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Damien Brevers Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA, USA
Psychological Medicine and Addictology Laboratory, Faculty of Medicine, Brugmann-campus, Université Libre de Bruxelles, Brussels, Belgium

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Xavier Noel Psychological Medicine and Addictology Laboratory, Faculty of Medicine, Brugmann-campus, Université Libre de Bruxelles, Brussels, Belgium

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Luke Clark Centre for Gambling Research, Department of Psychology, University of British Columbia, Vancouver, Canada

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Jekaterina Zyuzin Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA, USA

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Joohwan Justin park Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA, USA

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Antoine Bechara Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA, USA

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Background and aims

Precommitment refers to the ability to prospectively restrict the access to temptations. This study examined whether risk-taking during gambling is decreased when an individual has the opportunity to precommit to his forthcoming bet.

Methods

Sixty individuals participated in a gambling task that consisted of direct choice (simply chose one monetary option among four available ones, ranging from low-risk to high-risk options) or precommitment trials (before choosing an amount, participants had the opportunity to make a binding choice that made high-risk options unavailable).

Results

We found that participants utilized the precommitment option, such that risk-taking was decreased on precommitment trials compared to direct choices. Within the precommitment trials, there was no significant difference in risk-taking following decisions to restrict versus non-restrict.

Discussion

These findings suggest that the opportunity to precommit may be sufficient to reduce the attractiveness of risk.

Conclusions

Present results might be exploited to create interventions aiming at enhancing one’s ability to anticipate self-control failures while gambling.

Abstract

Background and aims

Precommitment refers to the ability to prospectively restrict the access to temptations. This study examined whether risk-taking during gambling is decreased when an individual has the opportunity to precommit to his forthcoming bet.

Methods

Sixty individuals participated in a gambling task that consisted of direct choice (simply chose one monetary option among four available ones, ranging from low-risk to high-risk options) or precommitment trials (before choosing an amount, participants had the opportunity to make a binding choice that made high-risk options unavailable).

Results

We found that participants utilized the precommitment option, such that risk-taking was decreased on precommitment trials compared to direct choices. Within the precommitment trials, there was no significant difference in risk-taking following decisions to restrict versus non-restrict.

Discussion

These findings suggest that the opportunity to precommit may be sufficient to reduce the attractiveness of risk.

Conclusions

Present results might be exploited to create interventions aiming at enhancing one’s ability to anticipate self-control failures while gambling.

Introduction

Not all risk-taking is created equal: some risks are taken under a clear and rational frame of mind, while others are made in a less planful fashion (Helfinstein et al., 2014). A major issue with spontaneous risk-taking is that it is taken in the “heat of the moment”, without more “cooler” elaborated cognitive control, which may, in turn, hamper one’s ability to pursue risk appropriately (Cohen & Lieberman, 2010; Knoch et al., 2006). Put differently, inducing cautiousness before deciding might enhance cognitive control, and in turn, would reduce the level of risk-taking. For instance, monetary risk-taking is diminished when imposing a delay before the next bet, as compared to a situation in which participants could bet directly (Newman, Patterson, & Kosson, 1987; Thompson & Corr, 2013, 2014). Others studies have shown that monetary risk-taking is lowered in a gambling context in which participants were required to stop their motor response (i.e., choosing a bet option), as compared to a condition without stop signals (Stevens et al., 2015; Verbruggen, Adams, & Chambers, 2012).

