Open access

Abstract

Background and aims

Gambling Disorder (GD) entails maladaptive patterns of decision-making. Neurophysiological research points out the effect of parasympathetic arousal, including phasic changes in heart rate variability (HRV), and interoceptive accuracy (IA, i.e., the ability to track changes in bodily signals), on decision-making. Nevertheless, scarce evidence is available on their role in GD. This is the first study exploring the impact in GD of respiratory sinus arrhythmia (RSA), an index of HRV, and IA on decision-making, as measured by the Iowa Gambling Task (IGT).

Methods

Twenty-two patients experiencing problems with slot-machines or video lottery terminals gambling and 22 gender- and age-matched healthy controls (HC) were recruited. A resting ECG was performed before and after the completion of the IGT. IA was assessed throughout the heartbeat detection task. We conducted a MANCOVA to detect the presence of significant differences between groups in RSA reactivity and IA. A linear regression model was adopted to test the effect of factors of interest on IGT scores.

Results

Patients with GD displayed significantly decreased RSA reactivity (P = 0.002) and IA (P = 0.024) compared to HCs, even after controlling for affective symptoms, age, smoking status, and BMI. According to the linear regression model, cardiac vagal reactivity and IA significantly predict decision-making impairments on the IGT (P = 0.008; P = 0.019).

Discussion and conclusions

Although the exact pathways linking HRV and IA to impaired decision-making in GD remain to be identified, a broader exploration relying upon an embodiment-informed framework may contribute to shed further light on the clinical phenomenology of the disorder.

Abstract

Background and aims

Gambling Disorder (GD) entails maladaptive patterns of decision-making. Neurophysiological research points out the effect of parasympathetic arousal, including phasic changes in heart rate variability (HRV), and interoceptive accuracy (IA, i.e., the ability to track changes in bodily signals), on decision-making. Nevertheless, scarce evidence is available on their role in GD. This is the first study exploring the impact in GD of respiratory sinus arrhythmia (RSA), an index of HRV, and IA on decision-making, as measured by the Iowa Gambling Task (IGT).

Methods

Twenty-two patients experiencing problems with slot-machines or video lottery terminals gambling and 22 gender- and age-matched healthy controls (HC) were recruited. A resting ECG was performed before and after the completion of the IGT. IA was assessed throughout the heartbeat detection task. We conducted a MANCOVA to detect the presence of significant differences between groups in RSA reactivity and IA. A linear regression model was adopted to test the effect of factors of interest on IGT scores.

Results

Patients with GD displayed significantly decreased RSA reactivity (P = 0.002) and IA (P = 0.024) compared to HCs, even after controlling for affective symptoms, age, smoking status, and BMI. According to the linear regression model, cardiac vagal reactivity and IA significantly predict decision-making impairments on the IGT (P = 0.008; P = 0.019).

Discussion and conclusions

Although the exact pathways linking HRV and IA to impaired decision-making in GD remain to be identified, a broader exploration relying upon an embodiment-informed framework may contribute to shed further light on the clinical phenomenology of the disorder.

Introduction

Gambling Disorder (GD) features a persistent and maladaptive urge to be involved in gambling activities, which may entail an abnormal preference towards high-risk pattern of decision-making (Brevers, Koritzky, Bechara, & Noël, 2014). Decision-making is broadly defined as the faculty to favor certain choices by pondering their conceivable punitive or rewarding outcomes. Impairments in decision-making abilities are thought to play a pivotal role in the onset and maintenance of addictive disorders, including GD. Indeed, deficits in decision-making have been associated with several parameters of gambling severity, such as gambling frequency, amount of money lost, and gambling urge intensity (Moccia et al., 2017). Additionally, there is evidence supporting the prognostic value of laboratory measures of decision making for several clinical outcomes, including poorer treatment compliance or increased relapse rates (Rochat, Maurage, Heeren, & Billieux, 2019).

Neurophysiological research points out the role of autonomic arousal, including phasic changes in heart rate variability (HRV), in sustaining decision-making (Dulleck, Ristl, Schaffner, & Torgler, 2011). HRV represents the time variation between heartbeats considered as successive peaks of QRS complexes (i.e., the combination of three of the graphical deflections seen on a typical ECG corresponding to the depolarization and the subsequent contraction of heart ventricles) and it is often considered as a proxy of the parasympathetic nervous system (PNS) activity (Laborde, Raab, & Kinrade, 2014). The PNS is a division of the autonomic nervous system, which controls through its efferents, such as the vagus nerve, several automatic processes, including digestion, respiration, as well as heart rate. Research suggests that the amplitude of respiratory sinus arrhythmia (RSA), a HRV metric referring to the spontaneous variation in heart rate that occurs during the breathing cycle, is a reliable index of PNS activity, reflecting the contribution of the vagus nerve to cardiac functioning (Laborde, Mosley, & Thayer, 2017). This is of relevance, as there is evidence that impaired PNS activity may account in GD clinical phenomenology, including decision-making. Goudriaan, Oosterlaan, de Beurs, and van den Brink (2006) reported blunted anticipatory parasympathetic responses, including skin conductance, to risky choices in GD. Similarly, there is evidence for abnormal PNS activity in situations associated with imaginal recall of winning versus losing scenarios in GD (Sharpe, 2004). Taken together, these findings point at a role of the PNS in impaired risk/reward assessment in GD.

Neurophysiological research on autonomic reactivity in GD has paid little attention to the individual's capacity to consciously detect changes in internal states of autonomic arousal, focusing, instead, predominantly on the amplitude of autonomic bodily signals per se (Brevers & Noël, 2013; Clark, Studer, Bruss, Tranel, & Bechara, 2014; Kennedy et al., 2019). Exposure to gambling-cues may result in enhanced autonomic arousal, which may be experienced subjectively by individuals with GD as abnormal bodily sensations that may ultimately lead to gambling urges (Sharpe, Tarrier, Schotte, & Spence, 1995). Interoceptive accuracy (IA) broadly refers to the ability to detect and track subtle changes in internal bodily sensations, including muscles, skin, joints, and viscera afferent signals (Garfinkel, Seth, Barrett, Suzuki, & Critchley, 2015). According to several lines of evidence, interoceptive processes may contribute to the onset and maintenance of addictive disorders (Verdejo-Garcia, Clark, & Dunn, 2012). Besides, the representation of bodily homeostatic milieu in brain areas such as the insular cortex is hypothesized to influence cognitive-affective processing, including decision-making (Bechara & Damasio, 2005).

The Iowa Gambling Task (IGT; Bechara, Damasio, Damasio, & Anderson, 1994) has been regarded among the most widely adopted and ecologically valid measure of decision-making in individuals with GD. One of the reasons for its ecological validity is that optimal performance on this task is attained through dealing with uncertainty in a context of reward and punishment, as in “real-life” decisions. Some choices lead to immediate and more substantial benefits but also carry the risk of greater loss, while other choices result in smaller immediate gains but provide greater benefits in the long run. Hence, the key aspect of this task is that participants have to forgo short-term gains for long-term gains (Brevers, Bechara, Cleeremans, & Noël, 2013). Individuals with GD perform poorly on the IGT, frequently chasing the larger, immediately rewarding gains, which ultimately lead to long-term losses (Moccia et al., 2017). Choosing among different options according to their long- and short-term outcomes implies similar neurobiological processes in human and translational models of decision-making, and thus, a disadvantageous pattern of preference for “high-risk/high-reward” options may represent a behavioral substrate of vulnerability to addictive disorders (Winstanley & Clark, 2016). Intriguingly, and consistent with this conceptual framework, IA and HRV were found to moderate IGT performance in healthy subjects (Drucaroff et al., 2011; Dunn et al., 2010).

To our knowledge, this is the first study that, focusing on an accurately selected group of GD individuals addicted to slot machines or video lottery terminals (VLT), explored the joint impact of IA and phasic changes in RSA on decision-making impairments as measured by the IGT. We hypothesized that individuals with GD report blunted RSA reactivity and decreased IA as compared to gender- and age- matched healthy controls (HCs), and that RSA and IA may predict the performance on the IGT.

