Abstract
Purpose of the Review:
This systematic review and meta-analysis examined whether gambling behavior is associated with neurocognitive deficits across executive function, decision-making, delay discounting, and reward sensitivity, and whether these deficits support the dual-process model of addiction.
Collection and Analysis of Data:
PubMed, Scopus, Embase, and Web of Science were searched on September 1, 2024. Observational studies including individuals with gambling behavior identified using Diagnostic and Statistical Manual of Mental Disorders (DSM)/International Classification of Diseases (ICD) criteria and/or standardized screening instruments were eligible. Risk of bias (ROB) was assessed using the Joanna Briggs Institute (JBI) checklist (cross-sectional studies). Random-effects meta-analyses using Hedges’ g were conducted in R software, with heterogeneity, publication bias, leave-one-out sensitivity, subgroup, and meta-regression analyses.
Results:
Of 12,488 records, 76 studies (n = 5362) met eligibility for the review, and 35 (n = 2822) were included in the meta-analysis. Across 15 random-effects meta-analyses (k = 02–12; n = 38–562; I2 = 0%–94.1%), the largest deficits were observed in inhibitory control on the Stroop task (ST) under gambling (g = 1.91; p < .05) and neutral conditions (g = 1.83; p < .05), followed by delay discounting (area under the curve [AUC]: g = −1.07; p < .001; discounting parameter: g = 0.45; p < .01), cognitive flexibility on the probabilistic reversal learning test (g = −0.51; p < .05) and Intra/Extra-Dimensional Set Shift Task (g = 0.72; p < .001), and working memory on the Trail Making Test–B (TMT-B) (g = 0.59; p < .05). Decision-making impairments were partially supported by qualitative evidence, while no consistent deficits were observed in planning ability or reward sensitivity.
Conclusions:
Findings indicate impairments in controlled and automatic processes, partially supporting the dual-process model of gambling disorder; however, heterogeneity and task-specific effects warrant caution.
Question: Are gambling behaviors associated with neurocognitive deficits across the cognitive domains of regulatory behaviors? Findings: Gambling behavior is associated with significant deficits in inhibitory control, delay discounting, cognitive flexibility, and working memory. Meaning: There are impairments in some controlled and automatic processes, providing partial support for the dual-process model of addiction in gambling disorder.Key Messages:
Addiction has long been understood as a “disease of volition caused by cognitive impairment.” 1 The emergence of a number of dual-process models attempted to explain this mechanism.2–5 Although differing in detail, they all view addictive behaviors as the joint outcome of two classes of processes—the dominance of the automatic, impulsive systems over the controlled, reflective systems.
Neurocognitive research on substance use disorders (SUDs) associated cognitive impairments with their development 6 and exacerbation 7 in support of these theoretical models. This resulted in the development of several implicit- cognition-based treatments for SUDs.8–12 The inclusion of behavioral addictions in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) 13 has increased focus on similar research for gambling behavior, based on their similarities with biological addictions. 14
However, even when evidence of neurocognitive impairments in individuals with gambling disorder has been shown across areas of compulsivity, 15 working memory, 16 decision-making, 17 inhibitory control, 18 and impulsivity, 19 the literature is still limited when it comes to cognitive remediation (CR) interventions being used in the domain of gambling. A systematic review 20 identified only one study based on CR that targeted gambling disorder using a game-based program. 21 In the past, two studies,22,23 effectively used stop-signal training programs to manage proactive motor control in persons who gamble. More recently, the approach-avoidance bias training approach has been used in the context of gambling. 24 However, beyond this, cognitive bias remediation has not been tested or applied to gambling behavior.
One reason for the limited number of assessments and interventional research in this area might be that the dual- process model of addiction in the context of gambling has not been supported much by empirical literature, except for a cross-sectional study conducted in 2007 on 107 male pathological gamblers. 25 It may also be a lack of inferential data on cognitive deficits associated with gambling, in terms of the theoretical model. A comprehensive evaluation of the automatic and controlled processes being affected in the process of developing an addiction remains lacking.
