Impulsive decision-making predicts the course of substance-related and addictive disorders

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Abstract

Background

This study investigated whether patterns of impulsive decision-making (i) differ between individuals with DSM-5 substance use disorders (SUD) or non-substance-related addictive disorders (ND) and healthy controls, and (ii) predict the increase of SUD and ND severity after one year.

Methods

In a prospective-longitudinal community study, 338 individuals (19–27 years, 59% female) were included in one of three groups: SUD ( n = 100), ND ( n = 118), or healthy controls ( n = 120). Group differences in four impulsive decision-making facets were analyzed with the Bayesian priors: delay discounting (mean = 0.37, variance = 0.02), probability discounting for gains and for losses (each − 0.16, 0.02), and loss aversion (− 0.44, 0.02). SUD and ND severity were assessed at baseline and after 1 year ( n = 312, 92%). Predictive associations between decision-making and SUD/ND severity changes were analyzed with the Bayesian prior: mean = 0.25, variance = 0.016.

Results

Compared with controls, the SUD group displayed steeper delay discounting and lower probability discounting for losses; the ND group displayed lower probability discounting for losses (posterior probabilities > 98%). SUD symptom increase after 1 year was predicted by steeper delay discounting and lower loss aversion; ND symptom increase by lower probability discounting for losses and lower loss aversion (posterior probabilities > 98%). There was low evidence for predictive relations between decision-making and the quantity-frequency of addictive behaviours.

Discussion

Impulsive decision-making characterizes SUD and ND and predicts the course of SUD and ND symptoms but not the engagement in addictive behaviours. Strength of evidence differed between different facets of impulsive decision-making and was mostly weaker than a priori expected.

Electronic supplementary material

The online version of this article (10.1007/s00213-020-05567-z) contains supplementary material, which is available to authorized users.

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Most cited references 54

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The neural basis of loss aversion in decision-making under risk.

Sabrina Tom, Craig Fox, Christopher Trepel … (2007)

People typically exhibit greater sensitivity to losses than to equivalent gains when making decisions. We investigated neural correlates of loss aversion while individuals decided whether to accept or reject gambles that offered a 50/50 chance of gaining or losing money. A broad set of areas (including midbrain dopaminergic regions and their targets) showed increasing activity as potential gains increased. Potential losses were represented by decreasing activity in several of these same gain-sensitive areas. Finally, individual differences in behavioral loss aversion were predicted by a measure of neural loss aversion in several regions, including the ventral striatum and prefrontal cortex.

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An international consensus for assessing internet gaming disorder using the new DSM-5 approach.

Nancy M Petry, Florian Rehbein, Douglas A. Gentile … (2014)

For the first time, the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) introduces non-substance addictions as psychiatric diagnoses. The aims of this paper are to (i) present the main controversies surrounding the decision to include internet gaming disorder, but not internet addiction more globally, as a non-substance addiction in the research appendix of the DSM-5, and (ii) discuss the meaning behind the DSM-5 criteria for internet gaming disorder. The paper also proposes a common method for assessing internet gaming disorder. Although the need for common diagnostic criteria is not debated, the existence of multiple instruments reflect the divergence of opinions in the field regarding how best to diagnose this condition. We convened international experts from European, North and South American, Asian and Australasian countries to discuss and achieve consensus about assessing internet gaming disorder as defined within DSM-5. We describe the intended meaning behind each of the nine DSM-5 criteria for internet gaming disorder and present a single item that best reflects each criterion, translated into the 10 main languages of countries in which research on this condition has been conducted. Using results from this cross-cultural collaboration, we outline important research directions for understanding and assessing internet gaming disorder. As this field moves forward, it is critical that researchers and clinicians around the world begin to apply a common methodology; this report is the first to achieve an international consensus related to the assessment of internet gaming disorder. © 2014 Society for the Study of Addiction.

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A discounting framework for choice with delayed and probabilistic rewards.

Leonard Green, Joel Myerson (2004)

When choosing between delayed or uncertain outcomes, individuals discount the value of such outcomes on the basis of the expected time to or the likelihood of their occurrence. In an integrative review of the expanding experimental literature on discounting, the authors show that although the same form of hyperbola-like function describes discounting of both delayed and probabilistic outcomes, a variety of recent findings are inconsistent with a single-process account. The authors also review studies that compare discounting in different populations and discuss the theoretical and practical implications of the findings. The present effort illustrates the value of studying choice involving both delayed and probabilistic outcomes within a general discounting framework that uses similar experimental procedures and a common analytical approach. ((c) 2004 APA, all rights reserved)

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Author and article information

Contributors

Anja Kräplin:

ORCID: http://orcid.org/0000-0002-1612-3932

anja.kraeplin@tu-dresden.de

Journal

Journal ID (nlm-ta): Psychopharmacology (Berl)

Journal ID (iso-abbrev): Psychopharmacology (Berl.)

Title: Psychopharmacology

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Print): 0033-3158

ISSN (Electronic): 1432-2072

Publication date (Electronic): 5 June 2020

Publication date PMC-release: 5 June 2020

Publication date (Print): 2020

Volume: 237

Issue: 9

Pages: 2709-2724

Affiliations

[1 ]GRID grid.4488.0, ISNI 0000 0001 2111 7257, Faculty of Psychology, , Technische Universität Dresden, ; Dresden, Germany

[2 ]GRID grid.4488.0, ISNI 0000 0001 2111 7257, Work Group Addictive Behaviors, Risk Analysis and Risk Management, Faculty of Psychology, , Technische Universität Dresden, ; Chemnitzer Straße 46, 01187 Dresden, Germany

[3 ]GRID grid.4488.0, ISNI 0000 0001 2111 7257, Department of Psychiatry, , Technische Universität Dresden, ; Dresden, Germany

[4 ]GRID grid.5963.9, Freiburg Center for Data Analysis and Modeling, , University of Freiburg, ; Freiburg im Breisgau, Germany

[5 ]GRID grid.417840.e, ISNI 0000 0001 1017 4547, IFT Institut für Therapieforschung, ; Munich, Germany

[6 ]GRID grid.10825.3e, ISNI 0000 0001 0728 0170, Department of Clinical Research, Faculty of Health, , University of Southern Denmark, ; Odense, Denmark

Author information

Anja Kräplin http://orcid.org/0000-0002-1612-3932

Article

Publisher ID: 5567

DOI: 10.1007/s00213-020-05567-z

PMC ID: 7501099

PubMed ID: 32500211

SO-VID: 029f2af9-b571-409f-a92c-60576419ea09

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 9 December 2019

Date accepted : 20 May 2020

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;

Award ID: SFB 940/1, SFB 940/2

Custom metadata

ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine

Keywords: substance use disorders,behavioural addictions,decision-making,impulsivity,risk-seeking,loss aversion

Data availability:

ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine

Keywords: substance use disorders, behavioural addictions, decision-making, impulsivity, risk-seeking, loss aversion

Impulsive decision-making predicts the course of substance-related and addictive disorders

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Abstract

Background

Methods

Results

Discussion

Electronic supplementary material

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Thermal Imaging in Social Decision Making

Most cited references 54

The neural basis of loss aversion in decision-making under risk.

An international consensus for assessing internet gaming disorder using the new DSM-5 approach.

A discounting framework for choice with delayed and probabilistic rewards.

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