Methods

Participants

Twenty-two treatment-seeking male patients, aged from 18 to 65 (mean age 47.5±12.5), with a diagnosis of GD according to DSM-5 criteria were consecutively recruited from GD specialized outpatient clinics of Fondazione Policlinico Universitario Agostino Gemelli IRCCS-Università Cattolica del Sacro Cuore, Rome, Italy. The Structured Clinical Interview for DSM-5 Clinician Version (SCID-5-CV; First, Williams, Karg, & Spitzer, 2017) was employed to establish GD diagnosis and psychiatric comorbidity. Patients were also screened for Personality Disorders using the Structured Clinical Interview for DSM-5 Personality Disorders (SCID-5-PD; First, Williams, Benjamin, & Spitzer, 2016). Diagnostic interviews were conducted at study entrance by raters with extensive training and high interrater reliability (k > 0.8). The following inclusion and exclusion criteria were strictly adopted to ensure a reliable study sample: all participants met DSM-5 criteria for GD, without any current comorbid psychiatric disorder or substance abuse. Moreover, participants had to demonstrate adequate command over written and spoken Italian language and report cognitive function within the normal range according to Raven's progressive matrices test (Raven, 2000). Twenty-two gender- and age-matched HCs were recruited through local online advertising. All HCs were screened for lifetime personal history of DSM-IV-TR Axis I and II disorders using the SCID-I/NP (First, Spitzer, Gibbon, & Williams, 2002) and SCID-II (First, Gibbon, Spitzer, Williams, & Benjamin, 1997). Participants with DSM-IV-TR Axis I or II disorders were excluded from the HC group. All other eligibility criteria were the same as those for the GD group. Additional exclusion criteria for both groups included unstable medical illnesses (head trauma, neurological and cardio-respiratory diseases, and diabetes), as well as current intake of medications altering the cardio-respiratory activity (Quintana, Alvares, & Heathers, 2016). Furthermore, since there is evidence that cardiac vagal tone is affected by regular exercise (Nakamura, Yamamoto, & Muraoka, 1993), only individuals not regularly involved in athletic, or endurance sports were recruited. The final sample comprised 44 individuals, 22 with GD and 22 gender- and age-matched HCs, a number that was comparable with previous neurophysiological research assessing HRV and IA in selected clinical groups (Ambrosecchia et al., 2017; Henry, Minassian, Paulus, Geyer, & Perry, 2010; Lavoie et al., 2004; Quintana, Guastella, McGregor, Hickie, & Kemp, 2013).

Procedure

Age, body mass index (BMI), smoking status, demographics, and family history of psychiatric disorders, were recorded for each participant at the time of admission. After arrival at the laboratory, participants completed the Depression Anxiety and Stress Scale-21 (DASS-21; Lovibond & Lovibond, 1995) and the Gambling Severity Assessment Scale (G-SAS; Kim, Grant, Potenza, Blanco, & Hollander, 2009) to assess subthreshold affective symptoms and GD severity over the previous week, respectively. Study took place on two consecutive experimental sessions a day apart, with IA assessment and psychometric testing on the first day and the IGT on the second. Participants were required to abstain from caffeine, tobacco, and alcohol, for 2 h before the experimental sessions. ECG was recorded for the entire duration of IA assessment on Day 1. Moreover, to detect differences in RSA reactivity, a 3-min resting ECG was performed before (Baseline) and after the completion (Recovery) of the IGT on Day 2. Participants were fitted with three 10 mm Ag/AgCl pre-gelled electrodes (ADInstruments, UK) placed on the wrists in an Einthoven's triangle configuration ECG recording. ECG data were converted and amplified with an eight-channel amplifier (PowerLabT26; ADInstruments UK) and displayed, stored, and analyzed with LabChart 7.3.1 software package (ADInstruments Inc, 2011). All tasks were carried out while participants were seated in a quiet and illuminated room. They were instructed to relax and remain as still as possible during recording to minimize motion artifacts.

Measures

Heartbeat detection task

IA was assessed throughout the heartbeat detection task (Schandry, 1981). On the heartbeat detection task, participants were required to silently count their own heartbeats over four different time intervals (25, 35, 45, and 100 s) presented in random order. Time intervals were signaled by an initial audio-visual start cue, followed by a stop cue to indicate the onset and offset of the timed window. After each timing period, the participant was asked to tell the experimenter the number of heartbeats detected. During the task, no feedback on the length of the counting phases or the quality of their performance was given, and participants were not permitted to use any tools or strategies (e.g., feeling pulse on the wrist) that could assist heartbeat counting. IA was calculated from the absolute difference between the estimated and actual number of recorded heartbeats according to the following equation: 1/4∑(1−(|recorded beats− counted beats|)recorded beats). By using this formula, IA score may vary between 0 and 1such that perfect heartbeat tracking is represented by a score of 1 and poor interoception by scores closer to zero.

IGT

All participants were administered a computerized version of the IGT. Subjects were provided with $2000 to start with. The computer screen displayed four rectangular decks. Participants chose a card by clicking on the appropriate deck on each trial of the IGT. Following each draw, a specified amount of virtual play money is awarded ($100 in decks A and B and $50 in decks C and D). However, the turning of some cards also carries an unpredictable penalty (which is large in decks A and B and small in decks C and D). The four decks differ in their long-term outcomes with decks A and B consistently delivering high immediate gains, but leading to greater loss over time, and decks C and D resulting in smaller immediate gains but providing greater gains in the long run. A NET score quantifying the amount of advantageous decision making was calculated as the number of draws from advantageous decks minus that from disadvantageous decks: NET score = (selected cards deck C + selected cards deck D) − (selected cards deck A + selected cards deck B). Accordingly, a score below zero indicates that participants adopted a disadvantageous strategy in the long run (more card selections in decks A and B) whereas a score above zero implied a more advantageous deck preference (more card selections in decks C and D). Following other studies, the NET score was further divided into 5 blocks, each of 20 consecutive card choices. All participants received standard instructions for the IGT. Briefly, they were advised that the task consisted in winning as much as possible and avoiding losses by drawing cards, one at a time, from the four decks. They were informed that each card drawn indicate how much they had won and whether there was also a penalty. They were also instructed that some decks are more advantageous than others and that they are free to switch from one deck to another at any time and as frequently as they liked.

ECG recording

The ECG was sampled at 1 kHz and online filtered with the Mains Filter. The peak of the R-wave of the ECG was detected from each sequential heartbeat and the R-R interval was timed to the nearest msec. R-R intervals were inspected and edited for artifacts. Editing consisted of a software artefacts detection [artefacts threshold 300 msec; LabChart's ECG Analysis module (ADInstruments Inc, 2011)] followed by a visual inspection of the ECG recorded signal. Artefacts were then edited by integer division or summation. The amplitude of RSA was calculated with CMetX, a time-domain method that allows derivation of components of HRV within specified frequency bands as spectral techniques (Berntson et al., 1997). The amplitude of RSA was estimated as the variance of heart rate across the band of frequencies that are associated with spontaneous respiration. RSA estimates were calculated using the following procedures: a) linear interpolation at 10 Hz sampling rate; b) application of a 241-point FIR filter with a 0.12–0.40 Hz bandpass; c) extraction of the band passed variance; d) transformation of the variance in its natural logarithm (Allen, Chambers, & Towers, 2007; Ferri, Ardizzi, Ambrosecchia, & Gallese, 2013). According to guidelines, this procedure was applied to distinct epochs of 30 s (Berntson et al., 1997). RSA-values corresponding to Baseline and Recovery were computed accordingly as the average of the six 30 s epochs. RSA reactivity was operationalized as the change in RSA absolute values [expressed in ln(msec)2] between baseline and recovery.

Statistical analysis

We first compared individuals with GD and HC on demographic, clinical characteristics, and IGT performance on the basis of contingency table/χ2 for categorical measures and Student's T-Test for continuous variables. To detect the presence of significant differences between GD individuals and HCs in phasic changes in cardiac vagal activity and interoception we performed a multivariate analysis of covariance (MANCOVA) using RSA reactivity and IA as dependent variables, group (GD vs. HCs) as independent factor, and DASS-21 total score, age, smoking status, and BMI as covariates. This was necessary in the light of the evidence pointing at an effect of affective symptoms, age, smoking, and BMI on cardiac vagal activity and interoceptive measures (Ambrosecchia et al., 2017; Hina & Aspell, 2019; Laborde et al., 2017; Murphy, Geary, Millgate, Catmur, & Bird, 2018; Pollatos, Traut-Mattausch, & Schandry, 2009). When the initial model was significant, we conducted a series of one-way analyses of covariance (ANCOVA) to test differences between groups on dependent variables. We used a statistical model corrected for multiple comparisons according to the Bonferroni procedure (P < 0.05/number of comparisons) to minimise the likelihood of type I statistical errors. We reported effect sizes using partial eta-squared (η2 p; small effect = 0.01, medium effect = 0.06, large effect = 0.14). To further confirm the presence of significant differences between GD individuals and HCs in resting state RSA, we also conducted supplemental analyses considering repeated measures of cardiac vagal activity from baseline to recovery (please, see Table S4 in supplementary material).

In the second part of the analysis, a linear regression model was adopted to predict the severity of decision-making impairments based on factors that significantly differed between the two groups in univariate/bivariate analysis. Unstandardized betas for effect size were provided. The level of significance was of 5%. Possible multicollinearity between the variables of interest was tested through the variance inflation factor (VIF) indicators. All statistical analysis were performed using SPSS v. 25 (IBM Corp., USA).