Hence, the current systematic review and meta-analysis aimed to address this gap by investigating neurocognitive deficits associated with gambling behavior, focusing specifically on decision-making tendencies and executive functioning, in line with the delineation of neurocognitive domains presented by the DSM-5. 26
Methods
This systematic review and meta- analysis were conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols (PRISMA-P 2020; see Online Supplementary Material–I) guidelines 27 and have been registered on International Prospective Register of Systematic Reviews (PROSPERO). 28 Protocol preparation was done by the first author under supervision from the third, fourth, and fifth authors.
The PROSPERO protocol was amended on 28 October 2024 to allow inclusion of studies using diagnostic criteria from earlier versions of the DSM and International Classification of Diseases (ICD), rather than restricting eligibility to diagnoses based solely on the most recent revisions of these manuals. This change was made after it became clear that limiting eligibility to DSM-5 or ICD-11 criteria would exclude many well-conducted studies. Given the substantial conceptual continuity in the core diagnostic features of gambling disorder across DSM-III to DSM-5 and ICD-9 to ICD-11, inclusion of earlier diagnostic criteria was considered appropriate and necessary to ensure a comprehensive synthesis of the available evidence.
Consequently, in view of the extensive number of eligible studies identified, the difficulty in fully describing the findings obtained after following the pre-planned analyses strategy in a single review article, along with discussion of the context and implications of deficits observed across various cognitive domains, aligning with the dual- process model of addiction; we decided to present these domains independently in two separate review articles. While the methodology remains the same across both reviews, the results and discussion sections differ significantly. Thus, a separate systematic review and meta-analysis have been presented using the same protocol, focusing on the cognitive domains of attention, memory, learning, verbal fluency, and figural fluency.
Selection Criteria
Inclusion Criteria
Studies were eligible for inclusion if they employed observational research designs and used objective methods to assess neurocognitive domains. Observational designs included, but were not limited to, case–control, cross-sectional, cohort, prospective, and longitudinal studies. Although observational studies constitute a broad category, this approach was used to include all available evidence in this area. At the same time, all included studies had to assess neurocognitive domains using objective, task-based measures, which allowed the data to be treated as methodologically comparable for computing standardized mean differences (SMD).
The population group had to include individuals diagnosed with gambling disorder across all socio-demographic populations as per the criteria mentioned in the DSM–III to DSM–V13,29–32 or the ICD–9 to ICD–1133–36 and/or individuals fulfilling the criteria of problem gambling or pathological gambling according to a standardized gambling screening or assessment scale.
Where applicable, control groups consisted of healthy or community participants with no exposure to gambling behavior; studies that included only psychiatric comparison groups were excluded to maintain interpretability of neurocognitive differences specific to gambling. There were no restrictions on publication date or geographical location.
Exclusion Criteria
Exclusion criteria included observational studies that used subjective methods to assess neurocognitive domains and secondary studies. Qualitative studies, case reports, case series, and editorials were also excluded. Inaccessible studies and those published in languages other than English were not considered for this review.
Search Strategy
An electronic literature search was prepared and conducted on 15 September 2024 by the first author across the databases PubMed, Scopus, Embase, and Web of Science to identify relevant original articles. The search queries used for each database, incorporating all keywords mentioned above, are available in Online Supplementary Material–II.
Study Screening and Selection
Duplicates were removed using Zotero (
Data Extraction
Data extraction of the included studies conducted by the first and second authors incorporated recording details such as the first author’s name, publication year, sample size, demographic details of the sample, study setting, geographical location, cognitive domains(s) tested, neuropsychological test(s) used for their assessment, average performance scores of participants in the patient group and the control group (if any), and the concluding outcome as per the study authors themselves. Authors of the included studies were not contacted for further information.
The studies were categorized by the cognitive domain they assessed, leading some to be included in multiple domains. Additionally, the same study may have been included in both the systematic review and the meta-analysis, depending on the neuropsychological tests employed and the format of the results reported for each performance test. If a study assessed the same domain using multiple neuropsychological tests, the results for each test were recorded separately.
