| Title |
An Eight Component Decision-Making Model for Problem Gambling:A Systems Approach to Stimulate Integrative Research
|
|---|---|
| Published in |
Journal of Gambling Studies, December 2010
|
| DOI | 10.1007/s10899-010-9219-8 |
| Pubmed ID | |
| Authors |
David Nussbaum, Kimia Honarmand, Richard Govoni, Martina Kalahani-Bargis, Stephanie Bass, Xinqun Ni, Kaitlyn LaForge, Andrea Burden, Kristoffer Romero, Sonya Basarke, Christine Courbasson, Wade Deamond |
| Abstract |
Problem Gambling (PG) represents a serious problem for affected individuals, their families and society in general. Previous approaches to understanding PG have been confined to only a subset of the psychobiological factors influencing PG. We present a model that attempts to integrate potential causal factors across levels of organization, providing empirical evidence from the vast literature on PG and complimentary literatures in decision-making and addiction. The model posits that components are arranged systematically to bias decisions in favor of either immediately approaching or avoiding targets affording the opportunity for immediate reward. Dopamine, Testosterone and Endogenous Opioids favor immediate approach, while Serotonin and Cortisol favor inhibition. Glutamate is involved in associative learning between stimuli and promotes the approach response through its link to the DA reward system. GABA functions to monitor performance and curb impulsive decision-making. Finally, while very high levels of Norepinephrine can induce arousal to an extent that is detrimental to sound decision-making, the reactivity of the Norepinephrine system and its effects of Cortisol levels can shift the focus towards long-term consequences, thereby inhibiting impulsive decisions. Empirical evidence is provided showing the effects of each component on PG and decision-making across behavioural, neuropsychological, functional neuroimaging and genetic levels. Last, an effect size analysis of the growing pharmacotherapy literature is presented. It is hoped that this model will stimulate multi-level research to solidify our comprehension of biased decision-making in PG and suggest pharmacological and psychological approaches to treatment. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 2 | 2% |
| United States | 1 | <1% |
| Germany | 1 | <1% |
| Belgium | 1 | <1% |
| Unknown | 111 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 24 | 21% |
| Researcher | 21 | 18% |
| Student > Master | 12 | 10% |
| Professor | 8 | 7% |
| Student > Bachelor | 8 | 7% |
| Other | 24 | 21% |
| Unknown | 19 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 41 | 35% |
| Medicine and Dentistry | 15 | 13% |
| Neuroscience | 10 | 9% |
| Social Sciences | 8 | 7% |
| Agricultural and Biological Sciences | 7 | 6% |
| Other | 12 | 10% |
| Unknown | 23 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 April 2012.
All research outputs
#20,657,128
of 25,374,917 outputs
Outputs from Journal of Gambling Studies
#791
of 989 outputs
Outputs of similar age
#170,888
of 190,488 outputs
Outputs of similar age from Journal of Gambling Studies
#8
of 8 outputs
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