Ethics

The study was approved by the local Ethics Committee and was undertaken in accordance with the Principles of Human Rights, as adopted by the World Medical Association at the 18th WMA General Assembly, Helsinki, Finland, June 1964 and subsequently amended at the 64th WMA General Assembly, Fortaleza, Brazil, October 2013. All participants gave their written informed consent to participate in the study after complete explanation of the procedures. Enrolled subjects did not receive any form of payment.

Results

Demographics, clinical features, and IGT performance

As expected from the matching procedure, individuals with GD and HCs did not differ for age. The two groups were similar also for civil status, occupation, living condition, and BMI (Table 1). Individuals with GD and HCs significantly differed for smoking status, family history of psychiatric disorders, and educational level (Table 1). Moreover, Individuals with GD scored significantly higher on the DASS-21 and the G-SAS compared to HCs (Table 1). Unsurprisingly, subjects with GD also performed worse on the IGT with NET total scores significantly lower than HCs (GD: -5.7 ± 22.5, HCs: 22.7 ± 39.2, overall sample = 8.8 ± 34.6, t = 2.89, df = 42, P = 0.006; see also Table S5 in supplementary material).

Table 1.

Clinical and demographic characteristics of the sample

CharacteristicsGD (N = 22)HC (N = 22)Overalldfχ2 or tP
Age (M±SD)47.5 ± 2.640.3 ± 2.844.0 ± 13.21−1.80.071
Body Mass Index (M±SD)25.1 ± 0.824.8 ± 3.924.9 ± 3.91−2.10.834
Education level (n%)19.80.002
 Graduate3 (13.6)13 (59.1)16 (36.4)
 Undergraduate19 (86.4)9 (40.9)28 (63.6)
Living alone (n%)3 (13.6)7 (31.8)10 (22.7)12.10.150
Occupation (n%)12.00.154.
 Employed18 (81.8)21 (95.5)39 (88.6)
 Unemployed4 (18.2)1 (4.5)5 (11.4)
Smoking (n%)20 (90.9)11 (50.0)31 (70.5)18.80.003
Marital status (n%)10.01.00
 Married6 (27.3)6 (27.3)32 (72.7)
 Unmarried16 (72.7)16 (72.7)12 (27.3)
Family history of psychiatric disorders (n%)13 (59.1)1 (4.5)14 (31.8)115.1<0.001
G-SAS (M±SD)21.9 ± 11.11.8 ± 3.011.9 ± 13.01−8.1<0.001
DASS-21 (M±SD)30.1 ± 17.518.3 ± 12.924.2 ± 16.41-2.50.016

Significant results in bold characters.

Abbreviations: M = mean; SD = standard deviation; df, degrees of freedom; χ2, chi-squared test; P, statistical significance; t = Student's t; SD standard deviation; G-SAS = Gambling Symptom Assessment Scale; DASS-21 = Depression, Anxiety and Stress Scale; GD = Gambling Disorder; HC = healthy controls.

RSA and IA

The MANCOVA, indicated a significant global effect (Wilks' Lambda = 0.60, F = 11.49, df = 2, P < 0.001) of variables of interest on the two diagnostic groups. Multivariate normality was respected as indicated by values obtained with Box's Test for Equivalence of Covariance Matrices (χ2 = 3.67, df = 3; P = 0.30). A series of univariate ANCOVAs we performed afterward indicated that patients with GD displayed a significant reduction in RSA reactivity [F = 11.2, df = 1; P = 0.002, η2 p = 0.228; GD: -0.15 ± 0.36 ln(msec)2; HCs: 0.39 ± 0.54 ln(msec)2], as well as significantly decreased IA (F = 5.5, df = 1; P = 0.024, η2 p = 0.126; GD: 0.37 ± 0.3; HCs: 0.61 ± 0.3) compared to HCs, even after controlling for DASS total score, age, smoking status, and BMI. Of note, none of these covariates resulted significant (Table 2). According to the linear regression model, cardiac vagal reactivity and IA also significantly predict decision-making impairments on the IGT. Indeed, both RSA and IA were positively associated with overall NET scores (Table 3). There was no significant multicollinearity in the model, as indicated by the fact that the VIF of all variables of interest was < 3 (O'brien, 2007).

Table 2.

Analysis of covariance for RSA reactivity and IA by DASS-21, BMI, age, and smoking status as covariates

Type III sum of squaresdf1df2Mean squareFPη2p
RSA reactivityGroup2.431382.4311.20.0020.228
DASS-210.241380.241.10.2920.029
BMI0.301380.301.30.2450.035
Age0.051380.050.20.6200.007
Smoking0.241380.241.10.2940.029
IAGroup0.471380.475.50.0240.126
DASS-210.041380.040.50.4740.014
BMI0.011380.000.00.8600.001
Age0.081380.080.90.3400.024
Smoking0.001380.000.00.9000.000

Significant results in bold characters.

Abbreviations: df1 = Degrees of freedom between groups; df2 = Degrees of freedom within groups; F = value of variance of the group means; η2p = partial eta squared measure of effect size; RSA = Respiratory Sinus Arrhythmia; IA = Interoceptive Accuracy; BMI = body mass index; DASS-21 = Depression Anxiety and Stress Scale.

Table 3.

Linear regression: effect of predictors on NET total score

PredictorsEstimateSE95% Confidence IntervaltPβ
LowerUpper
Education level18.769.94−0.0190.5471.8870.0670.263
Family history of psychiatric disorders6.8411.63−0.2280.4140.5880.5600.093
Smoking status−13.9710.38−0.4670.094−1.3460.0187−0.186
RSA reactivity25.679.200.1080.6842.7890.0080.396
IA38.0715.470.0590.6172.4610.0190.338
DASS-210.340.310-0.1340.4591.1070.2750.162
G-SAS0.160.497-0.3153.1380.4410.7390.062

Significant results in bold characters.

Abbreviations: P, statistical significance; SE = standard error; t = t statistic; β = standardized regression coefficient; RSA = Respiratory Sinus Arrhythmia; IA = Interoceptive Accuracy G-SAS = Gambling Symptom Assessment Scale; DASS-21 = Depression, Anxiety and Stress Scale.

Discussion

In line with our hypothesis, patients with GD displayed increased vagal withdrawal as well as reduced IA as compared to HCs. Furthermore, we observed that in our sample both RSA reactivity and IA were significant predictors of decision-making abilities as indexed by the IGT. To the very best of our knowledge, no previous studies have investigated this relationship in GD. A prior study conducted by Kennedy et al. (2019) in non-treatment seeking individuals with problem gambling did not detect significant differences in baseline RSA and several measures of interoception, including IA. However, sample composition of Kennedy and colleagues' study was different from this study in several respects, including an equal distribution between male and female GD subjects, as well as the heterogeneity of gambling activities in which participants engaged. Indeed, there is evidence that GD clinical phenomenology may vary by form of problematic gambling (Petry, 2003) and that GD severity increased with VLT involvement (Delfabbro, King, Browne, & Dowling, 2020) as well as with male gender (González-Ortega, Echeburúa, Corral, Polo-López, & Alberich, 2013), so that it is difficult to draw direct comparison.

The decrease in RSA values we observed among individuals experiencing problems with slot-machines or video lottery terminals gambling may endorse a hypothesis of unbalanced parasympathetic control in GD. The neurovisceral integration model posits that the relation between HRV and cognitive and emotional regulation functions is attributable to the ability of vagally-mediated HRV to index activity in a flexible network of neural structures that is dynamically organized in response to environmental challenges (Thayer and Lane, 2009). The main assumption of this model is that the higher the vagal tone, the better executive cognitive performance, as well as better emotional functioning (Laborde et al., 2017). Indeed, there is evidence that phasic increases in HRV on tasks that require affective or executive processes facilitate effective emotional and cognitive regulation (Thayer & Lane, 2009). Besides, decreased resting HRV has been widely reported in subjects with substance use disorders (Crowell, Price, Puzia, Yaptangco, & Cheng, 2017; Quintana, McGregor, Guastella, Malhi, & Kemp, 2013). Alternatively, the finding of increased vagal withdrawal we observed in GD group during the IGT could be also attributable to the fact that individuals experiencing problem gambling may pay more attention to monetary cues, as decreases in RSA have been observed in tasks requiring sustained attention (Duschek, Muckenthaler, Werner, & del Paso, 2009; Porges & Raskin, 1969).