Risk of Bias Assessment
Two authors independently rated the included studies for methodological quality using the eight-item Joanna Briggs Institute (JBI) Critical Appraisal Checklists for cross-sectional studies. 39 Although study designs varied in how they were reported, most studies used cross-sectional designs, typically cross- sectional case–control comparisons, or cross-sectional assessments of a gambling group. Even when control participants were recruited, studies were generally described as cross-sectional rather than as case–control. Where the study design was not clearly stated, no assumptions were made, as contacting authors for clarification was not part of the protocol. In studies with longitudinal components, only baseline between-group cognitive data were extracted and analyzed. As all analyses were conducted within a cross-sectional framework, the JBI checklist for analytical cross-sectional studies was considered the most appropriate and consistently applicable tool for assessing methodological quality across studies.
A summary score was calculated for each study based on the percentage of applicable checklist items rated positively (i.e., higher scores indicate better quality or a lower risk of bias [ROB]). Based on the score, the included studies were classified into the following three categories based on their sum total JBI score percentages: Low ROB (at least 70%), moderate ROB (50%–69%), and high ROB (49% or below). 40
Data Analysis
For the systematic review, a heatmap was generated using the qualitative data reported in the studies not included in the meta-analysis. The primary outcome for this part of the analysis was neuropsychological test results for the gambling group, compared to the control group.
For the meta-analysis, the mean scores and standard deviations for both the gambling and control groups on each neuropsychological task across various cognitive domains were collected from different studies. SMD in effect sizes (Hedge’s g) were calculated with that data, using the Meta-analysis Effect Size Calculator developed by Campbell Collaboration (
Given the significant variation in test parameters across neuropsychological tasks and the absence of reported correlations in prior literature, random- effect meta-analyses (DerSimonian–Laird) were performed for each task, provided that at least two eligible studies were available. For each cognitive task, the outcome measure was selected based on the prior literature, established norms, prevalence of each parameter, and consensus among the study team. In some cases, more than one outcome score was used, depending on the literature and the availability of data for all reported parameters. A leave-one-out sensitivity analysis was also conducted for all meta-analyses to assess the impact of outliers on the robustness of the results. Wherever possible, exploration of potential sources of heterogeneity was undertaken using funnel plots and influence, moderator, and subgroup analyses with random-effect models. The mediating effect of age, gender ratio, and scores on the South Oaks Gambling Screen, 42 was studied on the performance of the gambling group using meta-regression. In addition, subgroup analyses were conducted based on gambling behavior (at-risk gambling vs. problem gambling vs. pathological gambling vs. gambling disorder), geographical location by World Health Organization (WHO) region, and ROB categorization for included studies (low, moderate, and high). All analyses were conducted using the R statistical software, version 4.4.3. 43
Results
Study Selection
The initial search identified 12,488 records, which were first screened by title and abstract, leaving 303 records for further evaluation of the full-text studies. This screening was performed independently by two authors, with a Cohen’s Kappa value of 0.84 indicating nearly perfect agreement. Of the 303 records, full texts of 38 studies were inaccessible, reducing the number to 264, from which 76 studies (persons who gamble = 2,712, controls = 2,650) were included in this review, and out of which 35 studies (persons who gamble = 1,328, controls = 1,494) were included in the meta-analysis. The study selection process is presented as a PRISMA flow diagram (see Online Supplementary Material–III, Figure S1).
Characteristics of the Included Studies
This review included 76 studies published between 1993 and 2024 (see Online Supplementary Material–IV, Table S1). It comprised 4,583 persons who gamble (3,351 males, 912 females) and 4,881 controls (2,966 males, 1,501 females), with gender unspecified in some studies. Reported mean ages ranged from 19.29 to 53.7 years for persons who gamble and from 19.6 to 47.5 years for controls. Educational qualifications varied from 3.08 to 15.5 years for persons who gamble and from 3.31 to 16.1 years for controls. The population that gambled comprised individuals with gambling disorder (n = 755), pathological gambling (n = 1,462), problem gambling (n = 352), at-risk gambling (n = 43), and a miscellaneous category including habitual, non-strategic, and strategic gamblers (n = 100). Across studies, the following cognitive domains were assessed: Inhibitory control (32 studies), cognitive flexibility (24 studies), working memory (14 studies), planning (six studies), decision-making (31 studies), and delay discounting (17 studies). Geographically, 51 studies were conducted in the European Region, 14 in the Region of the Americas, 10 in the Western Pacific Region, and one in the Eastern Mediterranean Region.
