Authors:
Elodie HurelCHU Nantes, UIC Psychiatrie et Santé Mentale, Nantes Université, F-44000 Nantes, France
CHU Nantes, INSERM, MethodS in Patient Centered Outcomes and HEalth ResEarch, SPHERE, Nantes Université, Univ Tours, F-44000 Nantes, France

Search for other papers by Elodie Hurel in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-5700-8965
,
Marie Grall-BronnecCHU Nantes, UIC Psychiatrie et Santé Mentale, Nantes Université, F-44000 Nantes, France
CHU Nantes, INSERM, MethodS in Patient Centered Outcomes and HEalth ResEarch, SPHERE, Nantes Université, Univ Tours, F-44000 Nantes, France

Search for other papers by Marie Grall-Bronnec in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0003-0722-7243
,
Elsa ThiabaudCHU Nantes, UIC Psychiatrie et Santé Mentale, Nantes Université, F-44000 Nantes, France

Search for other papers by Elsa Thiabaud in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-5253-3595
,
Juliette LeboucherCHU Nantes, UIC Psychiatrie et Santé Mentale, Nantes Université, F-44000 Nantes, France

Search for other papers by Juliette Leboucher in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-4586-5011
,
Maxime LeroyDepartment of Clinical Research and Innovation, CHU Nantes, Biostatistics and Methodology Unit, Nantes Université, F-44000 Nantes, France

Search for other papers by Maxime Leroy in
Current site
Google Scholar
PubMed
Close
,
Bastien PerrotCHU Nantes, INSERM, MethodS in Patient Centered Outcomes and HEalth ResEarch, SPHERE, Nantes Université, Univ Tours, F-44000 Nantes, France
Department of Clinical Research and Innovation, CHU Nantes, Biostatistics and Methodology Unit, Nantes Université, F-44000 Nantes, France

Search for other papers by Bastien Perrot in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-3701-6693
, and
Gaëlle Challet-BoujuCHU Nantes, UIC Psychiatrie et Santé Mentale, Nantes Université, F-44000 Nantes, France
CHU Nantes, INSERM, MethodS in Patient Centered Outcomes and HEalth ResEarch, SPHERE, Nantes Université, Univ Tours, F-44000 Nantes, France

Search for other papers by Gaëlle Challet-Bouju in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-2238-8005
Open access

Abstract

Background and aims

This research aimed to characterize social information processing abilities in a population of regular nondisordered poker players compared to controls.

Methods

Participants completed the Posner cueing paradigm task including social cues (faces) to assess attention allocation towards social stimuli, including the effect of the presentation time (subliminal vs supraliminal) and of the emotion displayed. The study included two groups of participants: 30 regular nondisordered poker players (those who played at least three times a week in Texas Hold'em poker games for at least three months) and 30 control participants (those who did not gamble or gambled less than once a month, whatever the game).

Results

The group of regular nondisordered poker players displayed an enhancement of the inhibition of return during the Posner cueing task. This means that in valid trials, they took longer to respond to the already processed localization in supraliminal conditions compared to controls. However, our results did not evidence any particular engagement or disengagement attention abilities toward specific types of emotion.

Discussion and Conclusions

These results suggest that regular nondisordered poker players displayed social information processing abilities, which may be due to the importance to efficiently process social information that can serve as tells in live poker. The observed enhancement of the inhibition of return may permit poker players to not process a localization that has already processed to save attentional resources. Further research regarding the establishment of the IOR in other forms of gambling and with non-social cues needs to be performed.

Abstract

Background and aims

This research aimed to characterize social information processing abilities in a population of regular nondisordered poker players compared to controls.

Methods

Participants completed the Posner cueing paradigm task including social cues (faces) to assess attention allocation towards social stimuli, including the effect of the presentation time (subliminal vs supraliminal) and of the emotion displayed. The study included two groups of participants: 30 regular nondisordered poker players (those who played at least three times a week in Texas Hold'em poker games for at least three months) and 30 control participants (those who did not gamble or gambled less than once a month, whatever the game).

Results

The group of regular nondisordered poker players displayed an enhancement of the inhibition of return during the Posner cueing task. This means that in valid trials, they took longer to respond to the already processed localization in supraliminal conditions compared to controls. However, our results did not evidence any particular engagement or disengagement attention abilities toward specific types of emotion.

Discussion and Conclusions

These results suggest that regular nondisordered poker players displayed social information processing abilities, which may be due to the importance to efficiently process social information that can serve as tells in live poker. The observed enhancement of the inhibition of return may permit poker players to not process a localization that has already processed to save attentional resources. Further research regarding the establishment of the IOR in other forms of gambling and with non-social cues needs to be performed.

Introduction

Poker as a social game of skills

Poker is a social game involving a competition between players. Contrary to bank games, in which participants play against the bank and can expect to win in the short term but not to be beneficiaries in the long term, social games can allow long-term gains, provided that the players use the skill gap between them and their opponents to their advantage (Bjerg, 2010). Studies about the impact of skill on poker performances have emerged, but there is still a debate regarding the relative importance of chance versus skill. Hence, poker skills may be useful in the long term and are not always observable with only a few hands (Palomäki, Laakasuo, Cowley, & Lappi, 2020), suggesting a continuum between chance and skill (Fiedler & Rock, 2009). For example, the use of strategies was demonstrated to increase the probability of winning (DeDonno & Detterman, 2008), and rankings in a tournament can predict rankings in the following competitions (Croson, Fishman, & Pope, 2008). Another study demonstrated that ranks can be predicted by previous rankings, type of strategies used and experience in playing poker, and that skills would dominate chance after 1,500 hands played (Potter van Loon, van den Assem, & van Dolder, 2015). Nevertheless, other studies were more mitigated. Indeed, when giving the same cards to expert and nonexpert players in 60 hands of “Texas Hold'em” poker, the only significant difference was that the experts handled bad cards better than the controls (Meyer, von Meduna, Brosowski, & Hayer, 2013), showing the importance of chance.

Poker players' processing of social information

Among all the skills that may be involved in poker, players engaged in live poker have to efficiently process the social information displayed by their opponents (Billings, Davidson, Schaeffer, & Szafron, 2002), as it can serve as tells. Indeed, social information permits speculation on opponents' strategies and the prediction of their actions (opponent modelling, Billings et al., 2002). Some research has highlighted the importance of social information processing in poker (Palomäki et al., 2020). For example, in a simplified poker game, a study showed that the level of trustworthiness displayed by an opponent could modulate the wager (Schlicht, Shimojo, Camerer, Battaglia, & Nakayama, 2010). Gender displayed by opponents (Palomäki, Yan, Modic, & Laakasuo, 2016) and playing with a human or a computer (Carter, Bowling, Reeck, & Huettel, 2012) may also impact poker players' decisions. Additionally, research has shown a positive association between the self-reported quality of social competencies and performance in poker (Leonard & Williams, 2015; Schiavella, Pelagatti, Westin, Lepore, & Cherubini, 2018). The cognitive assessment of social information processing can include the conscious identification of social stimuli (such as identifying an emotion displayed by supraliminal vocal or facial stimuli) or the unconscious processing of social stimuli (such as discriminating an emotion displayed by a face and presented subliminally, which is invisible). In such assessments, social stimuli, such as faces, can be used as cues to capture the attention towards one of two locations on a screen before the presentation of a target, which may be presented either at the cued or noncued location. The measurement of reaction times then allows us to evaluate the facilitating or costing effects of cues (Posner, 1980). The engagement of attention towards a cue is indeed supposed to facilitate the detection of the cued target, leading to faster reaction times. Furthermore, the disengagement of attention from a cue is supposed to slow uncued target detection. Nevertheless, stimulus-onset asynchrony (SOA, i.e., the delay between cue onset and target onset) also impacts attention allocation. Indeed, when SOA is greater than approximately 200–300 ms and up to 3,000 ms (Samuel & Kat, 2003), the phenomenon of the inhibition of return (IOR) occurs, which is an adaptive mechanism that prevents an already processed localization from being processed again and allows more attentional resources to be allocated to information located elsewhere (Klein, 2000; Tang et al., 2015). This results in slowing the detection of the cued target while accelerating the detection of the noncued target.