Another possible alternative for diminishing impulsive risk-taking would be to allow people to precommit to their forthcoming choice. Precommitment refers to the ability to anticipate self-control failures and prospectively restrict the access to temptations (Fujita, 2011; Kalenscher & Pennartz, 2008; Kurth-Nelson & Redish, 2010, 2012). Findings from several studies highlighted that precommitment is an efficient strategy to resist short-term temptations in order to achieve long-term goals (Ariely & Wertenbroch, 2002; Ashraf, Karlan, & Yin, 2006; Giné, Karlan, & Zinman, 2010; Schwartz et al., 2014; Wertenbroch, 1998). These studies have shown that precommitment could help people to restrict themselves from (e.g.) smoking (Giné et al., 2010) or eating unhealthy food (Schwartz et al., 2014). For instance, individuals who agreed to pledge that they would increase their purchases of healthy food (at some risk of financial loss if the goal was not reached) subsequently showed an increase in healthy grocery items purchased, as compared with individuals who declined the commitment (Schwartz et al., 2014). This restriction process was framed on a single precommitment decision phase (e.g., to choose or not to precommit to a grocery-shopping program; Schwartz et al., 2014), separated from hours, days or even weeks from the actual temptation. Hence, while those studies highlighted that precommitment could reduce the incidence of self-control failure on several types of behaviors, this research only covers a certain type of situations where precommitment could impact decision-making. More specifically, studies are needed in order to examine the impact of pre-commitment processes that allow people to restrict themselves when choices are made repeatedly within the “heat of the moment”, that is, on a trial-per-trial basis in which an individual has the opportunity to precommit his/her forthcoming decision, such as during monetary risk-taking in gambling.

The trial-per-trial impact of precommitment was recently studied within an experiential delay-discounting task (Crockett et al., 2013), where the opportunity to make a binding choice was more effective at promoting selection of the larger-later option than a ‘willpower’ condition that was required sustained inhibitory control. These findings led us to hypothesize that precommitment could also impact sequential decision-making that involves probabilistic rewards, that is, gambling. Precommitment strategies are already provided within casino-settings to individuals who want to limit their level of monetary expense prior to gambling (Nower & Blaszczynski, 2010). For instance, gamblers can deposit predetermined amounts of money on “smart cards” or can also gamble on slot machines that enforce pre-set limits of money expense, after which the machine deactivates (Ladouceur, Blaszczynski, & Lalande, 2012; Nower & Blaszczynski, 2010). Nevertheless, the recent expansion of the gambling computer technology has facilitated and diversified the access to situations involving high monetary risk-taking (e.g., online poker, sport betting; Khazaal et al., 2013; Petry & Weinstock, 2007). Indeed, recent advances in online sport betting not only allow to place bets on several ongoing live sport competitions but also to bet on specific events occurring during the game. Hence, one major issue with the online gambling environment is that it may increase the development of abnormal monetary risk-taking by affording much faster response times as the usual constraints of casino settings (e.g., time delay to get to the casino, various delays imposed by the actions of other gamblers, presence of a dealer). In this context, it is important to examine whether precommitment could help one to restrict himself when the temptation is closer in time and occur repeatedly over a certain period of time.

The action of “gambling” in the present study consisted of choosing one of four monetary amounts. Participants were informed that the higher the amount, the less probable a win and than a loss corresponded to half of the amount they gambled with. Thus, selecting higher amounts constituted a more risky bet, whereas selecting lower amounts constituted a safer bet (but without having explicit information on win/loss probability). Risk-taking in our task consisted of preferring relatively higher amounts that carried a higher probability of losing (and in the case of the riskiest options, a negative expected value) over lower amounts that carried a lower probability of losing (Boyer, 2006; Verbruggen et al., 2012). This is the same behavior that problem gamblers engage in, for example, sport betting. In this context, self-control refers here as the ability to sacrifice the opportunity to experience high monetary reward in favor of lower but less risky rewards, that is, acting consistently with one’s more abstract distal motivation (e.g., to win money at the end of the experiment; Boyer et al., 2006; Fujita, 2011). Self-control failure, on the other hand, entails sacrificing one’s distal concerns of long-term monetary gains in favor of the possibility of enjoying higher but less probable reward (e.g., risk-seeking choice), that is, acting in a manner consistent with one’s concrete, proximal incentive (Dill & Holton, 2014; Fujita, 2011). Hence, each trial of our gambling paradigm triggers a self-control conflict because the participant’s distal and proximal motivations pressed for opposing actions. Using this gambling paradigm, we expected that participants would take less risk (i.e., enhanced self-control) when they have the opportunity to precommit to their forthcoming bet, that is, to make a binding choice that turns high-risk bet unavailable for choice, as compared with situations in which participants can gamble directly.