Risk of Bias Assessment
The average quality score of the included 76 studies was 6.30 using the JBI Critical Appraisal Checklists for cross-sectional studies. Of the total studies, 63 were categorized as having a low ROB, 36 as having a moderate ROB, and none as having a high ROB (Online Supplementary Material–III, Figure S2a). The domains most closely associated with an increased ROB were the identification of confounding factors and the strategies used to address them (Online Supplementary Material–III, see Figure S2b).
Main Results About the Primary and Secondary Outcomes
Part 1: Qualitative Synthesis
The non-meta-analyzable studies were reviewed using a heatmap covering all cognitive domains and neuropsychological tests employed (see Online Supplementary Material–III, Figure S3). This relative comparison between persons who gamble and controls across all domains showed that decision- making was most affected. However, findings across decision-making tasks were heterogeneous and varied by paradigm and parameters. The Iowa Gambling Task (IGT) yielded mixed results: Some studies reported poorer performance among persons who gamble, while others found no differences. Tasks assessing risk-taking under probabilistic conditions (e.g., Game of Dice Task, Balloon Analog Risk Task, Cups Task) generally indicated greater risk-taking among persons who gamble. In contrast, several paradigms, such as the Information Sampling Test, Paris Gambling Task, and Vancouver Gambling Task, reported no significant differences, suggesting that decision-making impairments in populations that gamble are task- dependent rather than uniformly present. The domain of inhibitory control came next, with most studies reporting poorer performance among persons who gamble and only a few showing no difference. Delay discounting and cognitive flexibility also showed clear deficits in the gambling groups. On the other hand, working memory, planning, and reward sensitivity showed fewer differences, with most studies indicating similar performance between the two groups.
Main Results About the Primary and Secondary Outcomes
Deficits in Cognitive Domains of the Populations that Gamble
A total of 15 random-effect meta-analyses (I2 = 0%–94.1%; see Supplementary File–IV, Table S2) indicated that gambling was associated with significant deficits in inhibitory control (Stroop task [ST]—neutral [g = 1.83, 95% CI = 0.28–3.37, p < .05] [Online Supplementary Material–V, Figure S1a] and gambling condition [g = 1.91, 95% CI = 0.33–3.49, p < .05] [Online Supplementary Material–V, Figure S1b]), cognitive flexibility (Intra/Extra-Dimensional Set Shift Task [g = 0.72, 95% CI = 0.29–1.16, p < .001] [Online Supplementary Material–V, Figure S1c], probabilistic reversal learning test [g = −0.51, 95% CI = −0.97 to −0.06, p < .05] [Online Supplementary Material–V, Figure S1d]), working memory (Trail Making Test–Part B (TMT-B) [g = 0.59, 95% CI = 0.08–1.10, p < .05] [Online Supplementary Material–V, Figure S1e]), and delay discounting (Delay Discounting Task [DDT]—discounting parameter [k] score [g = 0.45, 95% CI = 0.17–0.73, p < .01] [Online Supplementary Material–V, Figure S1f] and area under the curve (AUC) score [g = −1.07, 95% CI = −1.38 to −0.76, p < .001] [Online Supplementary Material–V, Figure S1g]).
Several of these findings were further strengthened with increased deficit scores in sensitivity analyses, including the Intra/Extra-Dimensional Set Shift Task, TMT-B, DDT–K Score, and DDT–AUC score by removing studies by Manning et al. (2013), 44 Kapsomenakis et al. (2018), 45 Ciccarelli et al. (2016), 46 and Albein-Urios et al. (2014), 47 respectively. Sensitivity analyses also revealed potential deficits for the Stop-Signal Task (SST) (Online Supplementary Material–V, Figure S1h), Wisconsin Card Sorting Test (WCST) (Online Supplementary Material– V, Figure S1i), Tower of London (ToL) task (Online Supplementary Material–V, Figure S1j), and IGT (Online Supplementary Material–V, Figure S1k) by omitting the outlier studies by Goudriaan et al. (2006), 48 Mallorquí-Bagué et al. (2021), 49 Aidelbaum et al. (2023), 50 and Kapsomenakis et al. (2018), 45 respectively (see Online Supplementary Material– V; Figures S2a–S2k). No significant effects were found at all for the ST—Interference/Inhibition Index (Online Supplementary Material–V, Figure S1l), Trail Making Test–Part A (Online Supplementary Material–V, Figure S1m), and Spatial Working Memory Task (SWMT) (Online Supplementary Material– V, Figure S1n), or Balloon Analog Risk Task (Online Supplementary Material–V, Figure S1o).