Our findings of decreased IA in individuals with GD also provide additional support for theories that emphasize the role of aberrant interoceptive processing in addictive disorders (Paulus & Stewart, 2014). Given that gambling reinforcing effects result in marked changes in bodily arousal (Sharpe et al., 1995), it is plausible that interoceptive processes may be implicated in GD clinical phenomenology. Moreover, trait individual differences in cardiac perception have been linked to a number of cognitive and affective phenomena, including time perception, anxiety and depressive symptoms, emotional reactivity and memory, alexithymia, as well as intuitive decision-making (Verdejo-Garcia et al., 2012). Of note, several of these dimensions are affected in subjects with GD (Bibby & Ross, 2017; Di Nicola, Pepe et al., 2020; Limbrick-Oldfield et al., 2020; Pettorruso et al., 2019; Rogier & Velotti, 2018), consistent with a key role of interoceptive processes in addictive disorders.

The findings of disadvantageous decision-making on the IGT, in combination with the predictive role of RSA reactivity and IA on NET total score are consistent with studies indicating deficient peripherical somatic processing signals in GD individuals (Lole & Gonsalvez, 2017; Lole, Gonsalvez, Barry, & Blaszczynski, 2014; Ulrich, Ambach, & Hewig, 2016). The somatic marker hypothesis provides a system-level framework describing how decision-making processes are shaped by emotional signals arising from peripherical changes in bodily arousal (Damasio, 1994). This bodily biofeedback may represent an influential embodied somatosensory pattern in the selection of adaptive behavior, giving rise to implicit or explicit knowledge for making advantageous decisions, and thus promoting self-regulation (Gallese & Sinigaglia, 2010; Verdejo-García & Bechara, 2009).

Before summarizing study conclusions, we must acknowledge some potential limitations. First, the relatively small sample size does not allow to extent the generalizability of our result to the whole population of individuals with GD. Second, to ensure the conceptual and methodological validity of the study, a sample of male subjects with GD addicted to slot-machines or VLT and without psychiatric comorbidity was selected. Accordingly, this issue might have led to a selection bias. However, selecting a well-characterized clinical group of subjects with GD can be also considered as a study strength. Finally, IA evaluation relied upon behavioral assessment and lacked association with self-report measurement of interoception.

Conclusions

Despite the above-mentioned limitations, this is the first study to detect the presence of significant abnormalities in RSA reactivity and IA among a homogeneous sample of individuals with GD. The finding reported here may have practical implications, as HRV-based rehabilitation programs may represent a promising venue in the treatment of addictive disorders, including GD (Di Nicola, Pepe et al., 2020; Eddie, Vaschillo, Vaschillo, & Lehrer, 2015). Moreover, based on our findings, a broader exploration relying upon an embodiment-informed framework (Miller, Kiverstein, & Rietveld, 2020) may contribute to shed further light on the clinical phenomenology of the disorder.

Funding sources

No financial support was received for this study.

Authors' contribution

Conceptualization, LM; methodology, LM, AMS, MQ, VG, MDN; software, LM, VDM; formal analysis, LM, MQ, DJ; data curation, LM, VDM, MQ, GR; writing original draft preparation, LM, DJ; review and editing, AMS, MQ, PV, GR, MDN, VG; supervision, AMS, PV, MDN, GS, LJ.; project administration, MDN, AMS, PV, LJ, GS.

Conflict of interest

The authors declare no conflict of interest.

References

  • Allen, J. J. , Chambers, A. S. , & Towers, D. N. (2007). The many metrics of cardiac chronotropy: A pragmatic primer and a brief comparison of metrics. Biological Psychology, 74(2), 243262. https://doi.org/10.1016/j.biopsycho.2006.08.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ambrosecchia, M. , Ardizzi, M. , Russo, E. , Ditaranto, F. , Speciale, M. , Vinai, P. , … Gallese, V. (2017). Interoception and autonomic correlates during social interactions. Implications for anorexia. Frontiers in Human Neuroscience, 11, 219. https://doi.org/10.3389/fnhum.2017.00219.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bechara, A. , & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 52(2), 336372. https://doi.org/10.1016/j.geb.2004.06.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bechara, A. , Damasio, A. R. , Damasio, H. , & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1–3), 715. https://doi.org/10.1016/0010-0277(94)90018-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berntson, G. G. , Bigger, J. T., Jr , Eckberg, D. L. , Grossman, P. , Kaufmann, P. G. , Malik, M. , … van der Molen, M. W. (1997). Heart rate variability: Origins, methods, and interpretive caveats. Psychophysiology, 34(6), 623648. https://doi.org/10.1111/j.1469-8986.1997.tb02140.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bibby, P. A. , & Ross, K. E. (2017). Alexithymia predicts loss chasing for people at risk for problem gambling. Journal of Behavioral Addictions, 6(4), 630638. https://doi.org/10.1556/2006.6.2017.076.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brevers, D. , Bechara, A. , Cleeremans, A. , & Noël, X. (2013). Iowa gambling task (IGT): Twenty years after – Gambling disorder and IGT. Frontiers in Psychology, 4, 665. https://doi.org/10.3389/fpsyg.2013.00665.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brevers, D. , Koritzky, G. , Bechara, A. , & Noël, X. (2014). Cognitive processes underlying impaired decision-making under uncertainty in gambling disorder. Addictive Behaviors, 39(10), 15331536. https://doi.org/10.1016/j.addbeh.2014.06.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brevers, D. , & Noël, X. (2013). Pathological gambling and the loss of willpower: A neurocognitive perspective. Socioaffective Neuroscience & Psychology, 3, 21592. https://doi.org/10.3402/snp.v3i0.21592.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, L. , Studer, B. , Bruss, J. , Tranel, D. , & Bechara, A. (2014). Damage to insula abolishes cognitive distortions during simulated gambling. Proceedings of the National Academy of Sciences of the United States of America, 111(16), 60986103. https://doi.org/10.1073/pnas.132229511.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crowell, S. E. , Price, C. J. , Puzia, M. E. , Yaptangco, M. , & Cheng, S. C. (2017). Emotion dysregulation and autonomic responses to film, rumination, and body awareness: Extending psychophysiological research to a naturalistic clinical setting and a chemically dependent female sample. Psychophysiology, 54(5), 713723. https://doi.org/10.1111/psyp.12838.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Damasio, A. R. (1994). Descartes' error: Emotion, reason and the human brain. New York, NY: Avon.

  • Delfabbro, P. , King, D. L. , Browne, M. , & Dowling, N. A. (2020). Do EGMs have a stronger association with problem gambling than racing and casino table games? Evidence from a decade of Australian prevalence studies. Journal of Gambling Studies, 36(2), 499511. https://doi.org/10.1007/s10899-020-09950-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Di Nicola, M. , De Crescenzo, F. , D'Alò, G. L. , Remondi, C. , Panaccione, I. , Moccia, L. , … Janiri, L. (2020). Pharmacological and psychosocial treatment of adults with gambling disorder: A meta-review. Journal of Addiction Medicine, 14(4), e15e23. https://doi.org/10.1097/ADM.0000000000000574.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Di Nicola, M. , Pepe, M. , Modica, M. , Lanzotti, P. , Panaccione, I. , Moccia, L. , & Janiri, L. (2020). Mixed states in patients with substance and behavioral addictions. The Psychiatric Clinics of North America, 43(1), 127137. https://doi.org/10.1016/j.psc.2019.10.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drucaroff, L. J. , Kievit, R. , Guinjoan, S. M. , Gerschcovich, E. R. , Cerquetti, D. , Leiguarda, R. , … Vigo, D. E. (2011). Higher autonomic activation predicts better performance in Iowa gambling task. Cognitive and Behavioral Neurology: Official Journal of the Society for Behavioral and Cognitive Neurology, 24(2), 9398. https://doi.org/10.1097/WNN.0b013e3182239308.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dulleck, U. , Ristl, A. , Schaffner, M. , & Torgler, B. (2011). Heart rate variability, the autonomic nervous system, and neuroeconomic experiments. Journal of Neuroscience, Psychology, and Economics, 4(2), 117. https://doi.org/10.1037/a0022245.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunn, B. D. , Galton, H. C. , Morgan, R. , Evans, D. , Oliver, C. , Meyer, M. , … Dalgleish, T. (2010). Listening to your heart. How interoception shapes emotion experience and intuitive decision making. Psychological Science, 21(12), 18351844. https://doi.org/10.1177/0956797610389191.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duschek, S. , Muckenthaler, M. , Werner, N. , & del Paso, G. A. (2009). Relationships between features of autonomic cardiovascular control and cognitive performance. Biological Psychology, 81(2), 110117. https://doi.org/10.1016/j.biopsycho.2009.03.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eddie, D. , Vaschillo, E. , Vaschillo, B. , & Lehrer, P. (2015). Heart rate variability biofeedback: Theoretical basis, delivery, and its potential for the treatment of substance use disorders. Addiction Research & Theory, 23(4), 266272. https://doi.org/10.3109/16066359.2015.1011625.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferri, F. , Ardizzi, M. , Ambrosecchia, M. , & Gallese, V. (2013). Closing the gap between the inside and the outside: Interoceptive sensitivity and social distances. Plos One, 8(10), e75758. https://doi.org/10.1371/journal.pone.0075758.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • First, M. , Gibbon, M. , Spitzer, R. , Williams, J. , & Benjamin, L. (1997). Structured clinical interview for DSM-IV Axis II personality disorders, (SCID-II). Washington, DC: American Psychiatric Pub.