Finally, an exploratory aim of this study was to examine whether the association between sensitivity to reward, sensitivity to punishment and risk-taking could be modulated by the type of trials (precommitment or direct choice). Indeed, some studies highlighted that sensitivity to reward and to punishment is predictive of monetary risk-taking (Franken & Muris, 2005; Goudriaan, Oosterlaan, de Beurs, & van den Brink, 2006; Kim & Lee, 2011; Studer & Clark, 2011; Suhr & Tsanadis, 2007). More specifically, in risky choice situations, high sensitivity to punishment should promote avoidance of potential losses and thereby lead to risk-averse behavior. In contrast, sensitivity to reward might lead to risk-seeking behavior. In this study, we examined whether precommitment could have a “protective” effect on the impact of sensitivity to punishment and reward on monetary risk-taking. Hence, we expected that risk-taking will negatively correlate with sensitivity to punishment, and positively correlate with sensitivity to reward for the direct choice trials, but not for the precommitment trials.

Methods

Participants

Sixty undergraduate students from the University of Southern California (USC), College of Letters, Art, and Sciences participated in this study (see Table 1 for participants’ characteristics).

Table 1.

Demographical data and scores on the BIS/BAS (reward sensitivity) and the SOGS in the Precommitment First (PF), the Direct Choice First (DCF) and the Intermittent Precommitment (IP) conditions

PF DCF IP Test statistics
n 20 20 20
Male/Female 10/10 10/10 10/10
Age 20.83(2.28) 20.40(1.64) 21.20(2.54) F(59) = 0.62, p = .54
BIS 15.00(3.41) 13.10(3.67) 14.45(2.18) F(59) = 1.91, p = .16
BAS reward sensitivity 8.30(2.20) 7.60(2.09) 7.55(2.24) F(59) = 0.743, p = .48
SOGS 0.50(0.88) 0.90(1.02) 0.75(1.06) F(59) = 0.082, p = .44
Table 2.

Risk-taking under precommitment and direct choice trials in each trial-order between-groups conditions (Direct Choice First, Precommitment First, Intermittent Precommitment)

Direct choice Precommitment
25 first trials 25 last trials 50 trials 25 first trials 25 last trials 50 trials
Conditions M sd M sd M sd M sd M sd M sd
Direct Choice First 1473 528 1428 515 1451 521 1474 748 1180 609 1325 678
Precommitment First 1218 696 1071 768 1146 732 1195 649 931 538 1061 593
Intermittent Precommitment 1290 776 1339 706 1313 741 983 647 1059 676 1064 662

M = mean; sd = standard deviation.

Gambling task

Stimuli were presented on a 19-inch LCD monitor against a black background. The task was run using E-Prime 2.0 Professional. The current procedure was based on a previous study by Crockett et al. (2013), and it consisted of two types of trials: direct choice and precommitment trials. During the direct choice trials (see Figure 1a), participants simply chose one monetary gamble from four available options by pressing the “f”, “g”, “h” or “j” key on the keyboard. During the precommitment trials (see Figure 1b), participants had, first, to decide whether to restrict their forthcoming choice. If participants chose to precommit, the two riskier options were made unavailable (i.e., crossed-out, see Figure 1b). If participants chose not to commit, they faced a choice situation in which the four amounts were available. For the half of the trials, the situation depicting the two crossed-out options was displayed on the left of the screen, and the situation with the four available options was displayed on the right of the screen, with these positions reversed for the other half of the trials. Participants chose the situation they wanted to face with left- and right-button presses via the keys “1” and “2” of the keyboard, respectively. Across each trial, the four choice options were randomly chosen among the following amounts (depicted in ¢; expected value, EV, were based on Verbruggen et al., 2012): 100 [pwin = .90; EV = 85], 200 [pwin = .80; EV = 140], 400 [pwin = .70; EV = 220], 600 [pwin = .50; EV = 150], 700 [pwin = .40; EV = 70], 900 [pwin = .30; EV = −450], 1000 [pwin = .20; EV = −2000]. A loss corresponded to the half of the amount selected (e.g., corresponding loss for the ¢100 amount corresponded to ¢−50). The amounts were aligned in a random order to prevent choices from being driven by spatial-attention or response-bias effects (which might occur, for example, if higher amounts were consistently presented on the right of the screen).

