Structural Relationship Between Dark Personality Traits, Morbid Curiosity, and Cognitive Abilities with Addiction Vulnerability

authors:

avatar Ali Ghaemi ORCID 1 , avatar Ladan Vaghef ORCID 1 , avatar Behzad Shalchi ORCID 2 , *

Department of Psychology, Faculty of Educational Sciences and Psychology, Azerbaijan Shahid Madani University, Tabriz, Iran
Psychiatry and Behavioral Sciences Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
International Journal of High Risk Behaviors and Addiction: Vol.13, issue 3; e145652
published online: September 23, 2024
article type: Research Article
received: February 14, 2024
revised: July 24, 2024
accepted: August 17, 2024
DOI: https://doi.org/10.5812/ijhrba-145652

how to cite: Ghaemi A, Vaghef L, Shalchi B. Structural Relationship Between Dark Personality Traits, Morbid Curiosity, and Cognitive Abilities with Addiction Vulnerability. Int J High Risk Behav Addict. 2024;13(3):e145652. https://doi.org/10.5812/ijhrba-145652.

Abstract

Background:

Drug addiction represents a global crisis with increasing rates, particularly in regions like Iran. The widespread impact of addiction on individuals and societies underscores the need for comprehensive research. Providing further context on the global and national scope of the addiction crisis would strengthen the introduction.

Objectives:

This study aimed to examine the structural relationship between dark personality traits, morbid curiosity, and cognitive abilities, and their influence on vulnerability to addiction among Zanjan University students during the 2020 - 2021 academic year.

Materials and Methods:

A total of 479 students from Zanjan University were selected through a cluster random sampling method during the 2020 - 2021 academic year. The study utilized the Addiction Vulnerability Scale (Zinali), the Dark Personality Traits Scale (Webster and Johnson), the Morbid Curiosity Scale (Scrivener), and a Cognitive Abilities Scale (Nejati). Descriptive statistics and structural equation modeling were employed, using tools such as Pearson correlation and structural equations through SPSS 25 and AMOS 24 software.

Results:

The findings indicated that the model proposed by the researchers demonstrated an acceptable fit. Furthermore, a significant structural relationship was observed between dark personality traits and vulnerability to addiction (P < 0.05) with a path coefficient of 0.37. Additionally, the structural relationship between morbid curiosity and addiction vulnerability was significant (P < 0.05) with a path coefficient of 0.31. Cognitive abilities also had a significant structural relationship with vulnerability to addiction (P < 0.05) with a path coefficient of 0.46.

Conclusions:

The results of this study provide important insights for the prevention and early identification of addiction in vulnerable populations. These findings have practical applications for educational institutions, addiction treatment centers, and correctional facilities.

1. Background

Addiction is considered one of the four main global crises, alongside environmental destruction, atomic threats, and poverty, and remains a significant health issue in the 21st century. Recent statistics highlight the high prevalence of addiction, particularly among young people (1). According to the United Nations Office on Drugs and Crime, approximately 275 million people, or 5.5% of the global population aged 15 to 64, have used drugs (2). In Iran, the addiction rate is notably high, with 1 in 100 individuals affected (3). Various factors contribute to addiction vulnerability, including psychological, interpersonal, cultural-social, and biological-genetic elements (4, 5).

Researchers have identified personality traits (6), particularly dark personality traits (narcissism, Machiavellianism, and antisociality) (7), as significant predictors of addiction vulnerability (8-10). These traits are linked to risk-taking, sensation-seeking, and self-control issues. However, research findings have been inconsistent (8, 9). For example, Javak and Dietrich (8) found that narcissism and antisociality are associated with addictive behaviors, while Machiavellianism is not. Conversely, Karimikian's research (11) indicated that Machiavellianism predicts addiction tendencies, but antisociality and narcissism do not.

Curiosity, particularly morbid curiosity, is another factor influencing vulnerability to addiction (12). Morbid curiosity focuses on disturbing topics such as death, violence, or other events that may be emotionally or physically harmful (13). This type of curiosity is linked to attentional processes and, consequently, to addiction vulnerability. Focusing on unpleasant aspects of the environment may aid in learning about them. Although Zuckerman and Little (14) introduced the concept of morbid curiosity over 35 years ago, research in this area remains limited. Examining morbid curiosity in relation to high-risk behaviors such as suicide (15), violence (16), and substance abuse, including drugs and alcohol, offers a new perspective on these behaviors. This study is the first to investigate the relationship between morbid curiosity and addiction vulnerability.