Objectives and hypotheses

In the present study, we were interested in determining whether a regular poker practice, defined as a repeated regular exposition to poker playing, is associated with specific social information attention abilities. To this end, we compared these abilities, which were assessed using a spatial cueing paradigm, between regular nondisordered poker players and controls with no or limited poker experience. Moreover, we were also interested in determining the types of emotions that are better processed by regular nondisordered poker players compared to controls.

For trials with an SOA lower than 200 ms, it is possible to assess the engagement and disengagement of attention towards emotional cues (faces) without the establishment of the IOR. We hypothesized that regular nondisordered poker players would display overall higher social information attention abilities than controls. In this case, lower reaction times (benefit) may be observed for cued trials (i.e., facilitation of the detection of the target due to the previous engagement of attention by the cue displayed at the same location) and higher reaction times (cost) may be observed for noncued trials (i.e., the cost related to disengaging attention from the location of the cue before detecting the target at the opposite location), regardless of the facial emotion type. Moreover, we hypothesized that regular nondisordered poker players would display higher information attention abilities compared to controls for the types of emotion that may be useful is the context of poker, i.e., joy, sadness, and surprise. This would result in higher benefit effects and lower cost effects in poker players compared with controls for trials with these emotions as cues.

In the case of trials with an SOA greater than 200 ms, the IOR is supposed to appear, which can lead to reversed effects on Posner's paradigm, i.e., costs instead of benefits for cued trials and benefits instead of costs for noncued trials. Regardless of the emotion explored, we hypothesized that regular nondisordered poker players would display a higher IOR effect than controls, which would result in a lower benefit effect for cued trials and a higher cost effect for noncued trials. Similarly, we expected a higher IOR effect for regular nondisordered poker players for the types of emotion that may be useful is the context of poker, i.e., joy, sadness, and surprise. This would result in lower benefit effects and higher cost effects for these emotions in nondisordered poker players than in controls.

Methods

This study is part of the PERHAPS research program (NCT02590211), which aims to assess the cognitive functioning of poker players with or without a gambling disorder.

Participants

This study included two groups of participants. The first group comprised regular nondisordered poker players (RPPG, n = 30). A regular playing was defined as the repeated practice of Texas Hold'em poker games at least once a week for at least three months. All regular players had to play poker in the live version, so that they had the chance to accrue knowledge pertaining to social cognitive processing. They could also play online but not exclusively.

The control group (CG, n = 30) included nonpoker players and participants who gambled less than once a month (poker or other gambling activities).

All participants were recruited from February 2017 to May 2018 through media announcements and from the registry of volunteers for research established by the research team.

Inclusion and noninclusion criteria

We included only men aged between 18 and 60 years old who presented correct vision and hearing (even after correction). We did not include women because more men tend to play poker (McCormack, Shorter, & Griffiths, 2014) and we anticipated that the gender ratio may have been very unbalanced between the two groups and would have introduce a bias. The other noninclusion criteria were as follows: (i) participants who had taken part in a pharmaceutical trial during the previous month; (ii) those who presented colour blindness; (iii) those who presented severe depression (assessed with the BDI-13); (iv) those who presented a high level of anxiety (assessed with the STAI-YB); (v) those who presented heart problems and/or had electrical implants; (vi) those who were under a gambling ban procedure; (vii) those under a guardianship or curatorship; (viii) those with any history of neurological diseases, such as a history of seizures; (ix) those who had a physical condition that could disturb the assessment; (x) those who presented a nonstable current psychiatric disorder (assessed with the MINI); (xi) those who presented a gambling disorder (assessed with the NODS); (xii) those who displayed a cognitive impairment (assessed with the MMSE); and (xiii) those who had used any psychoactive substance (self-declared) in the 8 h preceding the assessment (except for nicotine).

Measures used for inclusion and confounding factors

The Mini International Neuropsychiatric Interview (MINI) (Lecrubier et al., 1997) is a structured interview investigating mood, anxiety and addictive (substance) disorders, and psychotic syndrome.

The National Opinion Research Center DSM-IV Screen for Gambling Problems (NODS) (Gerstein et al., 1999) is a structured interview investigating the DSM-IV diagnostic criteria for pathological gambling. We used a modified version to follow DSM-5 changes (American Psychiatry Association, 2013) (i.e., removing the item exploring illegal acts and using a threshold of 4 instead of 5).

The 13-item Beck Depression Inventory self-report questionnaire (BDI-13) (Beck, Steer, & Carbin, 1988) is a questionnaire assessing the level of depression. A score ≥16 indicates a severe level of depression.

The trait version of the State-Trait Anxiety Inventory self-report questionnaire (STAI Y-B) (Spielberger, 1999) assesses anxiety. A score ≥56 indicates a high level of anxiety.

The Mini Mental State Examination (MMSE) (Folstein, Folstein, & McHugh, 1975) assesses the global cognitive level. A score ≤24 indicates cognitive impairment.

Measures used to investigate social attention allocation

We used the Posner Cueing Paradigm (or Spatial Cueing Paradigm) to assess attention engagement and disengagement towards social cues. This paradigm includes a cue to allocate the participant's attention to a location that may later contain a target and manipulates the spatial validity between the location of the target related to the location of the cue (Hayward & Ristic, 2013; Posner, 1980).

For each trial, two grey boxes (5.3 cm height × 3.0 cm width) appeared symmetrically on each side of a fixation cross for 1,000 ms. Then, a cue appeared on the centre of one of the two boxes for 17 ms (subliminal condition, automatic processing) or 500 ms (supraliminal condition, strategic processing, supposed to trigger the IOR).

The cue was a picture of a face displaying an emotion (surprise, disgust, joy, anger, fear, sadness or neutral), which was the same size as the grey boxes and extracted from the Karolinska Directed Emotional Faces (KDEF) corpus (Lundqvist, Flykt, & Öhman, 1998). In order to select which pictures to use, we performed a preliminary validation study on 15 volunteers using a procedure adapted from Bradley, Mogg, White, Groom, and De Bono (1999). Each picture from the KDEF corpus was rated by ten volunteers. They had to rate how much the picture elicited each of the following six emotions: surprise, disgust, joy, anger, fear, sadness, on a scale from 1 (not at all) to 6 (extremely). Emotional pictures were retained only if the mean rating of the corresponding emotion was higher than 3.5 and if the mean ratings of the other emotions were at least one point below the mean rating of the corresponding emotion. Neutral pictures were retained only if the mean ratings of all emotions were under 2. Pictures with too much luminosity were removed. Finally, we selected only the four best pictures for each emotion (surprise, disgust, joy, anger, fear, sadness or neutral), i.e. those which had the higher mean rating of the corresponding emotion for emotional pictures and those which had the lowest mean ratings of all emotions for neutral pictures.