Figure 1.
Figure 1.

(a) An example of direct choice trial. After seeing a fixation cross (1000 ms), participants simply chose between four options associated with different monetary amounts and received a feedback (2000 ms), either a win or a loss. A loss corresponds to the half of the amount selected. (b) An example of precommitment trial. After seeing a fixation cross (1000 ms), participants have to decide whether to face (i) a choice situation in which the four-choice options are available or either (ii) a situation in which the two riskier options are made unavailable (crossed-out options). After choosing the situation they want to face, they choose an amount and then receive a feedback (2000 ms), either a win or a loss

Citation: Journal of Behavioral Addictions 5, 1; 10.1556/2006.5.2016.008

In total, there were 50 direct choice trials and 50 precommitment trials. There was no decision-time limit. Each trial was separated by a fixation cross (1000 ms). The succession between direct choice trials and precommitment trials differed across three between-subjects conditions: “Direct Choice First” (DCF), “Precommitment First” (PF), and “Intermittent Precommitment” (IP). In the DCF condition, 50 direct choice trials were followed by 50 precommitment trials. In the PF condition, 50 precommitment trials were followed by 50 direct choice trials. The IP condition consisted of a random succession of direct choice trials (n = 50) and precommitment trials (n = 50). This between-groups condition was implemented to our design in order to control for the possibility that the impact of trial types (precommitment vs. direct choice) on risk-taking interacts with a specific dynamic of trials order presentation. Participants were divided equally between the DCF (n = 20), the PF (n = 20) and the IP conditions (n = 20) and were paired on gender across each condition (see Table 1). The number of participants to include was set a priori based on previous studies examining the effect of situational factors on monetary risk-taking (Thompson & Corr, 2013, 2014; Verbruggen et al., 2012). New participants were tested until that number was reached.

For each participant, index of risk-taking was first calculated on a trial-by-trial basis which corresponded to the value of the amount selected multiplied by the range of risk-taking [range = 1, 2, 3 or 4; when 1 corresponded to the smallest amount, which had the highest probability of winning (the safest bet) and 4 to the highest amount, which had the lowest probability of winning (the riskiest bet)]. These indices (range = 100–4000) were then averaged separately for the 50 direct choice trials and the 50 precommitment trials. Higher scores indicate higher risk-taking.

Reward and punishment sensitivity

The BIS/BAS self-report scale was administered (Carver & White, 1994). This scale measures affective responses to impending rewards (BAS) or punishments (BIS) and contains 20 items with a four-point Likert scale ranging from “strongly agree” to “strongly disagree”. The BAS items are divided into three subcategories: BAS drive, BAS reward sensitivity, and BAS fun seeking. The BIS subscale has no subcategories and measures only punishment sensitivity. The BIS subscale (seven items) and the BAS reward sensitivity subscale (five items) were used in this study because our primary research goal was to measure the influence of reward and punishment sensitivity on monetary risk-taking. Adequate reliability for the BIS punishment sensitivity subscale (Cronbach’s α = 0.74) and for the BAS reward sensitivity subscale (Cronbach’s α = 0.70) was established in this study.

Problem gambling severity

We used the 20-item South Oaks Gambling Screen (SOGS; Lesieur & Blume, 1987) to evaluate the participants’ gambling-related behavior and problems. The SOGS is a widely used screening instrument for problem gambling and shows good reliability and validity in the community and clinical samples (Lesieur & Blume, 1987). A total SOGS score of 5 or higher is typically used to classify probable pathological gambling (with scores between 1 and 4 indicating some problems with gambling; Lesieur & Blume, 1987).