Additionally, cognitive abilities play a role in addiction vulnerability (17). Cognitive abilities involve neural processes related to storing, processing, and using information. Individuals with weaker cognitive abilities often exhibit less control over impulsive behaviors, poor decision-making, and a tendency to imitate peers, which can increase vulnerability to addiction (18). The prevalence of cognitive disorders in drug addicts is still uncertain (19), with estimates ranging between 30% and 80% (20). This wide range suggests that the relationship between cognitive abilities and addiction requires further investigation.

2. Objectives

Given the ongoing issues, particularly the growing challenge of drug addiction in societies like Iran, this research aimed to fill a gap by examining the relationship between morbid curiosity and addiction vulnerability. The primary objective is to investigate the structural relationship between dark personality traits, morbid curiosity, and cognitive abilities with vulnerability to addiction.

3. Materials and Methods

3.1. Research Environment and Society

This descriptive-correlational study targeted students at Zanjan University during the 2020 - 2021 academic year, with a population of 10,414 students. Based on Cochran's formula, the minimum sample size required was 370; however, 500 participants were recruited to enhance credibility and generalizability. In the end, 479 students were included in the analysis, ensuring a robust sample for statistical purposes. Cluster random sampling was used to ensure validity, with inclusion criteria being willingness to participate and student status. Data were collected anonymously to maintain confidentiality, and participants were fully informed about the voluntary nature and purpose of the study.

3.2. Research Tools

In addition to demographic questions, the research tools include four scales:

3.2.1. Addiction Vulnerability Scale

The Zinali Addiction Vulnerability Scale (university student version) is a 40-item instrument designed to assess students' susceptibility to drug addiction. Each item is rated on a scale from 1 (none) to 3 (a lot), yielding a total score range of 40 to 120. The scale has demonstrated a criterion validity of 0.62 when compared to the addiction susceptibility scale and has been validated through exploratory factor analysis. The overall reliability of the scale is notably high, with a Cronbach's alpha of 0.98, and subscale reliabilities ranging from 0.92 to 0.97 (21). In the current study, however, the overall reliability (Cronbach's alpha) was 0.70, with subscale reliabilities ranging between 0.70 and 0.72.

3.2.2. Dark Personality Traits Scale

The Dark Personality Traits Scale, developed by Johnson and Webster in 2010, consists of 12 self-report items measuring Machiavellianism, narcissism, and antisocial behavior. Responses are rated on a Likert scale ranging from 1 (completely disagree) to 7 (completely agree), yielding scores ranging from 12 to 84. The scale demonstrates strong internal consistency, with a reported Cronbach's alpha of 0.83 (22). In Iranian research, test-retest reliability coefficients ranged from 0.66 to 0.80, with subscale consistencies between 0.68 and 0.77. Correlation coefficients for the total score were 0.57 for narcissism, 0.42 for antisocial behavior, and 0.55 for Machiavellianism (23). In the present study, Cronbach's alpha was 0.78 for the overall scale and 0.72, 0.75, and 0.74 for the narcissism, Machiavellianism, and antisocial behavior subscales, respectively.

3.2.3. Morbid Curiosity Scale

Scrivener's Morbid Curiosity Scale consists of 24 items that assess morbid curiosity across four dimensions: Dangerous people, body violation, supernatural danger, and interpersonal violence. Items are rated on a 5-point Likert Scale, ranging from 1 (completely disagree) to 5 (completely agree). Retest reliability coefficients were high: 0.80 for interpersonal violence, 0.81 for body violation, 0.86 for dangerous people, and 0.84 for supernatural danger. Cronbach's alpha values indicate strong reliability: 0.89, 0.87, 0.92, and 0.90 for these respective subscales (13). Validation research conducted by Ghaemi et al. (24) among Iranian university students reported a Cronbach's alpha of 0.85 for the entire scale and 0.80, 0.81, 0.79, and 0.79 for the subscales.