A mask without any stimulus (the two grey boxes) was then displayed for 17 ms in the subliminal condition and 50 ms in the supraliminal condition. Afterwards, a target (a black circle with a 0.3 cm diameter) appeared on the lower half of one of the grey boxes for 2,000 ms or until the participant responded (see Fig. 1). This shifted down presentation of the cue, and the target was chosen to avoid a masking forward effect (i.e., when the face cue masks the following target) (Fox, Russo, & Dutton, 2002).

Fig. 1.
Fig. 1.

Flow diagram of the Posner cueing task. Fig. 1 (a) Flow diagram of the Posner cueing task - Valid subliminal trial (b) Flow diagram of the Posner cueing task - Non valid supraliminal presentation

Citation: Journal of Behavioral Addictions 2023; 10.1556/2006.2022.00082

Participants were instructed to localize the target as quickly as possible by pressing the extreme left or right button on a 7-button response pad. When the target appeared at the cued location, the trial was considered valid; otherwise, the trial was considered invalid. For noncued trials, rectangles were also presented in both conditions (sub- and supraliminal) but without pictures, followed by the mask (also for 17 ms or 50 ms). This type of trial was a control measure of reaction times without cueing (without the previous allocation of attention). To prevent automatic responses, the intertrial intervals randomly (without replacement) varied for 500, 750, 1,000 or 1,500 ms.

Each participant underwent 224 trials. Half of the trials (112) were valid, one quarter (56) were nonvalid, and one-quarter were noncued. Half of each type of trial (valid, nonvalid and noncued) was supraliminal, and half was subliminal. For each type of trial (valid, nonvalid and noncued) and each presentation (supraliminal and subliminal), each of the 7 emotions was displayed the same number of times on the left and on the right.

The presentation order of the type of trials, presentation times and type of emotions was randomized. At the beginning of the task, participants practised for eight trials (4 valid, 2 nonvalid and 2 noncued) with pictures that were not used for the rest of the task. A break was added at the end of the 112th trial to avoid fatigue. Anticipative answers (i.e., in which participants answered in less than 250 ms) were excluded before data processing.

The outcomes recorded were reaction times and errors (omissions, i.e., the participant did not answer, or commissions, i.e., the participant chose the wrong side). Regarding the outcomes calculated, the benefit index (reaction time of noncued trials – reaction time of valid trials) reflects the decrease in reaction times due to the previous engagement of attention induced by the cue in a valid subliminal trial compared to a noncued subliminal trial. In the supraliminal condition, the benefit index was expected to decrease because of the higher reaction times in valid trials due to the IOR onset. Moreover, the cost index (reaction time of nonvalid trials – reaction time of noncued trials) reflects the increase in reaction times due to the necessary disengagement of attention from the location of the cue in a nonvalid subliminal trial compared to a noncued subliminal trial. In the supraliminal condition, the cost index was expected to decrease because of the lower reaction times in nonvalid trials due to IOR onset.

Procedure

After screening for eligibility, an appointment was proposed in the research centre to proceed with the inclusion and carry out the research visit. Sociodemographic data (age and educational level), concomitant treatments and measures for inclusion were first collected.

Then, the participants completed a subset of 5 cognitive tasks, including the Posner Cueing Task, with a total duration of 2 h. The task order was randomized for each participant, but the Posner Cueing Task was always performed first (to avoid alterations in attention). The Posner Cueing Task was computer-administered.

All participants were seated in a quiet room and positioned 60 cm from the screen. Computerized tasks were programmed using Superlab 5 (Cedrus Corporation, San Pedro, CA, USA), and the 7-button response pad RB-730 (Cedrus Corporation) was used to record the participants' responses.

Participants received 30€ at the end of the appointment in compensation for their participation.

Statistical analysis

A descriptive analysis was performed to determine the mean and standard deviation of all variables.

To consider possible confounding factors that may have affected attentional performance, age, education level, MMSE score, BDI score, STAI score, concomitant treatment and current mood, and anxiety, psychotic and alcohol or substance use disorders were compared between the two groups using non-parametric tests (Mann-Whitney or Fisher's exact test). Given the sample size, we used the threshold of P < 0.10 to include the corresponding potential confounding factor as a covariate in the subsequent statistical analyses.

Then, two linear mixed models with a random effect on the intercept were used, with Group (RPPG/CG), Emotion (surprise/disgust/joy/anger/fear/sadness/neutral) and Presentation time (supraliminal/subliminal) as fixed effects, and the benefit and cost indices as the dependent measures. For the two models, we included the interactions between Group and Emotion and between Group and Presentation time. We used a backward selection procedure starting with a model of the form:
Yipe=β0+ui+β1×group+β2×presentation+β3×emotion+β4×group×presentation+β5×group×emotion+β6×covariates+eipe
where Yipe is the outcome of interest (benefit index or cost index) of the participant i for presentation p and emotion e, β0 is the common intercept, uiN(0,σ12) is the random intercept, β1 is the group effect (poker vs. controls), β2 is the presentation effect (supraliminal vs. subliminal), β3 is the vector of effects for emotions (ref = neutral), β4 is the coefficient for the interaction term group*presentation, β5 is the vector of coefficients for the interaction terms group*emotion, β6 is the vector of coefficients for the covariates, and eipeN(0,σ22) is the residual variance.

We first tested the interaction term β4 with a Wald test and β5 with a joint test, before testing the other main effects. At each step, the term with the largest P-value was removed from the model, and a new model was estimated. In the final model, we estimated the variance explained by the fixed effects and by both the fixed and random effects by calculating pseudo R2 values (Snijders & Bosker, 1994).

Statistical analyses were performed using Stata 17.0 software. We used the mixed command to fit the linear mixed models with maximum likelihood estimation, and used the margins command to compute the adjusted predictions (estimated means) from the final model and to estimate the corresponding contrasts. All model assumptions (normality and homoscedasticity of the residuals and the BLUPs (Best Linear Unbiaised Predictions) of the random effects) were tested and satisfied.

Ethics

The PERHAPS protocol received approval from the French Research Ethics Committee (CPP) on September 12, 2016. All participants were informed about the study and provided their written informed consent.

Results

Sociodemographic, clinical, and gambling characteristics

The two groups differed for four potential confounding factors at the threshold of P < 0.10, namely, age (P = 0.021), level of education (P = 0.028), score on the MMSE (P = 0.030) and score on the BDI (P = 0.079) (see Table 1). Consequently, these four variables were included in all comparative analyses as covariates. Regarding the gambling profile, only 5 participants from the CG declared that they had played poker during their lifetime, of which only 2 declared that they played less than once a month in the past year. Regarding the other gambling activities, only 2 participants from the RPPG bet on sports more than once a week. Otherwise, no gambling activity was practised at this frequency.

Table 1.