    • Search Google Scholar
    • Export Citation
  • First, M. , Spitzer, R. , Gibbon, M. , & Williams, J. (2002). Structured clinical interview for DSM-IV-TR Axis I disorders, research version, non-patient edition. (SCID-I/NP). New York: Biometrics Research, New York State Psychiatric Institute .

    • Search Google Scholar
    • Export Citation
  • First, M. , Williams, J. , Benjamin, L. , & Spitzer, R. (2016). Structured clinical interview for DSM-5 personality disorders: SCID-5-PD. Am Psychiatr Assoc Publ Arlington, VA.

    • Search Google Scholar
    • Export Citation
  • First, M. B. , Williams, J. B. W. , Karg, R. S. , & Spitzer, R. L. (2017). Structured clinical interview for DSM‐5 disorders, clinician version (SCID‐5‐CV). Artmed, Porto Alegre.

    • Search Google Scholar
    • Export Citation
  • Gallese, V. , & Sinigaglia, C. (2010). The bodily self as power for action. Neuropsychologia, 48(3), 746755. https://doi.org/10.1016/j.neuropsychologia.2009.09.038.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garfinkel, S. N. , Seth, A. K. , Barrett, A. B. , Suzuki, K. , & Critchley, H. D. (2015). Knowing your own heart: Distinguishing interoceptive accuracy from interoceptive awareness. Biological Psychology, 104, 6574. https://doi.org/10.1016/j.biopsycho.2014.11.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • González-Ortega, I. , Echeburúa, E. , Corral, P. , Polo-López, R. , & Alberich, S. (2013). Predictors of pathological gambling severity taking gender differences into account. European Addiction Research, 19(3), 146154. https://doi.org/10.1159/000342311.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goudriaan, A. E. , Oosterlaan, J. , de Beurs, E. , & van den Brink, W. (2006). Psychophysiological determinants and concomitants of deficient decision making in pathological gamblers. Drug and Alcohol Dependence, 84(3), 231239. https://doi.org/10.1016/j.drugalcdep.2006.02.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henry, B. L. , Minassian, A. , Paulus, M. P. , Geyer, M. A. , & Perry, W. (2010). Heart rate variability in bipolar mania and schizophrenia. Journal of Psychiatric Research, 44(3), 168176. https://doi.org/10.1016/j.jpsychires.2009.07.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hina, F. , & Aspell, J. E. (2019). Altered interoceptive processing in smokers: Evidence from the heartbeat tracking task. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 142, 1016. https://doi.org/10.1016/j.ijpsycho.2019.05.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kennedy, D. , Goshko, C. B. , Murch, W. S. , Limbrick-Oldfield, E. H. , Dunn, B. D. , & Clark, L. (2019). Interoception and respiratory sinus arrhythmia in gambling disorder. Psychophysiology, 56(6), e13333. https://doi.org/10.1111/psyp.13333.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, S. W. , Grant, J. E. , Potenza, M. N. , Blanco, C. , & Hollander, E. (2009). The gambling symptom assessment Scale (G-SAS): A reliability and validity study. Psychiatry Research, 166(1), 7684. https://doi.org/10.1016/j.psychres.2007.11.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laborde, S. , Mosley, E. , & Thayer, J. F. (2017). Heart rate variability and cardiac vagal tone in psychophysiological research – Recommendations for experiment planning, data analysis, and data reporting. Frontiers in Psychology, 8, 213. https://doi.org/10.3389/fpsyg.2017.00213.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laborde, S. , Raab, M. , & Kinrade, N. P. (2014). Is the ability to keep your mind sharp under pressure reflected in your heart? Evidence for the neurophysiological bases of decision reinvestment. Biological Psychology, 100, 3442. https://doi.org/10.1016/j.biopsycho.2014.05.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavoie, K. L. , Fleet, R. P. , Laurin, C. , Arsenault, A. , Miller, S. B. , & Bacon, S. L. (2004). Heart rate variability in coronary artery disease patients with and without panic disorder. Psychiatry Research, 128(3), 289299. https://doi.org/10.1016/j.psychres.2004.06.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Limbrick-Oldfield, E. H. , Cherkasova, M. V. , Kennedy, D. , Goshko, C. B. , Griffin, D. , Barton, J. , & Clark, L. (2020). Gambling disorder is associated with reduced sensitivity to expected value during risky choice. Journal of Behavioral Addictions, Advance online publication. https://doi.org/10.1556/2006.2020.00088.

    • Search Google Scholar
    • Export Citation
  • Lole, L. , & Gonsalvez, C. J. (2017). Does size matter? An examination of problem gamblers' skin conductance responses to large and small magnitude rewards. Psychophysiology, 54(10), 15411548. https://doi.org/10.1111/psyp.12897.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lole, L. , Gonsalvez, C. J. , Barry, R. J. , & Blaszczynski, A. (2014). Problem gamblers are hyposensitive to wins: An analysis of skin conductance responses during actual gambling on electronic gaming machines. Psychophysiology, 51(6), 556564. https://doi.org/10.1111/psyp.12198.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lovibond, P. F. , & Lovibond, S. H. (1995). Manual for the depression anxiety stress scales (2nd ed.). Sydney, Australia: Psychology Foundation of Australia.

    • Search Google Scholar
    • Export Citation
  • Miller, M. , Kiverstein, J. , & Rietveld, E. (2020). Embodying addiction: A predictive processing account. Brain and Cognition, 138, 105495. https://doi.org/10.1016/j.bandc.2019.105495.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moccia, L. , Pettorruso, M. , De Crescenzo, F. , De Risio, L. , di Nuzzo, L. , Martinotti, G. , … Di Nicola, M. (2017). Neural correlates of cognitive control in gambling disorder: A systematic review of fMRI studies. Neuroscience and Biobehavioral Reviews, 78, 104116. https://doi.org/10.1016/j.neubiorev.2017.04.025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murphy, J. , Geary, H. , Millgate, E. , Catmur, C. , & Bird, G. (2018). Direct and indirect effects of age on interoceptive accuracy and awareness across the adult lifespan. Psychonomic Bulletin & Review, 25(3), 11931202. https://doi.org/10.3758/s13423-017-1339-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakamura, Y. , Yamamoto, Y. , & Muraoka, I. (1993). Autonomic control of heart rate during physical exercise and fractal dimension of heart rate variability. Journal of Applied Physiology (Bethesda, Md.: 1985), 74(2), 875881. https://doi.org/10.1152/jappl.1993.74.2.875.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O'brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673-690.