3.2.4. Cognitive Abilities Scale

The Cognitive Abilities Scale, developed by Nejati (25) in 2013, consists of 30 items designed to assess cognitive functions. Each item is rated on a five-point Likert scale, ranging from 1 (almost never) to 5 (almost always), with reverse scoring applied to items related to social cognition. The total score is based on seven factors identified through exploratory factor analysis: Memory, inhibitory control and selective attention, decision-making, planning, sustained attention, social cognition, and cognitive flexibility. Test-retest reliability, with a correlation coefficient of 0.86, demonstrated strong consistency over time. In the present study, Cronbach's alpha for the entire scale was 0.77, with subscale reliabilities ranging from 0.70 to 0.76.

3.3. Data Analysis Method

To analyze the research data, descriptive statistical measures such as frequency, mean, standard deviation, minimum, and maximum scores were utilized. Structural equation modeling (SEM) was applied to test the research hypotheses. Data analysis was conducted using SPSS version 25 and AMOS version 24.

4. Results

The study collected demographic data on participants, including age, gender, and educational level. The average age of participants was 24.13 years, with a standard deviation of 4.54, and ages ranged from 19 to 49 years. The youngest participant was 19, while the oldest was 49. Of the total participants, 259 (54.1%) were male, and 220 (45.9%) were female. Regarding educational levels, the sample included 10 associate students, 320 bachelor’s students, 124 postgraduate students, and 25 Ph.D. students. Table 1 presents the descriptive statistics for these demographic details.

Table 1.

Descriptive Statistics of the Research Variables

Variables and SubscaleAverageStandard Deviation
Vulnerability to addiction
Behaviors24.752.92
Way of life and social relations24.173.46
Personality characteristics19.912.63
Emotions and opinions11.981.92
Dark personality traits
Narcissism15.133.90
Machiavellianism14.583.69
Anti-social15.744.13
Morbid curiosity
Violence16.346.36
Body19.816.26
Supernatural15.956.16
Mind16.346.28
Cognitive abilities
Memory18.123.76
Selective attention and inhibitory control18.303.86
Decision making15.123.75
Planning8.602.62
Sustained attention9.272.35
Social cognition9.152.38
Social flexibility11.573.31

As shown in Table 2, the research variables are presented with their mean, standard deviation, minimum, and maximum values across the respective subscales. All variables exhibit absolute skewness coefficients below 3 and kurtosis coefficients below 10, indicating a normal distribution (26). Therefore, parametric statistical methods are suitable for analysis. The correlations among the research variables are detailed below before proceeding with the structural equation modeling.

Table 2.

Correlation Between Research Variables

Variables123456789101112131415161718
1. Behaviors1
2. Way of life and social relations 0.10 a1
3. Personality traits0.000.071
4. Emotions and opinions0.10 a0.00-0.011
5. Narcissism0.12 a0.070.050.031
6. Machiavellianism0.20 a0.080.080.050.29 b1
7. Anti-socialism0.19 a0.030.060.10 a0.27 b0.33 b1
8. Violence0.30 a0.11 a0.11 a0.12 a0.23 b0.38 b0.37 b1
9. Body-0.01-0.070.020.040.000.000.050.041
10. Supernatural0.24 b0.17 b0.040.040.17 b0.24 b0.27 b0.55 b0.21 b1
11. Mind0.24 b0.09 a0.050.070.13 b0.29 b0.28 b0.67 b0.25 b0.34 b1
12. Memory0.07-0.03-0.030.060.020.040.01 0.09 a-0.010.15 b0.09 a1
13. Selective attention and inhibitory control0.13 a0.000.010.050.070.010.000.080.010.070.10 a0.071
14. Decision making0.13 a0.080.09 a0.080.12 b0.13 b0.080.22 b0.030.16 b0.14 b0.11 a0.14 b1
15. Planning0.11 a0.040.11 a0.010.14 b0.14 b0.050.12 a0.040.040.09 a0.000.10 b0.21 b1
16. Sustained attention0.12 b0.030.060.030.030.040.040.14 b-0.010.050.11 a-0.010.070.12 b0.15 b1
17. Social cognition0.16 b-0.010.000.09 a0.080.10 a0.060.17 b0.080.12 b0.12 a0.20 b0.14 b0.25 b0.080.071
18. Social flexibility0.10 a0.040.030.040.11 a0.09 a0.09 a0.15 b-0.030.13 b0.16 b0.060.20 b0.24 b0.17 b0.18 b0.16 b1

a P < 0.05.

b P < 0.01.

As shown in Table 2, certain variables demonstrate a direct significant relationship with one another, while none of the research variables display an inverse significant relationship. Following this, we will assess the fit indices of the proposed model, as presented in Table 3.