Sociodemographic, clinical, and gambling characteristics

VariablesControl Group (CG) n = 30Recreational Regular Poker Player Group (RNDPPG) n = 30P
Mean (sd) or n
Sociodemographic characteristics
Age29.13 (10.2)33.23 (7.8)0.021
Level of education (years)14.7 (1.95)13.33 (2.59)0.028
Clinical characteristics
MMSE score29.41 (0.9)28.87 (1.1)0.030
BDI score1.23 (2.5)1.83 (2.1)0.079
STAI score31.8 (8.6)33.5 (7.2)0.382
Presence of concomitant pharmacological treatments250.143
MINI diagnosis (number of subjects with a positive diagnosis)
Actual mood disorder111.000
Actual anxious disorder111.000
Actual addictive disorder240.286
Actual psychotic disorder101.000
Gambling characteristics
Number of games currently playedNumber of participants per number of games
0170
193
239
319
404
505
Frequency of games played
Lottery (number of players who had already played in their lifetime)22/2329/30
Current frequency (past 12 months)
More than once a week01
Once a week04
Once or more a month02
Less than once a month911
Did not play in the actual period1311
Slot machines (number of players who had already played in their lifetime)12/2322/30
Current frequency (past 12 months)
More than once a week00
Once a week00
Once or more a month01
Less than once a month27
Did not play in the actual period1014
Blackjack (number of players who had already played in their lifetime)2/2314/30
Current frequency (past 12 months)
More than once a week00
Once a week00
Once or more a month02
Less than once a month03
Did not play in the actual period29
Horse race betting (number of players who had already played in their lifetime)9/2315/30
Current frequency (past 12 months)
More than once a week00
Once a week00
Once or more a month01
Less than once a month16
Did not play in the actual period88
Sport betting (number of players who had already played in their lifetime)11/2326/30
Current frequency (past 12 months)
More than once a week02
Once a week01
Once or more a month03
Less than once a month415
Did not play in the actual period65
Not known1
Poker (number of players who had already played in their lifetime)5/2330/30
Current frequency (past 12 months)
More than once a week026
Once a week04
Once or more a month00
Less than once a month20
Did not play in the actual period30

Bold P-values are those under the threshold of 0.10, and the corresponding variables were including as covariates in the subsequent analyses.

Regarding the RPPG, poker players all had poker as their preferred type of gambling among all the games they experimented, even if the large majority of them also played to other gambling activities. They all played both live and online poker, but more than half of them (53.3%) preferred gambling live poker. They started gambling on average at 15 years old (m = 14.9, sd = 3.9), and were mainly initiated through scratch cards (66.7%) or poker (13.3%). They had on average a regular practice of gambling for close to 10 years (m = 9.6, sd = 6.1).

Posner cueing task

Benefit index

The estimated coefficients of the linear mixed model for the benefit index are presented in Table 2, and effects are plotted in Fig. 2. The variance explained by the fixed effects was 5.7% and the variance explained by both the fixed and random effects was 16.5%.

Table 2.

Final model from the linear mixed model analysis on the benefit index

VariablesCoefficientStandard errorConfidence intervalP value
Group (ref. CG)−5.694.90[−15.29; 3.92]0.246
Presentation (ref. subliminal)−1.253.48[−8.07; 5.58]0.720
Group × Presentation−10.544.88[−20.11; −0.97]0.031
Covariates
Age−0.290.23[−0.16; 0.74]0.213
Level of education (years)0.291.00[−1.67; 2.25]0.769
MMSE score−2.792.25[−7.20; 1.63]0.216
BDI score1.610.90[−0.15; 3.37]0.074

Italic lines represent the covariates integrated in the model as potential confounding factors.

Fig. 2.
Fig. 2.

Adjusted predictions and 95% confidence intervals of the benefit index from the final model

Note: Vertical bars represent the 95% confidence intervals of the adjusted predictions. CG: Control Group; RPPG: Regular Poker Player Group

Citation: Journal of Behavioral Addictions 2023; 10.1556/2006.2022.00082

There was no significant effect of the type of emotion on the benefit index.

With the supraliminal presentation, the benefit index in the RPPG group was 16.2 points [95% CI: −25.8; −6.6] lower than in the CG (P < 0.001). With the subliminal presentation, this difference was not significant (−5.7 [−15.3; −3.9], P = 0.246).

Moreover, there was no significant effect of the presentation time in the CG (−1.2 [−8.1; 5.6]. However, for the RPPG, the benefit index with the supraliminal presentation was 11.8 [−18.5; −5.1] points lower than with the subliminal presentation. This seems to reflect the establishment of an IOR in supraliminal trials, that was stronger for the RPPG compared to the CG.

Cost index

The estimated coefficients of the linear mixed model for the cost index are presented in Table 3, and effects are plotted in Fig. 3. The variance explained by the fixed effects was 5.4% and the variance explained by both the fixed and random effects was 18.9%.

Table 3.

Final model from the linear mixed model analysis on the cost index

VariablesCoefficientStandard errorConfidence intervalP value
Presentation (ref. subliminal)−13.822.85[−19.40; −8.23]<0.001
Covariates
Age0.510.23[0.05; 0.96]0.030
Level of education (years)2.251.02[0.26; 4.24]0.027
MMSE score1.522.28[−2.95; 5.99]0.505
BDI score−1.420.92[−3.22; 0.38]0.122

Italic lines represent the covariates integrated in the model as potential confounding factors.

Fig. 3.
Fig. 3.

Adjusted predictions and 95% confidence intervals of the cost index from the final model Note: Vertical bars represent the 95% confidence intervals of the adjusted predictions.

Citation: Journal of Behavioral Addictions 2023; 10.1556/2006.2022.00082

There was no significant difference on the cost index between CG and RPPG. The cost index with the supraliminal presentation was 13.8 [−19.4; −8.2] points lower than with the subliminal presentation.

Discussion

This study assessed the social allocation of attention in poker players with repeated regular exposition to poker playing compared to controls with no or limited poker experience, using a Posner paradigm task. Regardless of the group, this study showed that the disengagement of attention was facilitated in nonvalid supraliminal trials compared to nonvalid subliminal trials. When comparing the two groups, this study showed that the IOR was higher for the poker player group than the CG.

Indeed, the analysis of nonvalid trials showed that the cost index diminished significantly in supraliminal trials. These results suggest that the nonvalidity slowing is especially linked to lower SOA and that higher SOA already permits disengaging from nonvalid cues. This may also be linked with the establishment of the IOR. Indeed, the further the target is from the cue in the supraliminal condition, the faster the reaction time (Bennett & Pratt, 2001). The IOR may facilitate re-engagement towards another localization.

Our study also showed that poker players displayed a higher decrease of the benefit index in the supraliminal condition compared to controls. These results suggest that the IOR of poker players is enhanced compared to that of controls. This type of result has already been observed in a population of cannabis users (Vivas, Estevez, Moreno, Panagis, & Flores, 2012) (see (Colzato & Hommel, 2009) for opposite results) and cocaine users after taking a dose of dextroamphetamine (Fillmore, Rush, & Abroms, 2005). The enhanced IOR found in these populations suggests that the IOR can be improved by the ingestion of drugs, such as cannabis. Nevertheless, the mechanisms are still not understood (Vivas et al., 2012). Other studies, gathered in a systematic review, have assessed the effect of alcohol, ecstasy, and hallucinogens. Nonetheless, only the absence of a difference or a lower IOR was evidenced (Olthuis & Klein, 2012). As for drugs use, repeated poker practice may also impact the IOR. Conversely, an optimal IOR may be helpful to win a poker game, and those with a good IOR may tend to continue playing poker because of this advantage.