Procedure

After having signed an IRB-approved informed consent form, all participants (N = 60) received the same task instructions and were ignorant about the logic of trials succession order. They were informed that the probability of winning decreased as the amount increased and that a loss corresponded to the half of the amount selected. Each participant received a fixed remuneration of $5, and another $5 to gamble with. The two $5 bills were placed next to the experimenter’s computer so that the participant could see it. Subjects were informed that, at the end of the experiment, their total amount will be added (if > ¢0) or subtracted (if < ¢0) from their $5 gambling balance. At the end of each trial (i.e., after choosing an amount), participants were told how much they had won or lost (2000 ms), but they received no information about their current balance during the task. The experiment started with a short practice phase that consisted of two direct choice trials and two precommitment trials. Directly after the task completion, participants were given the total amount won or lost during the task. They were then asked to fill out the BIS/BAS scale and the SOGS.

Ethics

The study procedures were carried out in accordance with the Declaration of Helsinki. Each participant gave informed consent to the experimental procedure, which was approved by the USC Institutional Review Board. Participants received $5 fixed remuneration, plus money won during the gambling task.

Results

Participant characteristics

Participants from the DCF, the PF and the IP conditions were similar in age, sensitivity to reward (scores on the BAS reward sensitivity subscale from the BIS/BAS) and sensitivity to punishment (scores on the BIS scale from the BIS/BAS). On the SOGS, no participant reported a score ≥4 (which corresponds to probable pathological gambling). Moreover, only 4 participants reported gambling once or more a week (playing cards for money or betting on the lottery). These results are depicted in Table 1. We observed no significant correlation between BIS/BAS (reward sensitivity subscale), SOGS, and age (all p > .061).

Risk-taking under precommitment vs. direct choice

Mixed-model ANOVA was used with the type of trials (direct choice vs. precommitment) and times (25 first trials vs. 25 last trials) as a within-subjects factor; trials succession conditions (DCF vs. PF vs. IP) as a between-subjects factor; and scores of risk-taking as a dependent measure (see Table 2 for descriptive statistics). These analyses revealed a main effect of the type of trials, F(1,59) = 6.89, p = .011, η2 = .11, indicating that risk-taking was lower during the precommitment trials than during the direct choice trials (see Figure 2). We also observed a main effect of Time, F(1,59) = 4.09, p = .048, η2 = .07, indicating that risk-taking was lower in the last 25 trials for both precommitment and direct choice trials. There was no main effect of the trials’ succession conditions and no interaction effect between the type of trials, time and the trials’ succession conditions (all p > .25).

Figure 2.
Figure 2.

Average score of risk-taking (range = 100–4000) for the Direct Choice and the Precommitment trials

Citation: Journal of Behavioral Addictions 5, 1; 10.1556/2006.5.2016.008

We observed no significant correlation between risk-taking (for both 25 first and 25 last trials of the direct choice and precommitment conditions) and individual variables (BIS/BAS, SOGS, and age; all p > .22). Independent sample t-tests revealed that gender had no significant effect on risk-taking (all p > .14).

Complementary analyses: Risk-taking within precommitment trials

Additional analyses were undertaken within precommitment trials (n = 50) in order to examine the average of risk-taking when choosing under the crossed-out situation versus under the situation in which the four amounts were available. ANOVA was undertaken with the type of situation faced during the precommitment trials (choice with four options vs. choices with the two less risky options) as a within-subjects factor; trials succession conditions (DCF vs. PF vs. IP) as a between-subjects factor; and scores of risk-taking as a dependent measure (see Table 3 for descriptive statistics). These analyses revealed no significant main effect of the type of situations faced (p = .12), the trial order conditions (p = .33), and no interaction effect between the type of situations and the trial order conditions (p = .63). On precommitment trials, participants opted to restrict their options on 54.2% of trials (mean 27.1 of the 50 precommitment trials).

Table 3.