  • Paulus, M. P. , & Stewart, J. L. (2014). Interoception and drug addiction. Neuropharmacology, 76 Pt B(0), 342350. https://doi.org/10.1016/j.neuropharm.2013.07.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petry N. M. (2003). A comparison of treatment-seeking pathological gamblers based on preferred gambling activity. Addiction (Abingdon, England), 98(5), 645655. https://doi.org/10.1046/j.1360-0443.2003.00336.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pettorruso, M. , Martinotti, G. , Cocciolillo, F. , De Risio, L. , Cinquino, A. , Di Nicola, M. , … Di Giuda, D. (2019). Striatal presynaptic dopaminergic dysfunction in gambling disorder: A 123 I-FP-CIT SPECT study. Addiction Biology, 24(5), 10771086. https://doi.org/10.1111/adb.12677.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pollatos, O. , Traut-Mattausch, E. , & Schandry, R. (2009). Differential effects of anxiety and depression on interoceptive accuracy. Depression and Anxiety, 26(2), 167173. https://doi.org/10.1002/da.20504.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Porges, S. W. , & Raskin, D. C. (1969). Respiratory and heart rate components of attention. Journal of Experimental Psychology, 81(3), 497503. https://doi.org/10.1037/h0027921.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quintana, D. S. , Alvares, G. A. , & Heathers, J. A. (2016). Guidelines for reporting articles on psychiatry and heart rate variability (GRAPH): Recommendations to advance research communication. Translational Psychiatry, 6(5), e803. https://doi.org/10.1038/tp.2016.73.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quintana, D. S. , Guastella, A. J. , McGregor, I. S. , Hickie, I. B. , & Kemp, A. H. (2013). Heart rate variability predicts alcohol craving in alcohol dependent outpatients: Further evidence for HRV as a psychophysiological marker of self-regulation. Drug and Alcohol Dependence, 132(1–2), 395398. https://doi.org/10.1016/j.drugalcdep.2013.02.025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quintana, D. S. , McGregor, I. S. , Guastella, A. J. , Malhi, G. S. , & Kemp, A. H. (2013). A meta-analysis on the impact of alcohol dependence on short-term resting-state heart rate variability: Implications for cardiovascular risk. Alcoholism, Clinical and Experimental Research, 37(Suppl. 1), E23E29. https://doi.org/10.1111/j.1530-0277.2012.01913.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raven, J. (2000). The Raven’s progressive matrices: Change and stability over culture and time. Cognitive Psychology, 41(1), 148. https://doi.org/10.1006/cogp.1999.0735.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rochat, L. , Maurage, P. , Heeren, A. , & Billieux, J. (2019). Let's open the decision-making umbrella: A framework for conceptualizing and assessing features of impaired decision making in addiction. Neuropsychology Review, 29(1), 2751. https://doi.org/10.1007/s11065-018-9387-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogier, G. , & Velotti, P. (2018). Conceptualizing gambling disorder with the process model of emotion regulation. Journal of Behavioral Addictions, 7(2), 239251. https://doi.org/10.1556/2006.7.2018.52.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schandry R. . (1981). Heart beat perception and emotional experience. Psychophysiology, 18(4), 483488. https://doi.org/10.1111/j.1469-8986.1981.tb02486.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sharpe L. . (2004). Patterns of autonomic arousal in imaginal situations of winning and losing in problem gambling. Journal of Gambling Studies, 20(1), 95104. https://doi.org/10.1023/B:JOGS.0000016706.96540.43.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sharpe, L. , Tarrier, N. , Schotte, D. , & Spence, S. H. (1995). The role of autonomic arousal in problem gambling. Addiction (Abingdon, England), 90(11), 15291540. https://doi.org/10.1046/j.1360-0443.1995.9011152911.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thayer, J. F. , & Lane, R. D. (2009). Claude Bernard and the heart-brain connection: Further elaboration of a model of neurovisceral integration. Neuroscience and Biobehavioral Reviews, 33(2), 8188. https://doi.org/10.1016/j.neubiorev.2008.08.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ulrich, N. , Ambach, W. , & Hewig, J. (2016). Severity of gambling problems modulates autonomic reactions to near outcomes in gambling. Biological Psychology, 119, 1120. https://doi.org/10.1016/j.biopsycho.2016.06.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verdejo-García, A. , & Bechara, A. (2009). A somatic marker theory of addiction. Neuropharmacology, 56(Suppl. 1), 4862. https://doi.org/10.1016/j.neuropharm.2008.07.035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verdejo-Garcia, A. , Clark, L. , & Dunn, B. D. (2012). The role of interoception in addiction: A critical review. Neuroscience and Biobehavioral Reviews, 36(8), 18571869. https://doi.org/10.1016/j.neubiorev.2012.05.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Winstanley, C. A. , & Clark, L. (2016). Translational models of gambling-related decision-making. Current Topics in Behavioral Neurosciences, 28, 93120. https://doi.org/10.1007/7854_2015_5014.

    • Crossref
    • Search Google Scholar
    • Export Citation

Supplemental methods

Supplementary statistical analyses

In the main analyses we compared GD individuals and HCs in RSA reactivity, which was operationalized as the change in RSA absolute values between the two conditions of baseline and recovery. To further confirm the presence of significant differences between GD individuals and HCs in RSA, we conducted a one-way analysis of covariance (ANCOVA) with repeated measures to compare group means from baseline to recovery. Specifically, we set the diagnostic group (GD vs. HC) as between factor, the condition (Baseline vs. Recovery) as within factor, and DASS-21 total score, age, smoking status, and BMI as covariates, to generate F value and its associated significance level for within-subjects effects. Associated effect sizes (partial eta squared) were also reported. The level of significance was set at 5%.

Supplemental results

An ANCOVA with repeated measures determined that mean RSA significantly differed between baseline [GD: 4.35 ± 1.62 ln(msec)2; HCs: 5.29 ± 1.16 ln(msec)2] and recovery [GD: 4.20 ± 1.68 ln(msec)2; HCs: 5.69 ± 1.12 ln(msec)2] in the two diagnostic groups (P = 0.002), even after controlling for DASS total score, age, smoking status, and BMI. Of note, none of these covariates resulted significant (Table S4 and Figure S1).

Table S4.

Repeated Measure ANCOVA (within subjects effects)

Type III sum of squaresdf1df2Mean squareFpη²p
Condition0.111380.111.00.3140.027
Condition * Age0.021380.020.20.6190.007
Condition * DASS-210.121380.121.10.2910.029
Condition * Group1.211381.2111.10.0020.228
Condition * Smoking0.121380.121.10.2940.029
Condition * BMI0.151380.151.30.2450.035

Significant results in bold characters.

Abbreviations: df1=Degrees of freedom between groups; df2=Degrees of freedom within groups; p=statistical significance; F = value of variance of the group means; η²p = partial eta squared measure of effect size; BMI = body mass index; DASS-21=Depression Anxiety and Stress Scale.

Table S5.

IGT NET scores of GD and HC groups

IGT NET (M ± SD)GD

(N = 22)
HC

(N = 22)
Overall
NET 1−2.5 ± 5.6−1.9 ± 7.2−2.2 ± 6.4
NET 2−1.1 ± 5.15.7 ± 9.12.4 ± 8.1
NET 3−1.1 ± 6.97.0 ± 10.13.1 ± 9.5
NET 4−0.4 ± 7.96.9 ± 11.23.4 ± 10.4
NET 5−0.4 ± 8.05.4 ± 12.92.6 ± 11.1

Abbreviations: M = mean; SD = standard deviation; IGT = Iowa Gambling Task; GD = Gambling Disorder; HC = healthy controls.

Fig. S1.
Fig. S1.

Estimated marginal means and standard errors of RSA adjusted for age, DASS-21 total scores, smoking status, and BMI during baseline and recovery in HC and GD groups

Citation: Journal of Behavioral Addictions 10, 3; 10.1556/2006.2021.00067

  • Allen, J. J. , Chambers, A. S. , & Towers, D. N. (2007). The many metrics of cardiac chronotropy: A pragmatic primer and a brief comparison of metrics. Biological Psychology, 74(2), 243262. https://doi.org/10.1016/j.biopsycho.2006.08.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ambrosecchia, M. , Ardizzi, M. , Russo, E. , Ditaranto, F. , Speciale, M. , Vinai, P. , … Gallese, V. (2017). Interoception and autonomic correlates during social interactions. Implications for anorexia. Frontiers in Human Neuroscience, 11, 219. https://doi.org/10.3389/fnhum.2017.00219.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bechara, A. , & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 52(2), 336372. https://doi.org/10.1016/j.geb.2004.06.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bechara, A. , Damasio, A. R. , Damasio, H. , & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1–3), 715. https://doi.org/10.1016/0010-0277(94)90018-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berntson, G. G. , Bigger, J. T., Jr , Eckberg, D. L. , Grossman, P. , Kaufmann, P. G. , Malik, M. , … van der Molen, M. W. (1997). Heart rate variability: Origins, methods, and interpretive caveats. Psychophysiology, 34(6), 623648. https://doi.org/10.1111/j.1469-8986.1997.tb02140.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bibby, P. A. , & Ross, K. E. (2017). Alexithymia predicts loss chasing for people at risk for problem gambling. Journal of Behavioral Addictions, 6(4), 630638. https://doi.org/10.1556/2006.6.2017.076.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brevers, D. , Bechara, A. , Cleeremans, A. , & Noël, X. (2013). Iowa gambling task (IGT): Twenty years after – Gambling disorder and IGT. Frontiers in Psychology, 4, 665. https://doi.org/10.3389/fpsyg.2013.00665.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brevers, D. , Koritzky, G. , Bechara, A. , & Noël, X. (2014). Cognitive processes underlying impaired decision-making under uncertainty in gambling disorder. Addictive Behaviors, 39(10), 15331536. https://doi.org/10.1016/j.addbeh.2014.06.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brevers, D. , & Noël, X. (2013). Pathological gambling and the loss of willpower: A neurocognitive perspective. Socioaffective Neuroscience & Psychology, 3, 21592. https://doi.org/10.3402/snp.v3i0.21592.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, L. , Studer, B. , Bruss, J. , Tranel, D. , & Bechara, A. (2014). Damage to insula abolishes cognitive distortions during simulated gambling. Proceedings of the National Academy of Sciences of the United States of America, 111(16), 60986103. https://doi.org/10.1073/pnas.132229511.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crowell, S. E. , Price, C. J. , Puzia, M. E. , Yaptangco, M. , & Cheng, S. C. (2017). Emotion dysregulation and autonomic responses to film, rumination, and body awareness: Extending psychophysiological research to a naturalistic clinical setting and a chemically dependent female sample. Psychophysiology, 54(5), 713723. https://doi.org/10.1111/psyp.12838.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Damasio, A. R. (1994). Descartes' error: Emotion, reason and the human brain. New York, NY: Avon.