Interestingly, a trend towards an enhanced IOR has also been found in a group of patients with Asperger syndrome (Rinehart, Bradshaw, Moss, Brereton, & Tonge, 2008) but not in patients with autism spectrum disorders (Antezana, Mosner, Troiani, & Yerys, 2016; Lin, Miao, & Zhang, 2020). In patients with Asperger syndrome, this result was linked to the fact that this population displays improved attentional visual search efficiency. Thus, it would be interesting to see if poker players also display a better visual search efficiency or if an enhanced IOR is only present when social cues are presented.

As the IOR seems to be helpful not to process a localization that has already been assessed (Klein, 2000; Satel, Wilson, & Klein, 2019), this enhancement may be a sign of a better attentional system in poker players. Indeed, diminishing the refreshment rate of information permits increasing the amount of information that can be processed, which may be very helpful in the practice of poker. Thus, this enhanced IOR may reflect attention particularities linked with the practice of live poker, in which social information needs to be processed efficiently.

Moreover, this research did not evidence any particular engagement or disengagement attention abilities toward specific emotions. This is contrary to our hypothesis that regular nondisordered poker players would display higher information attention abilities compared to controls for the types of emotion that may be useful is the context of poker, i.e., joy, sadness, and surprise. Indeed, the processing efficiency hypothesis (Calvo, Avero, & Lundqvist, 2006; Gordillo León, Mestas Hernández, Pérez Nieto, & Arana Martínez, 2021) postulates that negative stimuli (especially fear) is processed more efficiently than neutral or positive stimuli to react more quickly. For example, in studies regarding spatial attention, fear is the main emotion that is known to automatically capture attention (Bannerman, Milders, de Gelder, & Sahraie, 2009). This hypothesis would explain why lower attentional resources are needed to identify fear and therefore increase reaction speed (Calvo et al., 2006). Anger can be included in this theory as well (Pinkham, Griffin, Baron, Sasson, & Gur, 2010). Nevertheless, our study did not show a significant advantage of any kind of facial cues compared to neutral facial cues.

Finally, beyond social information processing assed by neuropsychological tasks, higher self-reported interpersonal relationships quality has been cited as a predictor of good poker abilities (Schiavella et al., 2018). Interestingly, lower level of interpersonal relationships quality was linked with higher level of addiction in poker players (Schiavella et al., 2018), and an alteration of conscious social information processing was also evidenced in a group of patients with gambling disorder compared to controls (Kornreich et al., 2016). Therefore, higher social cognition abilities may be a marker of good skills in poker while lower social cognition abilities may be a feature of addictive disorders. It is thus important to confront results obtained with samples of poker players with and without a gambling disorder to identify what cognitive processes are linked to a repeated but non pathological poker practice to those related to the addictive process. This may provide useful knowledge to clinicians and researchers specialised in gambling and gambling disorder, both for improving theoretical models of addictive behaviours at various stages of the addictive process, such as in the I-PACE (Interaction of Person-Affect-Cognition-Execution) model (Brand et al., 2019), and for a use in therapy with poker players with a gambling disorder. Indeed, with reference to the present study, it could be possible to explain to patients with a poker-related gambling disorder that the repeated practice of poker may not lead to the development of specific emotion identification strategies for example.

Nevertheless, we did not ask to poker players participants whether they actively tried to practice various skill elements (including reading facial expressions from opponents for example) in the game. Poker-related social skills improvement can also be reached with the help of other players (social learning) (Talberg, 2019). Finally, several social-related personality traits may have been pre-existent in certain participants, independently of the practice of poker, and have influenced the social allocation of attention examined in this study. As a consequence, the effects highlighted in this study may come from either implicit learning (via mere exposure to poker), deliberate learning (via active practice of social skills in the game) or pre-existing social abilities (e.g. trait sociability, extraversion). Future studies on this topic should include information on mere exposure versus deliberate practice, as well as social-related personality traits, in data collected to be able to distinguish such effects one from each other.

Strengths and limitations

The main strength of our research is that we investigated an understudied population with an attentional task designed to assess engagement and disengagement biases towards social-emotional information. One of the limitations of this study is that we did not follow our group over time to observe if their cognitive functioning impacted the onset of addiction or if their attentional particularities changed over time and practice. Additionally, results obtained in this study only concern live poker players. Indeed, the observation of facial expressions and nonverbal information is lacking in online poker. Moreover, there was no women included which do not permit to generalize our results to both genders. Finally, we did not control for nicotine use before the experiment, despite its dopaminergic effect.

Conclusions

In conclusion, our study showed particularities in social information processing associated with the regular practice of poker. We found a higher IOR in the poker player group compared to the CG. Further research regarding the establishment of the IOR in various forms of gambling, specifically what can cause its enhancement, needs to be performed. Indeed, it would be interesting to know if this enhancement of the IOR also appears in nonsocial cues. Nevertheless, more studies are needed to explore the directionality of the link. Indeed, this study cannot conclude if poker practice led to these changes or if these particularities were displayed by the individuals before practising poker and contributed to the experimentation and maintenance of poker practice, given the advantage drawn from this specificity. Interestingly, poker players may practice their social information attention abilities on other people in everyday life, but also specifically on other poker players, who may be bluffing and/or hiding their own social information. On the contrary, the non-players would practice these specific abilities only on everyday life situations, when people are not attempting to conceal the expression of their emotions most of the time. This specific feature of poker is an important particularity that have to be taken into account when assessing and discussing poker players' social information processing abilities. Moreover, it would be interesting to explore whether individuals with gambling disorder show a different response pattern that could be a marker between controlled and excessive practice.

Funding sources

The PERHAPS study was funded by a grant from CHU Nantes (Internal call for tenders; RC14_0036). The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Authors' contribution

EH: collected data; detected any cause for invalid responses in all cognitive tasks; analysis and interpretation of the data; and wrote the first draft of the manuscript. MGB: obtained funding; study supervision; and inclusion of participants. ET and JL: collected data and detected any cause for invalid responses in all cognitive tasks. ML and BP: performed statistical analysis. GCB: study concept and design; obtained funding; analysis and interpretation of the data; and study supervision. All authors had full access to all data and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors provided feedback on the first draft of the manuscript and approved the final manuscript.

Conflicts of interest

MGB, JL, ET, EH and GCB declare that CHU Nantes has received funding from the gambling industry (FDJ and PMU) in the form of a philanthropic sponsorship (donations that do not assign a purpose of use). This funding had no influence on the present work, and scientific independence towards gambling industry operators is warranted. There were no publishing constraints. ML and BP declare that they have no conflicts of interest.

Acknowledgements

This research was conducted based on the initiative of and coordinated by the UIC Psychiatrie et Santé Mentale of CHU Nantes, which sponsored this study.

References

  • American Psychiatry Association (2013). DSM-5: Diagnostic and statistical manual of mental disorders. Washington, DC: American Psychiatric Publishing.