Table 3.

The Fit Indices of the Model

Acceptable ValueAcceptable ValueThe Amount Obtained
Chi-square-214.59
Degrees of freedom-129
Chi-square/degrees of freedomLess than 31.66
goodness of fit More than 0.90.95
Adjusted goodness of Fit More than 0.90.93
Parsimony normed fixedMore than 0.60.69
Incremental fitMore than 0.90.92
Tucker-lewis’s coefficientMore than 0.90.90
Comparative Fit IndexMore than 0.90.91
Root mean square error of approximationLess than 0.080.037

As shown in Table 3, the reported fit indices indicate that the model developed by the researchers demonstrates a good fit. This suggests that the proposed structural model adequately represents the data. Additionally, the structural model of the research is illustrated in Figure 1.

The model is drawn in AMOS software
The model is drawn in AMOS software
Figure 1.

The model is drawn in AMOS software

In the final section of the findings, the path coefficients and significance of the relationships between the variables are reported. The maximum likelihood method was used to calculate the path coefficients and determine the significance of these relationships. The results are presented in Table 4.

Table 4.

Path Coefficient and Significance of Relationships Between Variables

PathNon-standard EstimateStandard EstimateStandard ErrorCritical RatioSignificance Level
Dark personality traits → vulnerability to addiction0.190.370.092.060.039
morbid curiosity → vulnerability to addiction0.090.310.042.020.043
Cognitive abilities → vulnerability to addiction0.750.460.302.440.014

Table 4 shows significant structural relationships between dark personality traits and vulnerability to addiction (P = 0.039), as well as between morbid curiosity and vulnerability to addiction (P = 0.043), since both values are below the 0.05 threshold. The structural relationship between cognitive abilities and vulnerability to addiction, with a P-value of 0.14, is also significant. These findings support the research hypotheses. Additionally, multiple linear regression analysis reveals that dark personality traits, morbid curiosity, and cognitive abilities collectively explain only 14% of the variance in vulnerability to addiction.

5. Discussion

The study aimed to develop a structural model to examine the relationship between addiction vulnerability and dark personality traits, morbid curiosity, and cognitive abilities among students. The findings revealed that dark personality traits significantly influence addiction vulnerability, consistent with the research of Jauk and Dietrich (8), Yousefi and Teimoori (9), and Sadri Damirchi et al. (10). This result is also in line with the hierarchical taxonomy of psychopathology (HiTOP), which positions narcissism and antisociality on the externalizing spectrum of mental disorders, adjacent to substance use disorders. The HiTOP model suggests that narcissistic and antisocial traits elevate addiction vulnerability through externalizing behaviors—hostile behaviors linked to narcissism and disinhibited behaviors related to antisocial traits. It further indicates that antisocial traits are more strongly associated with substance use than narcissistic traits due to their relationship with impulsivity and disinhibition (27). Additionally, individuals with dark personality traits, characterized by emotional instability and low empathy, may experience psychological insecurity, driven by a constant need for admiration, distrust of others, and interpersonal conflicts (28). This insecurity can make them more susceptible to substance use disorders, as they may turn to substances as a coping mechanism to manage emotional distress and unmet psychological needs (29).

The second key finding of the study was the significant structural relationship between morbid curiosity and vulnerability to addiction. This finding is novel and lacks direct precedence in the existing literature, though it aligns with the research of Asadi and Porzor (30), while contrasting with Racz's findings (31). This relationship can be understood through the identity formation process during adolescence and early adulthood, where increased risk-taking behaviors and curiosity often manifest in unhealthy activities, such as smoking or drug use (32). According to the optimal level of arousal theory, individuals who seek excitement and novelty may turn to substance use to meet their need for stimulation. The desire for novelty and escape from monotony, particularly in sensation-seeking individuals, can contribute significantly to substance abuse (33). Additionally, the link between morbid curiosity and addiction vulnerability can be explained by an individual's inherent drive to resolve uncertainty, even if negative outcomes are expected (34). This drive can lead individuals to explore ambiguous or dangerous phenomena, such as accidents or drugs, thus heightening their curiosity and making them more vulnerable to addiction.