  • Delfabbro, P. , King, D. L. , Browne, M. , & Dowling, N. A. (2020). Do EGMs have a stronger association with problem gambling than racing and casino table games? Evidence from a decade of Australian prevalence studies. Journal of Gambling Studies, 36(2), 499511. https://doi.org/10.1007/s10899-020-09950-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Di Nicola, M. , De Crescenzo, F. , D'Alò, G. L. , Remondi, C. , Panaccione, I. , Moccia, L. , … Janiri, L. (2020). Pharmacological and psychosocial treatment of adults with gambling disorder: A meta-review. Journal of Addiction Medicine, 14(4), e15e23. https://doi.org/10.1097/ADM.0000000000000574.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Di Nicola, M. , Pepe, M. , Modica, M. , Lanzotti, P. , Panaccione, I. , Moccia, L. , & Janiri, L. (2020). Mixed states in patients with substance and behavioral addictions. The Psychiatric Clinics of North America, 43(1), 127137. https://doi.org/10.1016/j.psc.2019.10.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drucaroff, L. J. , Kievit, R. , Guinjoan, S. M. , Gerschcovich, E. R. , Cerquetti, D. , Leiguarda, R. , … Vigo, D. E. (2011). Higher autonomic activation predicts better performance in Iowa gambling task. Cognitive and Behavioral Neurology: Official Journal of the Society for Behavioral and Cognitive Neurology, 24(2), 9398. https://doi.org/10.1097/WNN.0b013e3182239308.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dulleck, U. , Ristl, A. , Schaffner, M. , & Torgler, B. (2011). Heart rate variability, the autonomic nervous system, and neuroeconomic experiments. Journal of Neuroscience, Psychology, and Economics, 4(2), 117. https://doi.org/10.1037/a0022245.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunn, B. D. , Galton, H. C. , Morgan, R. , Evans, D. , Oliver, C. , Meyer, M. , … Dalgleish, T. (2010). Listening to your heart. How interoception shapes emotion experience and intuitive decision making. Psychological Science, 21(12), 18351844. https://doi.org/10.1177/0956797610389191.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duschek, S. , Muckenthaler, M. , Werner, N. , & del Paso, G. A. (2009). Relationships between features of autonomic cardiovascular control and cognitive performance. Biological Psychology, 81(2), 110117. https://doi.org/10.1016/j.biopsycho.2009.03.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eddie, D. , Vaschillo, E. , Vaschillo, B. , & Lehrer, P. (2015). Heart rate variability biofeedback: Theoretical basis, delivery, and its potential for the treatment of substance use disorders. Addiction Research & Theory, 23(4), 266272. https://doi.org/10.3109/16066359.2015.1011625.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferri, F. , Ardizzi, M. , Ambrosecchia, M. , & Gallese, V. (2013). Closing the gap between the inside and the outside: Interoceptive sensitivity and social distances. Plos One, 8(10), e75758. https://doi.org/10.1371/journal.pone.0075758.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • First, M. , Gibbon, M. , Spitzer, R. , Williams, J. , & Benjamin, L. (1997). Structured clinical interview for DSM-IV Axis II personality disorders, (SCID-II). Washington, DC: American Psychiatric Pub.

    • Search Google Scholar
    • Export Citation
  • First, M. , Spitzer, R. , Gibbon, M. , & Williams, J. (2002). Structured clinical interview for DSM-IV-TR Axis I disorders, research version, non-patient edition. (SCID-I/NP). New York: Biometrics Research, New York State Psychiatric Institute .

    • Search Google Scholar
    • Export Citation
  • First, M. , Williams, J. , Benjamin, L. , & Spitzer, R. (2016). Structured clinical interview for DSM-5 personality disorders: SCID-5-PD. Am Psychiatr Assoc Publ Arlington, VA.

    • Search Google Scholar
    • Export Citation
  • First, M. B. , Williams, J. B. W. , Karg, R. S. , & Spitzer, R. L. (2017). Structured clinical interview for DSM‐5 disorders, clinician version (SCID‐5‐CV). Artmed, Porto Alegre.

    • Search Google Scholar
    • Export Citation
  • Gallese, V. , & Sinigaglia, C. (2010). The bodily self as power for action. Neuropsychologia, 48(3), 746755. https://doi.org/10.1016/j.neuropsychologia.2009.09.038.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garfinkel, S. N. , Seth, A. K. , Barrett, A. B. , Suzuki, K. , & Critchley, H. D. (2015). Knowing your own heart: Distinguishing interoceptive accuracy from interoceptive awareness. Biological Psychology, 104, 6574. https://doi.org/10.1016/j.biopsycho.2014.11.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • González-Ortega, I. , Echeburúa, E. , Corral, P. , Polo-López, R. , & Alberich, S. (2013). Predictors of pathological gambling severity taking gender differences into account. European Addiction Research, 19(3), 146154. https://doi.org/10.1159/000342311.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goudriaan, A. E. , Oosterlaan, J. , de Beurs, E. , & van den Brink, W. (2006). Psychophysiological determinants and concomitants of deficient decision making in pathological gamblers. Drug and Alcohol Dependence, 84(3), 231239. https://doi.org/10.1016/j.drugalcdep.2006.02.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henry, B. L. , Minassian, A. , Paulus, M. P. , Geyer, M. A. , & Perry, W. (2010). Heart rate variability in bipolar mania and schizophrenia. Journal of Psychiatric Research, 44(3), 168176. https://doi.org/10.1016/j.jpsychires.2009.07.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hina, F. , & Aspell, J. E. (2019). Altered interoceptive processing in smokers: Evidence from the heartbeat tracking task. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 142, 1016. https://doi.org/10.1016/j.ijpsycho.2019.05.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kennedy, D. , Goshko, C. B. , Murch, W. S. , Limbrick-Oldfield, E. H. , Dunn, B. D. , & Clark, L. (2019). Interoception and respiratory sinus arrhythmia in gambling disorder. Psychophysiology, 56(6), e13333. https://doi.org/10.1111/psyp.13333.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, S. W. , Grant, J. E. , Potenza, M. N. , Blanco, C. , & Hollander, E. (2009). The gambling symptom assessment Scale (G-SAS): A reliability and validity study. Psychiatry Research, 166(1), 7684. https://doi.org/10.1016/j.psychres.2007.11.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laborde, S. , Mosley, E. , & Thayer, J. F. (2017). Heart rate variability and cardiac vagal tone in psychophysiological research – Recommendations for experiment planning, data analysis, and data reporting. Frontiers in Psychology, 8, 213. https://doi.org/10.3389/fpsyg.2017.00213.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laborde, S. , Raab, M. , & Kinrade, N. P. (2014). Is the ability to keep your mind sharp under pressure reflected in your heart? Evidence for the neurophysiological bases of decision reinvestment. Biological Psychology, 100, 3442. https://doi.org/10.1016/j.biopsycho.2014.05.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavoie, K. L. , Fleet, R. P. , Laurin, C. , Arsenault, A. , Miller, S. B. , & Bacon, S. L. (2004). Heart rate variability in coronary artery disease patients with and without panic disorder. Psychiatry Research, 128(3), 289299. https://doi.org/10.1016/j.psychres.2004.06.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Limbrick-Oldfield, E. H. , Cherkasova, M. V. , Kennedy, D. , Goshko, C. B. , Griffin, D. , Barton, J. , & Clark, L. (2020). Gambling disorder is associated with reduced sensitivity to expected value during risky choice. Journal of Behavioral Addictions, Advance online publication. https://doi.org/10.1556/2006.2020.00088.

    • Search Google Scholar
    • Export Citation
  • Lole, L. , & Gonsalvez, C. J. (2017). Does size matter? An examination of problem gamblers' skin conductance responses to large and small magnitude rewards. Psychophysiology, 54(10), 15411548. https://doi.org/10.1111/psyp.12897.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lole, L. , Gonsalvez, C. J. , Barry, R. J. , & Blaszczynski, A. (2014). Problem gamblers are hyposensitive to wins: An analysis of skin conductance responses during actual gambling on electronic gaming machines. Psychophysiology, 51(6), 556564. https://doi.org/10.1111/psyp.12198.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lovibond, P. F. , & Lovibond, S. H. (1995). Manual for the depression anxiety stress scales (2nd ed.). Sydney, Australia: Psychology Foundation of Australia.