    • Search Google Scholar
    • Export Citation
  • Antezana, L., Mosner, M. G., Troiani, V., & Yerys, B. E. (2016). Social-emotional inhibition of return in children with autism spectrum disorder versus typical development. Journal of Autism and Developmental Disorders, 46(4), 12361246. http://doi.org/10.1007/s10803-015-2661-9.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Bannerman, R. L., Milders, M., de Gelder, B., & Sahraie, A. (2009). Orienting to threat: Faster localization of fearful facial expressions and body postures revealed by saccadic eye movements. Proceedings of the Royal Society B: Biological Sciences, 276(1662), 16351641. http://doi.org/10.1098/rspb.2008.1744.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, A. T., Steer, R. A., & Carbin, M. G. (1988). Psychometric properties of the beck depression inventory: Twenty-five years of evaluation. Clinical Psychology Review, 8(1), 77100. https://doi.org/10.1016/0272-7358(88)90050-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bennett, P. J., & Pratt, J. (2001). The spatial distribution of inhibition of return. Psychological Science, 12(1), 7680. http://doi.org/10.1111/1467-9280.00313.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Billings, D., Davidson, A., Schaeffer, J., & Szafron, D. (2002). The challenge of poker. Artificial Intelligence, 134(1), 201240. http://doi.org/10.1016/S0004-3702(01)00130-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bjerg, O. (2010). Problem gambling in poker: Money, rationality and control in a skill-based social game. International Gambling Studies, 10(3), 239254. https://doi.org/10.1080/14459795.2010.520330.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Bradley, B. P., Mogg, K., White, J., Groom, C., & De Bono, J. (1999). Attentional bias for emotional faces in generalized anxiety disorder. British Journal of Clinical Psychology, 38(3), 267278. http://doi.org/10.1348/014466599162845.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Brand, M., Wegmann, E., Stark, R., Müller, A., Wölfling, K., Robbins, T. W., & Potenza, M. N. (2019). The interaction of person-affect-cognition-execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neuroscience and Biobehavioral Reviews, 104, 110. http://doi.org/10.1016/j.neubiorev.2019.06.032.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Calvo, M. G., Avero, P., & Lundqvist, D. (2006). Facilitated detection of angry faces: Initial orienting and processing efficiency. Cognition & Emotion, 20(6), 785811. http://doi.org/10.1080/02699930500465224.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carter, R. McK., Bowling, D. L., Reeck, C., & Huettel, S. A. (2012). A distinct role of the temporal-parietal junction in predicting socially guided decisions. Science (New York, N.Y.), 337(6090), 109111. http://doi.org/10.1126/science.1219681.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Colzato, L. S., & Hommel, B. (2009). Recreational use of cocaine eliminates inhibition of return. Neuropsychology, 23(1), 125129. http://doi.org/10.1037/a0013821.

  • Croson, R., Fishman, P., & Pope, D. G. (2008). Poker superstars: Skill or luck? Chance, 21(4), 2528. http://doi.org/10.1007/s00144-008-0036-0.

    • Search Google Scholar
    • Export Citation
  • DeDonno, M. A., & Detterman, D. K. (2008). Poker is a skill. Gaming Law Review, 12(1), 3136. http://doi.org/10.1089/glr.2008.12105.

  • Fiedler, I. C., & Rock, J.-P. (2009). Quantifying skill in games—Theory and empirical evidence for poker. Gaming Law Review and Economics, 13(1), 5057. http://doi.org/10.1089/glre.2008.13106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fillmore, M. T., Rush, C. R., & Abroms, B. D. (2005). D-Amphetamine-Induced enhancement of inhibitory mechanisms involved in visual search. Experimental and Clinical Psychopharmacology, 13(3), 200208. http://doi.org/10.1037/1064-1297.13.3.200.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). ‘Mini-Mental state’. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189198. https://doi.org/10.1016/0022-3956(75)90026-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fox, E., Russo, R., & Dutton, K. (2002). Attentional bias for threat: Evidence for delayed disengagement from emotional faces. Cognition & Emotion, 16(3), 355379. http://doi.org/10.1080/02699930143000527.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gerstein, D., Hoffmann, J., Larison, C., Murphy, S., Palmer, A., Chuchro, L., … Sinclair, S. (1999). Gambling impact and behavior study. Report to the national gambling impact study commission. National Gambling Impact Study Commission (NORC).

    • Search Google Scholar
    • Export Citation
  • Gordillo León, F., Mestas Hernández, L., Pérez Nieto, M. Á., & Arana Martínez, J. M. (2021). Detecting emotion faces in a Posner’s spatial cueing task: The adaptive value of surprise. Journal of Cognitive Psychology, 33(1), 3848. http://doi.org/10.1080/20445911.2020.1862854.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hayward, D. A., & Ristic, J. (2013). Measuring attention using the posner cuing paradigm: The role of across and within trial target probabilities. Frontiers in Human Neuroscience, 7, 205. http://doi.org/10.3389/fnhum.2013.00205.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Klein, N. (2000). Inhibition of return. Trends in Cognitive Sciences, 4(4), 138147. http://doi.org/10.1016/s1364-6613(00)01452-2.

  • Kornreich, C., Saeremans, M., Delwarte, J., Noël, X., Campanella, S., Verbanck, P., … Brevers, D. (2016). Impaired non-verbal emotion processing in pathological gamblers. Psychiatry Research, 236, 125129. http://doi.org/10.1016/j.psychres.2015.12.020.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Lecrubier, Y., Sheehan, D., Weiller, E., Amorim, P., Bonora, I., Sheehan, K. H., … Dunbar, G. (1997). The Mini international neuropsychiatric interview (MINI). A short diagnostic structured interview: Reliability and validity according to the CIDI. European Psychiatry, 12(5), 224231. http://doi.org/10.1016/S0924-9338(97)83296-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leonard, C. A., & Williams, R. J. (2015). Characteristics of good poker players. Journal of Gambling Issues, 31(0), 4568. http://doi.org/10.4309/jgi.2015.31.5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Z., Miao, C., & Zhang, Y. (2020). Human electrophysiology reveals delayed but enhanced selection in inhibition of return. Cognition, 205, 104462. http://doi.org/10.1016/j.cognition.2020.104462.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Lundqvist, D., Flykt, A., & Öhman, A. (1998). The Karolinska directed emotional faces – KDEF, CD ROM from department of clinical neuroscience. Karolinska Institutet.

    • Search Google Scholar
    • Export Citation
  • McCormack, A., Shorter, G. W., & Griffiths, M. D. (2014). An empirical study of gender differences in online gambling. Journal of Gambling Studies, 30(1), 7188. http://doi.org/10.1007/s10899-012-9341-x.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Meyer, G., von Meduna, M., Brosowski, T., & Hayer, T. (2013). Is poker a game of skill or chance? A quasi-experimental study. Journal of Gambling Studies, 29(3), 535550. http://doi.org/10.1007/s10899-012-9327-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olthuis, J. V., & Klein, R. M. (2012). On the measurement of the effects of alcohol and illicit substances on inhibition of return. Psychopharmacology, 221(4), 541550. http://doi.org/10.1007/s00213-012-2725-x.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Palomäki, J., Laakasuo, M., Cowley, B. U., & Lappi, O. (2020). Poker as a domain of expertise. Journal of Expertise, 3(2), 22.