The third key finding of the research revealed a significant structural relationship between cognitive abilities and vulnerability to addiction, aligning with the studies by Mosalman et al. (17) and Gould (35). One explanation for this relationship is that individuals with poor cognitive abilities tend to exhibit higher impulsivity, which cognitive neuroscience links to disruptions in cognitive inhibition within brain regions responsible for regulating behavior (36). This impulsivity is a known factor contributing to addiction (37). Furthermore, deficiencies in learning and working memory also increase vulnerability to addiction (38, 39). A working memory deficit prior to drug exposure is considered a factor that heightens susceptibility to addiction. This is particularly evident in individuals with psychiatric disorders such as depression and schizophrenia, who often exhibit significant learning and memory deficits and have higher rates of stimulant addiction (34). Another study found that adolescents with impaired working memory are more prone to drug addiction (40). Acute drug use, such as nicotine and cocaine, has been shown to increase hippocampal activity (41), suggesting that individuals may turn to psychostimulants as a way to compensate for memory deficits (37). Moreover, lower cognitive abilities can reduce health literacy and promote unhealthy lifestyles, making individuals more emotionally vulnerable to addiction (42).

5.1. Conclusions

This research demonstrated a significant structural relationship between dark personality traits, morbid curiosity, and cognitive abilities in predicting vulnerability to addiction. However, as the study was conducted solely on students from Zanjan University, caution is advised in generalizing the findings to other populations. Given Iran's cultural and ethnic diversity, it is recommended that further research be carried out in different cultural contexts to gain a more comprehensive understanding of addiction vulnerability. Additionally, investigating addiction vulnerability in other social groups beyond students would provide valuable insights into the broader societal factors influencing addiction.

References

  • 1.

    Eslampanah M, Mahdian H, Jajarmi M. [investigate the fitness of Iranian adolescents' risk-taking model in addiction with emphasis on family functioning and maladaptive schemas, distress tolerance and emotion regulation]. Res Addiction. 2020;13(54):67-90. FA.

  • 2.

    United Nations Office on Drugs and Crime. World Drug Report 2021. 2021. Available from: https://www.unodc.org/documents/data-and-analysis/WDR2021/WDR.

  • 3.

    Khazaie H, Behrouz B, Chehri A, Gerber M, Holsboer-Trachsler E, Sadeghi Bahmani D, et al. Among adolescents, addiction susceptibility and sleep-related dysfunction have a common cognitive-emotional base and predict poor sleep quantity. J Substance Use. 2019;24(4):426-31. https://doi.org/10.1080/14659891.2019.1588405.

  • 4.

    Fox TP, Oliver G, Ellis SM. The destructive capacity of drug abuse: an overview exploring the harmful potential of drug abuse both to the individual and to society. ISRN Addict. 2013;2013:450348. [PubMed ID: 25938116]. [PubMed Central ID: PMC4392977]. https://doi.org/10.1155/2013/450348.

  • 5.

    Hersen M, Ammerman RT. Advanced abnormal child psychology. New Jersey: Lawrence Erlbaum Associates Publishers; 2000.

  • 6.

    Hosťovecký M, Prokop P. The relationship between internet addiction and personality traits in Slovak secondary schools students. J Appl Mathematics, Statistics Inform. 2018;14(1):83-101. https://doi.org/10.2478/jamsi-2018-0006.

  • 7.

    Paulhus DL, Williams KM. The Dark Triad of personality: Narcissism, Machiavellianism, and psychopathy. J Res Personality. 2002;36(6):556-63. https://doi.org/10.1016/s0092-6566(02)00505-6.

  • 8.

    Jauk E, Dieterich R. Addiction and the dark triad of personality. Frontiers Psychiatry. 2019;10:662.

  • 9.

    Yousefi R, Teimoori S. [The Structural Relationship Between The Dark Triad Traits and Addiction Potentiality]. Res Addiction. 2020;13(54):31-46. FA.

  • 10.

    Sadri Damirchi E, Esrafily H, Mesbahi F. [Addiction Potential Pattern Based on Dark Triad Traits of Personality and Schema Modes]. Res Addiction. 2019;13(53):119-38. FA.

  • 11.

    Karimikian V. The role of dark dimensions of personality, emotional dysregulation and religious attitude in students' tendency to abuse drugs [Master thesis]. Tabriz, Iran: Shahid Madani University of Azerbaijan; 2016.

  • 12.

