. Among them include concerns about how these datasets with sensitive information must be shown depending on aspects related to data aggregation, scaling, privacy issues, or the need to know in advance the particularities of the study area. In this paper, we introduce our experience in mapping fine-grained data related to the incidence of the COVID-19 during the first wave in the region of Galicia (NW Spain), and after that we discuss the mentioned aspects.

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