Effect of Gambling Severity
The meta-regression showed that those with higher South Oaks Gambling Screen (SOGS) scores had slower reaction times on the SST and the ST in the gambling condition. Subgroup analyses also indicated that pathological gamblers exhibited greater deficits than problem gamblers on the ST—neutral condition (reaction time) and ST—gambling condition (reaction time). Exploratory analyses, based on only one study in each group, on the other hand, suggested the following patterns: Problem gamblers > pathological gamblers > gambling disorder for deficits in planning ability on the ToL task, and pathological gamblers > gambling disorder on the Balloon Analog Risk Task (see Online Supplementary Material–V; Figures S3a–S3o).
Effect of Demographic Factors
A meta-regression analysis showed that age was positively associated with performance on the ST—interference/inhibition index, but negatively associated with reaction time on the ST—gambling condition. Male gender, in contrast, showed a positive association with reaction time on the same task.
Regional subgroup analyses found that studies conducted in European Regions showed greater deficits than those conducted in Western Pacific Regions on the ST and greater scores than the Regions of the Americas on the Balloon Analog Risk Task (see Online Supplementary Material–V; Figures S4a–s4l).
Effects of Risk of Bias
ROB subgroup analyses showed that studies with a moderate ROB reported greater deficits than those with a low ROB for both conditions of the ST—neutral and gambling (see Online Supplementary Material–V; Figures S5a–S5h).
Discussion
This systematic review and meta- analysis focused on combining evidence from existing literature to see if gambling also warrants the applicability of the dual-process model of addiction. It is a model that theorizes that addiction arises from the dominance of an automatic, impulse-driven system over a controlled, reflective system, leading to disruptions in both sets of cognitive functions. Hence, in the present review, we focused on both these conscious and unconscious cognitive mechanisms of addiction, covering inhibitory control, cognitive flexibility, working memory, decision-making, delay discounting, and reward sensitivity in an attempt to explore any potential associations in the context of gambling.
Across domains of cognition, the performance of persons who gamble showed deficits in meta-analytic results. For inhibitory control, the ST neutral and gambling conditions, assessed in terms of reaction time, showed large deficits. In contrast, the ST inhibition index and the SST showed no deficit. This is supported by a recent systematic review, which reported impaired inhibitory control in some studies that used the ST but not in those that used the SST. 18 In the DDT, performance again differed by the metric: The k score showed a small deficit, whereas the AUC score indicated a large deficit, aligning the results of a prior meta-analysis to some degree. 19 For cognitive flexibility, while the WCST did not show any deficits, the Intra-Extra Dimensional Set Shift Task (IDED) and the Probabilistic Reversal Learning Task (PRLT) showed moderate deficits. These results are partly in line with a recent meta-analysis, which confirmed the null findings for WCST but gave mixed results for IDED. 51 Conversely, our results for the TMT-B within the working memory category showed moderate impairments, which differed from Peixoto et al. review; however, this was based on only five included studies compared to our eight datasets. The TMT-A and the SWMT, on the other hand, showed no deficits. For planning and decision-making, no deficits came up either. This is in contrast to an earlier review by Kovács et al. (2017), 17 which reported greater deficits in the IGT in persons who gamble as compared to controls and even substance- dependent populations. However, the null finding for planning using the ToL task was consistent with an earlier systematic review by Dannon et al. (2010). 52 Within reward sensitivity, we were able to perform only a systematic review. Two studies found deficits, whereas two did not. The remaining non-meta-analytic studies were reviewed using a heatmap, which showed that decision-making was most affected, followed by inhibitory control. Delay discounting and cognitive flexibility also showed some clear deficits in the gambling groups. On the other hand, working memory, planning, and reward sensitivity showed fewer differences, with most studies indicating similar performance between the two groups. The overall review of the literature indicates that, across most cognitive domains, persons who gamble generally performed worse than controls, except in planning and reward sensitivity. However, several of the meta-analyses, particularly those assessing inhibitory control, demonstrated substantial heterogeneity across studies. This high level of between-study variability likely reflects differences in task parameters, outcome measures, and study populations; therefore, the pooled estimates should be interpreted cautiously when considering the generalizability of these findings.