  • Palomäki, J., Yan, J., Modic, D., & Laakasuo, M. (2016). ‘To bluff like a man or fold like a girl?’ – Gender biased deceptive behavior in online poker. Plos One, 11(7), e0157838. http://doi.org/10.1371/journal.pone.0157838.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pinkham, A. E., Griffin, M., Baron, R., Sasson, N. J., & Gur, R. C. (2010). The face in the crowd effect: Anger superiority when using real faces and multiple identities. Emotion, 10(1), 141146. http://doi.org/10.1037/a0017387.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Posner, M. (1980). Orienting of attention. The Quarterly Journal of Experimental Psychology: QJEP, 32, 325. http://doi.org/10.1080/00335558008248231.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Potter van Loon, R. J. D., van den Assem, M. J., & van Dolder, D. (2015). Beyond chance? The persistence of performance in online poker. Plos One, 10(3), e0115479. http://doi.org/10.1371/journal.pone.0115479.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Rinehart, N. J., Bradshaw, J. L., Moss, S. A., Brereton, A. V., & Tonge, B. J. (2008). Brief report: Inhibition of return in young people with autism and asperger’s disorder. Autism: The International Journal of Research and Practice, 12(3), 249260. http://doi.org/10.1177/1362361307088754.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Samuel, A. G., & Kat, D. (2003). Inhibition of return: A graphical meta-analysis of its time course and an empirical test of its temporal and spatial properties. Psychonomic Bulletin & Review, 10(4), 897906. http://doi.org/10.3758/BF03196550.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Satel, J., Wilson, N. R., & Klein, R. M. (2019). What neuroscientific studies tell us about inhibition of return. Vision, 3(4), 58. http://doi.org/10.3390/vision3040058.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Schiavella, M., Pelagatti, M., Westin, J., Lepore, G., & Cherubini, P. (2018). Profiling online poker players: Are executive functions correlated with poker ability and problem gambling? Journal of Gambling Studies, 34(3), 823851. http://doi.org/10.1007/s10899-017-9741-z.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Schlicht, E. J., Shimojo, S., Camerer, C. F., Battaglia, P., & Nakayama, K. (2010). Human wagering behavior depends on opponents’ faces. Plos One, 5(7), e11663. http://doi.org/10.1371/journal.pone.0011663.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Snijders, T. A. B., & Bosker, R. J. (1994). Modeled variance in two-level models. Sociological Methods & Research, 22(3), 342363. http://doi.org/10.1177/0049124194022003004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spielberger, C. D. (1999). Manual for the state-trait anger expression inventory-2. Odessa, FL: Psychological Assessment Ressources.

  • Talberg, O. N. (2019). Learning poker in different communities of practice: A qualitative analysis of poker players? Learning processes and the norms in different learning communities. Journal of Gambling Issues, 42(42), 841. http://doi.org/10.4309/jgi.2019.42.2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, Y., Li, Y., Zhuo, K., Wang, Y., Liao, L., Song, Z., … Liu, D. (2015). Neural correlates of the preserved inhibition of return in schizophrenia. Plos One, e0119521. http://doi.org/10.1371/journal.pone.0119521.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Vivas, A. B., Estevez, A. F., Moreno, M., Panagis, G., & Flores, P. (2012). Use of cannabis enhances attentional inhibition. Human Psychopharmacology: Clinical and Experimental, 27(5), 464469. http://doi.org/10.1002/hup.2248.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • American Psychiatry Association (2013). DSM-5: Diagnostic and statistical manual of mental disorders. Washington, DC: American Psychiatric Publishing.

    • Search Google Scholar
    • Export Citation
  • Antezana, L., Mosner, M. G., Troiani, V., & Yerys, B. E. (2016). Social-emotional inhibition of return in children with autism spectrum disorder versus typical development. Journal of Autism and Developmental Disorders, 46(4), 12361246. http://doi.org/10.1007/s10803-015-2661-9.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Bannerman, R. L., Milders, M., de Gelder, B., & Sahraie, A. (2009). Orienting to threat: Faster localization of fearful facial expressions and body postures revealed by saccadic eye movements. Proceedings of the Royal Society B: Biological Sciences, 276(1662), 16351641. http://doi.org/10.1098/rspb.2008.1744.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, A. T., Steer, R. A., & Carbin, M. G. (1988). Psychometric properties of the beck depression inventory: Twenty-five years of evaluation. Clinical Psychology Review, 8(1), 77100. https://doi.org/10.1016/0272-7358(88)90050-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bennett, P. J., & Pratt, J. (2001). The spatial distribution of inhibition of return. Psychological Science, 12(1), 7680. http://doi.org/10.1111/1467-9280.00313.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Billings, D., Davidson, A., Schaeffer, J., & Szafron, D. (2002). The challenge of poker. Artificial Intelligence, 134(1), 201240. http://doi.org/10.1016/S0004-3702(01)00130-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bjerg, O. (2010). Problem gambling in poker: Money, rationality and control in a skill-based social game. International Gambling Studies, 10(3), 239254. https://doi.org/10.1080/14459795.2010.520330.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bradley, B. P., Mogg, K., White, J., Groom, C., & De Bono, J. (1999). Attentional bias for emotional faces in generalized anxiety disorder. British Journal of Clinical Psychology, 38(3), 267278. http://doi.org/10.1348/014466599162845.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Brand, M., Wegmann, E., Stark, R., Müller, A., Wölfling, K., Robbins, T. W., & Potenza, M. N. (2019). The interaction of person-affect-cognition-execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neuroscience and Biobehavioral Reviews, 104, 110. http://doi.org/10.1016/j.neubiorev.2019.06.032.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Calvo, M. G., Avero, P., & Lundqvist, D. (2006). Facilitated detection of angry faces: Initial orienting and processing efficiency. Cognition & Emotion, 20(6), 785811. http://doi.org/10.1080/02699930500465224.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carter, R. McK., Bowling, D. L., Reeck, C., & Huettel, S. A. (2012). A distinct role of the temporal-parietal junction in predicting socially guided decisions. Science (New York, N.Y.), 337(6090), 109111. http://doi.org/10.1126/science.1219681.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Colzato, L. S., & Hommel, B. (2009). Recreational use of cocaine eliminates inhibition of return. Neuropsychology, 23(1), 125129. http://doi.org/10.1037/a0013821.

  • Croson, R., Fishman, P., & Pope, D. G. (2008). Poker superstars: Skill or luck? Chance, 21(4), 2528. http://doi.org/10.1007/s00144-008-0036-0.

    • Search Google Scholar
    • Export Citation
  • DeDonno, M. A., & Detterman, D. K. (2008). Poker is a skill. Gaming Law Review, 12(1), 3136. http://doi.org/10.1089/glr.2008.12105.

  • Fiedler, I. C., & Rock, J.-P. (2009). Quantifying skill in games—Theory and empirical evidence for poker. Gaming Law Review and Economics, 13(1), 5057. http://doi.org/10.1089/glre.2008.13106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fillmore, M. T., Rush, C. R., & Abroms, B. D. (2005). D-Amphetamine-Induced enhancement of inhibitory mechanisms involved in visual search. Experimental and Clinical Psychopharmacology, 13(3), 200208. http://doi.org/10.1037/1064-1297.13.3.200.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). ‘Mini-Mental state’. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189198. https://doi.org/10.1016/0022-3956(75)90026-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fox, E., Russo, R., & Dutton, K. (2002). Attentional bias for threat: Evidence for delayed disengagement from emotional faces. Cognition & Emotion, 16(3), 355379. http://doi.org/10.1080/02699930143000527.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gerstein, D., Hoffmann, J., Larison, C., Murphy, S., Palmer, A., Chuchro, L., … Sinclair, S. (1999). Gambling impact and behavior study. Report to the national gambling impact study commission. National Gambling Impact Study Commission (NORC).