    Johnston LD, O'Malley PM, Bachman JG, Schulenberg JE. Monitoring the Future National Survey Results on Drug Use, 1975-2004 Volume I, Secondary School Students. Maryland, U.S: National Institutes of Health U.S. Department of Health and Human Services; 2005.

  • 13.

    Scrivner C. The psychology of morbid curiosity: Development and initial validation of the morbid curiosity scale. Pers Individ Diff. 2021;183:111139. https://doi.org/10.1016/j.paid.2021.111139.

  • 14.

    Zuckerman M, Litle P. Personality and curiosity about morbid and sexual events. Pers Individ Diff. 1986;7(1):49-56. https://doi.org/10.1016/0191-8869(86)90107-8.

  • 15.

    Fakhari A, Doshmangir L, Farahbakhsh M, Shalchi B, Shafiee-Kandjani AR, Alikhah F, et al. Developing inpatient suicide prevention strategies in medical settings: Integrating literature review with expert testimony. Asian J Psychiatr. 2022;78:103266. [PubMed ID: 36244295]. https://doi.org/10.1016/j.ajp.2022.103266.

  • 16.

    Girasek H, Nagy VA, Fekete S, Ungvari GS, Gazdag G. Prevalence and correlates of aggressive behavior in psychiatric inpatient populations. World J Psychiatry. 2022;12(1):1-23. [PubMed ID: 35111577]. [PubMed Central ID: PMC8783168]. https://doi.org/10.5498/wjp.v12.i1.1.

  • 17.

    Mosalman A, Hoseini A, Sadeghpoor M. ​Prediction of Addiction Readiness Based on Behavioral Activation and Inhibition of Systems (BIS/BAS) and Cognitive Abilities Among Students in Amol Institute of Higher Education 2017-2018. Military Caring Sci. 2018;5(2):146-55. https://doi.org/10.29252/mcs.5.2.146.

  • 18.

    Ebrahimi E, Abolmaali Alhosseini K. [The Mediating Role of Motivational Structure in the Relationship between Cognitive Abilities and High-Risk Behaviors in Adolescents]. Educ Psychol. 2017;13(46):171-90. FA. https://doi.org/10.22054/jep.2018.8481.

  • 19.

    Toledo-Fernández A, Brzezinski-Rittner A, Roncero C, Benjet C, Salvador-Cruz J, Marín-Navarrete R. Assessment of neurocognitive disorder in studies of cognitive impairment due to substance use disorder: A systematic review. J Substance Use. 2017;23(5):535-50. https://doi.org/10.1080/14659891.2017.1397208.

  • 20.

    Copersino ML, Fals-Stewart W, Fitzmaurice G, Schretlen DJ, Sokoloff J, Weiss RD. Rapid cognitive screening of patients with substance use disorders. Exp Clin Psychopharmacol. 2009;17(5):337-44. [PubMed ID: 19803633]. [PubMed Central ID: PMC3144764]. https://doi.org/10.1037/a0017260.

  • 21.

    Zeinali A. [Expanding and normalizing addiction aptitude questionnaire edited by students]. Educ Psychol Quarterly. 2014;4(4):1. FA.

  • 22.

    Jonason PK, Webster GD. The dirty dozen: A concise measure of the dark triad. Psychol Assessment. 2010;22(2):420-32. https://doi.org/10.1037/a0019265.

  • 23.

    Yousefi R, Piri F. [Psychometric Properties of Persian Version of Dirty Dozen Scale]. Iran Psychiatry Clin Psychol. 2016;22(1):67-76. FA. https://doi.org/10.1037/t61237-000.

  • 24.

    Ghaemi A, Vaghef L, Shalchi B. [Validity and reliability of persian version of morbid curiosity scale in students]. Med J Tabriz Uni Med Sci. 2024;45(6):485-94. FA. https://doi.org/10.34172/mj.2024.001.

  • 25.

    Nejati V. [Cognitive Abilities Questionnaire: Development and Evaluation of Psychometric Properties]. Adv Cogn Sci. 2013;15(2):11-9. FA.

  • 26.

    Kline RB. Principles and practice of structural equation modeling. New York: Guilford publications; 2023.

  • 27.

    Kotov R, Krueger RF, Watson D, Achenbach TM, Althoff RR, Bagby RM, et al. The Hierarchical Taxonomy of Psychopathology (HiTOP): A dimensional alternative to traditional nosologies. J Abnorm Psychol. 2017;126(4):454-77. [PubMed ID: 28333488]. https://doi.org/10.1037/abn0000258.