Taken together, none of the neurocognitive domains examined appeared to be entirely unaffected by gambling behavior, as evidenced by the systematic review, meta-analysis, and sensitivity analysis. Our study reviews cognitive domains regulating both controlled and automatic behavior, and their deterioration supports the dual-process model of addiction in the context of gambling disorder to some extent. While our findings provide preliminary support for this theoretical model, this direction requires further research with more quantitative data. The scope of our meta-analyses was limited by the number of studies that could be included, which restricted the strength and generalizability of our findings. Cross-study comparisons were difficult due to methodological heterogeneity. Future studies will need to adopt more uniform designs as a basis for analyzable studies and generalizable conclusions. Primary studies and reviews covering other impulsive and regulatory cognitive domains are also required to strengthen the possibility of the dual-process model of addiction being applied to gambling.
At the same time, developing techniques to assess and treat these cognitive disturbances identified with the current evidence is important, as evidenced by the progress in SUDs.53,54 If the cognitive training strategies employed for biological addictions can be adapted and applied to gambling behavior, it may help interrupt the transition to forms of behavioral addictions as well. It may be worth trying to determine if treating the mechanism of formation of addictive behaviors actually works to treat the condition.
The meta-analyses suggest that the extent of impairment detected within a cognitive domain varies depending on the task and outcome measure selected. For example, in measuring inhibitory control, ST and SST tasks, when measured using reaction time, are more sensitive to detecting deficits than indices derived from interference or inhibition based on the same tasks. Similarly, in cognitive flexibility, the IDED and the PRLT detected deficits that the WCST did not. In delay discounting, the k score detected only small deficits, whereas the AUC score detected large deficits. 19 It is also possible that the deficits only become evident with the growing complexity of the task. For instance, while working memory impairment is evident on the TMT-B, it is absent on the Trail Making Test–Part A. Subsequent research can address the mechanisms underlying these task-specific differences, as well as the most sensitive measurement tools to consistently identify the deficit in this group. Clinically, such research can guide the use of appropriate assessment methods to better identify cognitive deficits in this population.
Across all cognitive domains, the meta-regression and subgroup analyses indicated that only inhibitory control deficits were significantly influenced by age, male gender, and gambling severity. In past literature on impulsivity, both ST and SST have been examined. For ST, moderation analyses found no significant effects of gender, geographical location, or study quality. In contrast, SST results indicated significant moderation by gender and study quality: Mixed-gender samples showed greater impairments than all-male samples, and higher-quality studies reported more deficits. These results, however, are not consistent with our findings, either in terms of the moderating factors or the neuropsychological tests involved. A possible explanation is the body of new literature that has emerged over the five years since that review was published. Most other domains showed no significant associations with demographic or methodological factors. Preliminary associations emerged for decision-making and planning regarding gambling severity and geographical location, but these remain exploratory and require confirmation in future studies as more data become available.