    • Search Google Scholar
    • Export Citation
  • Gordillo León, F., Mestas Hernández, L., Pérez Nieto, M. Á., & Arana Martínez, J. M. (2021). Detecting emotion faces in a Posner’s spatial cueing task: The adaptive value of surprise. Journal of Cognitive Psychology, 33(1), 3848. http://doi.org/10.1080/20445911.2020.1862854.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hayward, D. A., & Ristic, J. (2013). Measuring attention using the posner cuing paradigm: The role of across and within trial target probabilities. Frontiers in Human Neuroscience, 7, 205. http://doi.org/10.3389/fnhum.2013.00205.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Klein, N. (2000). Inhibition of return. Trends in Cognitive Sciences, 4(4), 138147. http://doi.org/10.1016/s1364-6613(00)01452-2.

  • Kornreich, C., Saeremans, M., Delwarte, J., Noël, X., Campanella, S., Verbanck, P., … Brevers, D. (2016). Impaired non-verbal emotion processing in pathological gamblers. Psychiatry Research, 236, 125129. http://doi.org/10.1016/j.psychres.2015.12.020.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Lecrubier, Y., Sheehan, D., Weiller, E., Amorim, P., Bonora, I., Sheehan, K. H., … Dunbar, G. (1997). The Mini international neuropsychiatric interview (MINI). A short diagnostic structured interview: Reliability and validity according to the CIDI. European Psychiatry, 12(5), 224231. http://doi.org/10.1016/S0924-9338(97)83296-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leonard, C. A., & Williams, R. J. (2015). Characteristics of good poker players. Journal of Gambling Issues, 31(0), 4568. http://doi.org/10.4309/jgi.2015.31.5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Z., Miao, C., & Zhang, Y. (2020). Human electrophysiology reveals delayed but enhanced selection in inhibition of return. Cognition, 205, 104462. http://doi.org/10.1016/j.cognition.2020.104462.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Lundqvist, D., Flykt, A., & Öhman, A. (1998). The Karolinska directed emotional faces – KDEF, CD ROM from department of clinical neuroscience. Karolinska Institutet.

    • Search Google Scholar
    • Export Citation
  • McCormack, A., Shorter, G. W., & Griffiths, M. D. (2014). An empirical study of gender differences in online gambling. Journal of Gambling Studies, 30(1), 7188. http://doi.org/10.1007/s10899-012-9341-x.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Meyer, G., von Meduna, M., Brosowski, T., & Hayer, T. (2013). Is poker a game of skill or chance? A quasi-experimental study. Journal of Gambling Studies, 29(3), 535550. http://doi.org/10.1007/s10899-012-9327-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olthuis, J. V., & Klein, R. M. (2012). On the measurement of the effects of alcohol and illicit substances on inhibition of return. Psychopharmacology, 221(4), 541550. http://doi.org/10.1007/s00213-012-2725-x.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Palomäki, J., Laakasuo, M., Cowley, B. U., & Lappi, O. (2020). Poker as a domain of expertise. Journal of Expertise, 3(2), 22.

  • Palomäki, J., Yan, J., Modic, D., & Laakasuo, M. (2016). ‘To bluff like a man or fold like a girl?’ – Gender biased deceptive behavior in online poker. Plos One, 11(7), e0157838. http://doi.org/10.1371/journal.pone.0157838.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pinkham, A. E., Griffin, M., Baron, R., Sasson, N. J., & Gur, R. C. (2010). The face in the crowd effect: Anger superiority when using real faces and multiple identities. Emotion, 10(1), 141146. http://doi.org/10.1037/a0017387.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Posner, M. (1980). Orienting of attention. The Quarterly Journal of Experimental Psychology: QJEP, 32, 325. http://doi.org/10.1080/00335558008248231.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Potter van Loon, R. J. D., van den Assem, M. J., & van Dolder, D. (2015). Beyond chance? The persistence of performance in online poker. Plos One, 10(3), e0115479. http://doi.org/10.1371/journal.pone.0115479.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Rinehart, N. J., Bradshaw, J. L., Moss, S. A., Brereton, A. V., & Tonge, B. J. (2008). Brief report: Inhibition of return in young people with autism and asperger’s disorder. Autism: The International Journal of Research and Practice, 12(3), 249260. http://doi.org/10.1177/1362361307088754.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Samuel, A. G., & Kat, D. (2003). Inhibition of return: A graphical meta-analysis of its time course and an empirical test of its temporal and spatial properties. Psychonomic Bulletin & Review, 10(4), 897906. http://doi.org/10.3758/BF03196550.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Satel, J., Wilson, N. R., & Klein, R. M. (2019). What neuroscientific studies tell us about inhibition of return. Vision, 3(4), 58. http://doi.org/10.3390/vision3040058.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Schiavella, M., Pelagatti, M., Westin, J., Lepore, G., & Cherubini, P. (2018). Profiling online poker players: Are executive functions correlated with poker ability and problem gambling? Journal of Gambling Studies, 34(3), 823851. http://doi.org/10.1007/s10899-017-9741-z.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Schlicht, E. J., Shimojo, S., Camerer, C. F., Battaglia, P., & Nakayama, K. (2010). Human wagering behavior depends on opponents’ faces. Plos One, 5(7), e11663. http://doi.org/10.1371/journal.pone.0011663.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Snijders, T. A. B., & Bosker, R. J. (1994). Modeled variance in two-level models. Sociological Methods & Research, 22(3), 342363. http://doi.org/10.1177/0049124194022003004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spielberger, C. D. (1999). Manual for the state-trait anger expression inventory-2. Odessa, FL: Psychological Assessment Ressources.

  • Talberg, O. N. (2019). Learning poker in different communities of practice: A qualitative analysis of poker players? Learning processes and the norms in different learning communities. Journal of Gambling Issues, 42(42), 841. http://doi.org/10.4309/jgi.2019.42.2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, Y., Li, Y., Zhuo, K., Wang, Y., Liao, L., Song, Z., … Liu, D. (2015). Neural correlates of the preserved inhibition of return in schizophrenia. Plos One, e0119521. http://doi.org/10.1371/journal.pone.0119521.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Vivas, A. B., Estevez, A. F., Moreno, M., Panagis, G., & Flores, P. (2012). Use of cannabis enhances attentional inhibition. Human Psychopharmacology: Clinical and Experimental, 27(5), 464469. http://doi.org/10.1002/hup.2248.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand
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
  • CABELLS Journalytics

2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

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

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
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

  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Luke CLARK (University of British Columbia, Canada)
  • Daniel KING (Flinders University, Australia)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • H. N. Alexander LOGEMANN (ELTE Eötvös Loránd University, Hungary)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)

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)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • 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)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • 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 (Humboldt-Universität zu Berlin, Germany)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Astrid MÜLLER  (Hannover Medical School, Germany)
  • 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)
  • Róbert URBÁN  (ELTE Eötvös Loránd University, Hungary)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN  (Ariel University, Israel)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Aug 2022 0 0 0
Sep 2022 0 0 0
Oct 2022 0 0 0
Nov 2022 0 0 0
Dec 2022 0 0 0
Jan 2023 0 279 138
Feb 2023 0 0 0