    • Search Google Scholar
    • Export Citation
  • Miller, M. , Kiverstein, J. , & Rietveld, E. (2020). Embodying addiction: A predictive processing account. Brain and Cognition, 138, 105495. https://doi.org/10.1016/j.bandc.2019.105495.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moccia, L. , Pettorruso, M. , De Crescenzo, F. , De Risio, L. , di Nuzzo, L. , Martinotti, G. , … Di Nicola, M. (2017). Neural correlates of cognitive control in gambling disorder: A systematic review of fMRI studies. Neuroscience and Biobehavioral Reviews, 78, 104116. https://doi.org/10.1016/j.neubiorev.2017.04.025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murphy, J. , Geary, H. , Millgate, E. , Catmur, C. , & Bird, G. (2018). Direct and indirect effects of age on interoceptive accuracy and awareness across the adult lifespan. Psychonomic Bulletin & Review, 25(3), 11931202. https://doi.org/10.3758/s13423-017-1339-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakamura, Y. , Yamamoto, Y. , & Muraoka, I. (1993). Autonomic control of heart rate during physical exercise and fractal dimension of heart rate variability. Journal of Applied Physiology (Bethesda, Md.: 1985), 74(2), 875881. https://doi.org/10.1152/jappl.1993.74.2.875.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O'brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673-690.

  • Paulus, M. P. , & Stewart, J. L. (2014). Interoception and drug addiction. Neuropharmacology, 76 Pt B(0), 342350. https://doi.org/10.1016/j.neuropharm.2013.07.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petry N. M. (2003). A comparison of treatment-seeking pathological gamblers based on preferred gambling activity. Addiction (Abingdon, England), 98(5), 645655. https://doi.org/10.1046/j.1360-0443.2003.00336.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pettorruso, M. , Martinotti, G. , Cocciolillo, F. , De Risio, L. , Cinquino, A. , Di Nicola, M. , … Di Giuda, D. (2019). Striatal presynaptic dopaminergic dysfunction in gambling disorder: A 123 I-FP-CIT SPECT study. Addiction Biology, 24(5), 10771086. https://doi.org/10.1111/adb.12677.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pollatos, O. , Traut-Mattausch, E. , & Schandry, R. (2009). Differential effects of anxiety and depression on interoceptive accuracy. Depression and Anxiety, 26(2), 167173. https://doi.org/10.1002/da.20504.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Porges, S. W. , & Raskin, D. C. (1969). Respiratory and heart rate components of attention. Journal of Experimental Psychology, 81(3), 497503. https://doi.org/10.1037/h0027921.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quintana, D. S. , Alvares, G. A. , & Heathers, J. A. (2016). Guidelines for reporting articles on psychiatry and heart rate variability (GRAPH): Recommendations to advance research communication. Translational Psychiatry, 6(5), e803. https://doi.org/10.1038/tp.2016.73.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quintana, D. S. , Guastella, A. J. , McGregor, I. S. , Hickie, I. B. , & Kemp, A. H. (2013). Heart rate variability predicts alcohol craving in alcohol dependent outpatients: Further evidence for HRV as a psychophysiological marker of self-regulation. Drug and Alcohol Dependence, 132(1–2), 395398. https://doi.org/10.1016/j.drugalcdep.2013.02.025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quintana, D. S. , McGregor, I. S. , Guastella, A. J. , Malhi, G. S. , & Kemp, A. H. (2013). A meta-analysis on the impact of alcohol dependence on short-term resting-state heart rate variability: Implications for cardiovascular risk. Alcoholism, Clinical and Experimental Research, 37(Suppl. 1), E23E29. https://doi.org/10.1111/j.1530-0277.2012.01913.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raven, J. (2000). The Raven’s progressive matrices: Change and stability over culture and time. Cognitive Psychology, 41(1), 148. https://doi.org/10.1006/cogp.1999.0735.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rochat, L. , Maurage, P. , Heeren, A. , & Billieux, J. (2019). Let's open the decision-making umbrella: A framework for conceptualizing and assessing features of impaired decision making in addiction. Neuropsychology Review, 29(1), 2751. https://doi.org/10.1007/s11065-018-9387-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogier, G. , & Velotti, P. (2018). Conceptualizing gambling disorder with the process model of emotion regulation. Journal of Behavioral Addictions, 7(2), 239251. https://doi.org/10.1556/2006.7.2018.52.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schandry R. . (1981). Heart beat perception and emotional experience. Psychophysiology, 18(4), 483488. https://doi.org/10.1111/j.1469-8986.1981.tb02486.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sharpe L. . (2004). Patterns of autonomic arousal in imaginal situations of winning and losing in problem gambling. Journal of Gambling Studies, 20(1), 95104. https://doi.org/10.1023/B:JOGS.0000016706.96540.43.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sharpe, L. , Tarrier, N. , Schotte, D. , & Spence, S. H. (1995). The role of autonomic arousal in problem gambling. Addiction (Abingdon, England), 90(11), 15291540. https://doi.org/10.1046/j.1360-0443.1995.9011152911.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thayer, J. F. , & Lane, R. D. (2009). Claude Bernard and the heart-brain connection: Further elaboration of a model of neurovisceral integration. Neuroscience and Biobehavioral Reviews, 33(2), 8188. https://doi.org/10.1016/j.neubiorev.2008.08.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ulrich, N. , Ambach, W. , & Hewig, J. (2016). Severity of gambling problems modulates autonomic reactions to near outcomes in gambling. Biological Psychology, 119, 1120. https://doi.org/10.1016/j.biopsycho.2016.06.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verdejo-García, A. , & Bechara, A. (2009). A somatic marker theory of addiction. Neuropharmacology, 56(Suppl. 1), 4862. https://doi.org/10.1016/j.neuropharm.2008.07.035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verdejo-Garcia, A. , Clark, L. , & Dunn, B. D. (2012). The role of interoception in addiction: A critical review. Neuroscience and Biobehavioral Reviews, 36(8), 18571869. https://doi.org/10.1016/j.neubiorev.2012.05.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Winstanley, C. A. , & Clark, L. (2016). Translational models of gambling-related decision-making. Current Topics in Behavioral Neurosciences, 28, 93120. https://doi.org/10.1007/7854_2015_5014.

    • Crossref
    • Search Google Scholar
    • Export Citation
The author instruction is available in PDF.
Please, download the file from HERE

Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

Indexing and Abstracting Services:

  • Web of Science [Science Citation Index Expanded (also known as SciSearch®)
  • Journal Citation Reports/Science Edition
  • Social Sciences Citation Index®
  • Journal Citation Reports/ Social Sciences Edition
  • Current Contents®/Social and Behavioral Sciences
  • EBSCO
  • GoogleScholar
  • PsycINFO
  • PubMed Central
  • SCOPUS
  • Medline
  • CABI
2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 850 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information Gold Open Access
Purchase per Title  

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Publication
Programme
2021 Volume 10
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • H. N. Alexander LOGEMANN (ELTE Eötvös Loránd University, Hungary)
  • Anikó MARÁZ (Humboldt University of Berlin, Germany)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)
  • Attila SZABÓ (ELTE Eötvös Loránd University, Hungary)
  • Róbert URBÁN (ELTE Eötvös Loránd University, Hungary)
  • Aviv M. WEINSTEIN (Ariel University, Israel)

Editorial Board

  • Max W. ABBOTT (Auckland University of Technology, New Zealand)
  • Elias N. ABOUJAOUDE (Stanford University School of Medicine, USA)
  • Hojjat ADELI (Ohio State University, USA)
  • Alex BALDACCHINO (University of Dundee, United Kingdom)
  • Alex BLASZCZYNSKI (University of Sidney, Australia)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Beáta BÖTHE (University of Montreal, Canada)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Sam-Wook CHOI (Eulji University, Republic of Korea)
  • Damiaan DENYS (University of Amsterdam, The Netherlands)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Heather HAUSENBLAS (Jacksonville University, USA)
  • Tobias HAYER (University of Bremen, Germany)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Jaeseung JEONG (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Emmanuel KUNTSCHE (La Trobe University, Australia)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Michel LEJOXEUX (Paris University, France)
  • Anikó MARÁZ (Eötvös Loránd University, Hungary)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Frederick GERARD MOELLER (University of Texas, USA)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Nancy PETRY (University of Connecticut, USA)
  • Bettina PIKÓ (University of Szeged, Hungary)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Rory C. REID (University of California Los Angeles, USA)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Ferenc TÚRY (Semmelweis University, Hungary)
  • Alfred UHL (Austrian Federal Health Institute, Austria)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Jun 2021 0 0 0
Jul 2021 0 0 0
Aug 2021 0 0 0
Sep 2021 0 0 0
Oct 2021 0 265 188
Nov 2021 0 175 120
Dec 2021 0 0 0