Some other limitations were also identified in our review. Instead of a literature search focused on the domains addressed in this review, the search was structured more broadly, potentially limiting the specificity of our review findings. Subsequent manual checks revealed that the database searches did not capture several relevant studies. It was challenging to categorize neuropsychological tests into a single cognitive domain, as most tests assessed multiple cognitive abilities, raising questions about the applicability of domain-specific outcomes. A review of previous studies further revealed that classification approaches varied considerably across reviews, which may limit comparability and pose difficulties for readers attempting to interpret results across studies. We did not account for comorbidities in the populations that gambled when these were not explicitly mentioned in the articles. While some studies excluded participants with comorbidities, others neither assessed nor reported them; in these cases, they were included in our analysis. Consequently, our study does not adequately account for the association of these comorbidities with the cognitive deficits exhibited by the target population groups. Excluding self-reported studies from this review prevented exploration of the relationship between subjective and objective measures. Much of the research was conducted in Europe and America, and generalizing these findings to global populations that gamble might not be appropriate, given cultural and demographic differences across populations. Moreover, empirical studies on cognitive aspects of gambling, particularly in domains such as planning and reward sensitivity, are few and require further research to confirm the findings of the current review.
As a result, there are limitations to applying the dual-process model to gambling behavior. The evidence across cognitive domains was heterogeneous and task-dependent, and not all domains or neuropsychological tasks consistently aligned with the predictions of the dual-process framework. In addition, methodological variability across studies, differences in task parameters, and the limited number of studies available for certain domains restrict the strength of conclusions that can be drawn regarding the model. These additions clarify that while the observed deficits in both controlled (e.g., inhibitory control, cognitive flexibility, working memory) and automatic processes (e.g., decision-making, delay discounting) are broadly consistent with the dual-process framework, the current evidence remains preliminary and incomplete, and further empirical research is required to evaluate the applicability of the model to gambling behavior fully.
Despite considerable research into cognitive deficits associated with gambling disorder over the last few years, existing studies have some limitations in terms of design. First, most studies had cross-sectional designs, precluding the establishment of potentially causal relationships between inhibitory control and these disorders. It is essential to move beyond “convenience research” and lay out methodologies that ensure studies meaningfully contribute to establishing gambling disorder as a distinct clinical entity and provide us with a more in-depth understanding of its features. Therefore, longitudinal studies are needed to examine potential changes in inhibitory control over the course of the disorder and determine whether these modifications play a role in the predisposition, development, or exacerbation of the condition.
Conclusions
Deficits are quantifiably substantiated in domains of inhibitory control, delay discounting, cognitive flexibility, and working memory, while decision- making provides qualitative evidence of impairments to some extent. There are no deficits in planning capacity or reward sensitivity. Having impairments in both controlled and automatic regulatory processes provides support for the dual-process model of addiction for gambling disorder.
Supplementary Material
Supplemental material for this article is available online.
Supplementary Material
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Supplementary Material
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Supplementary Material
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Supplementary Material
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Footnotes
Acknowledgements
The authors acknowledge and thank all researchers whose work was included in this review.
Appropriate Permissions from the Concerned Authorities
None.
Data Availability Statement
The data extraction sheet will be made available upon request.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Declaration Regarding the Use of Generative AI
No part of this article was written or generated by a generative AI tool. The authors take full responsibility for the accuracy, integrity, and originality of the published article.
Ethics Committee Approval
Ethical approval was not required for this study, as it is a systematic review and meta-analysis based on data from previously published studies and did not involve collection of primary data from human participants. The study protocol was prospectively registered on PROSPERO.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was partly funded by the Indian Council of Medical Research, India, via the grant number EMR/CAR-MH/2024/DL. Dr Yatan Pal Singh Balhara is the primary recipient of this research grant.
Informed Consent/Assent
N/A.
Patient Consent
Patient consent was also not required, as no individual-level participant data were collected or reported in this study.
Prior Presentations
This review article has not been presented at any conferences/ symposia as of now.
Prospective Registration
URL of the registry record: Registration number: CRD42024585796 Date of registration: 12 September 2024
PROSPERO/CTRI Details
This systematic review and meta-analysis were conducted and reported in accordance with the PRISMA-P 2020 guidelines (Page et al., 2021) and have been registered on PROSPERO International Prospective Register of Systematic Reviews (URL:
Registration
N/A.
Simultaneous Submission to Another Journal or Resource
No.
Status of Your Study (For Study Protocol)
Completed.
Citation Diversity Statement
We are committed to equitable citation practices and have made conscious efforts to include work from authors of diverse genders, geographic regions (including the Global South), career stages, and historically marginalized groups. We aim to support a more inclusive and representative scholarly record.
References
Supplementary Material
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