  • 28.

    Jordan D, Jonason PK, Zeigler-Hill V, Winer E, Fletcher S, Underhill D. A Dark Web of Personality: Network Analyses of Dark Personality Features and Pathological Personality Traits. J Psychopathol Behav Assessment. 2021;44(1):186-201. https://doi.org/10.1007/s10862-021-09882-3.

  • 29.

    Shalchi B, Nosrati Beigzadeh M, Shafiee-Kandjani AR, Shahrokhi H, Hoseinzadeh Khanmiri B. Childhood maltreatment and vulnerability to substance use disorders: The mediating role of psychological security. Health Promot Perspect. 2024;14(2):193-201. https://doi.org/10.34172/hpp.42525.

  • 30.

    Asadi SM, Porzoor P. [Investigating the causes of tendency to use substances (addiction) in Ardabil]. Res Addiction. 2020;14(56):169-98. FA. https://doi.org/10.29252/etiadpajohi.14.56.169.

  • 31.

    Racz J. The role of the curiosity in interviews with drug users. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research. 2008.

  • 32.

    Ahmadi TM, Asgari M, Toghiri A. [The Effectiveness of Life Skills Training on Reduction of the Risk and Enhancement of the Protective Factors against Drug Abuse for Delinquency Children in Reformatory Place]. Res Addiction. 2013;7(27):149-60. FA.

  • 33.

    Zuckerman M. Behavioral Expressions and Biosocial Bases of Sensation Seeking. Cambridge, England: Cambridge University Press; 1994.

  • 34.

    Hsee CK, Ruan B. Curiosity and Its Implications for Consumer Behavior. Continuing to Broaden the Marketing Concept. 2020. p. 223-39. https://doi.org/10.1108/s1548-643520200000017017.

  • 35.

    Gould TJ. Addiction and cognition. Addict Sci Clin Pract. 2010;5(2):4-14. [PubMed ID: 22002448]. [PubMed Central ID: PMC3120118].

  • 36.

    Aron AR. The neural basis of inhibition in cognitive control. Neuroscientist. 2007;13(3):214-28. [PubMed ID: 17519365]. https://doi.org/10.1177/1073858407299288.

  • 37.

    D'Souza MS. Brain and Cognition for Addiction Medicine: From Prevention to Recovery Neural Substrates for Treatment of Psychostimulant-Induced Cognitive Deficits. Front Psychiatry. 2019;10:509. [PubMed ID: 31396113]. [PubMed Central ID: PMC6667748]. https://doi.org/10.3389/fpsyt.2019.00509.

  • 38.

    Wunderli MD, Vonmoos M, Furst M, Schadelin K, Kraemer T, Baumgartner MR, et al. Discrete memory impairments in largely pure chronic users of MDMA. Eur Neuropsychopharmacol. 2017;27(10):987-99. [PubMed ID: 28866005]. https://doi.org/10.1016/j.euroneuro.2017.08.425.

  • 39.

    Potvin S, Stavro K, Rizkallah E, Pelletier J. Cocaine and cognition: a systematic quantitative review. J Addict Med. 2014;8(5):368-76. [PubMed ID: 25187977]. https://doi.org/10.1097/ADM.0000000000000066.

  • 40.

    Khurana A, Romer D, Betancourt LM, Hurt H. Working memory ability and early drug use progression as predictors of adolescent substance use disorders. Addiction. 2017;112(7):1220-8. [PubMed ID: 28206689]. [PubMed Central ID: PMC5461192]. https://doi.org/10.1111/add.13792.

  • 41.

    Kutlu MG, Gould TJ. Nicotinic modulation of hippocampal cell signaling and associated effects on learning and memory. Physiol Behav. 2016;155:162-71. [PubMed ID: 26687895]. [PubMed Central ID: PMC4718748]. https://doi.org/10.1016/j.physbeh.2015.12.008.

  • 42.

    Fakhari A, Shalchi B, Rahimi VA, Naghdi Sadeh R, Lak E, Najafi A, et al. Mental health literacy and COVID-19 related stress: The mediating role of healthy lifestyle in Tabriz. Heliyon. 2023;9(7). e18152. [PubMed ID: 37501969]. [PubMed Central ID: PMC10368831]. https://doi.org/10.1016/j.heliyon.2023.e18152.