Risk-taking under precommitment trials, when choosing to face the crossed-out situation (with the two riskier options unavailable for choosing) and under the non-crossed-out situation (with the four amounts available for choosing), separately for the Direct Choice First, Precommitment First, and Intermittent Precommitment conditions

Crossed-out Non-crossed-out
M sd M sd
Direct Choice First 1358 302 1438 354
Precommitment First 1336 252 1380 209
Intermittent Precommitment 1492 402 1535 419

M = mean; sd = standard deviation.

Association between sensitivity to reward/punishment and risk-taking

Pearson correlation analyses have been undertaken separately for the DCF, the PF, and the IP conditions, for both the precommitment trials and the direct choice trials. Results showed that sensitivity for punishment (scores on the BIS subscale of the BIS/BAS) was negatively correlated with risk-taking during the direct choice trials in the DCF condition (n = 20; r = −.53, p = .015) but not in the PF condition (n = 20; r = .28, p = .22), neither in the IP condition (n = 20; r = −.043, p = .86). No significant correlation was observed with self-reported scores of sensitivity to punishment and risk-taking during the precommitment trials. No significant correlation was observed with self-reported scores of sensitivity to reward (scores on the BAS reward sensitivity subscale of the BIS/BAS).

Discussion

In the present study, monetary risk-taking was reduced when participants had the opportunity to precommit to their forthcoming gamble, that is, to make a binding choice that turned high-risk option unavailable for choice compared to a direct choice where this option was not presented. Importantly, within precommitment trials, participants were not less risky after restricting compared to when electing to face the four options.

This finding suggests that risk-taking was not moderated by precommitment per se (i.e., deciding to face the “crossed-out” situations), but rather by enforcing a “precommitment-oriented” break before deciding. Hence, precommitment could impact self-control at a deliberative stage (i.e., when the agent forms a judgment as to what action is best; Dill & Holton, 2014; Vohs et al., 2008), and in turn enhance one’s ability to exert self-control in choosing (i.e., a volitional stage of self-control) and executing (i.e., an implemental stage of self-control) an action (for a detailed description of these three stages of self-control, see Dill & Holton, 2014). This assumption is in line with recent brain-imaging findings (Crockett et al., 2013), which highlighted higher brain activation within the lateral frontopolar cortex (a brain region notably involved in prospective valuation and counterfactual thinking; Burgess, Dumontheil, & Gilbert, 2007; Tsujimoto, Genovesio, & Wise, 2011) while people had to decide whether to precommit or not their forthcoming choice toward a larger-later reward rather than a smaller-sooner reward. Hence, this pre-choice “precommitment-oriented” period may have triggered prospective (“look-ahead”) reflective processes allowing to compute and to compare expected values attached to specific alternatives or courses of action. Nevertheless, the observed decrease of risk-taking under precommitment trials could be also explained by alternative and/or complementary mechanisms. For instance, crossing out the two higher bet options during the first phase of each precommitment trial (i.e., deciding whether to commit a forthcoming choice; see also Figure 1) might have lowered the perceptual motivational value of those bets. Moreover, in the present design, the bet options were not shown an equal amount of time under direct choice and precommitment trials. Hence, diminished risk-taking under precommitment trials might also be due to the enhanced bet choice delay. Indeed, recent studies showed that monetary risk-taking is diminished when imposing a delay before the next bet, as compared to a situation in which participants could bet directly (Newman et al., 1987; Thompson & Corr, 2013, 2014). Thus, in this context, precommitment trials might have maximized the opportunity for a “cooling off” period, allowing one to decide in a more rational – less emotionally oriented – way (Ladouceur et al., 2012; Nower & Blaszczynski, 2010). Future studies are needed to address these issues in order to gain more insight on alternative mechanisms that could further explain diminished risk-taking under precommitment.

We also observed that the degree of risk-taking decreased throughout the task, and independently of trial types. This finding is consistent with previous studies on risk-taking (Stevens et al., 2015; Verbruggen et al., 2102) and suggests that participants learned to be more cautious in their choices after having experienced several wins and losses. The effect of time also indicates that precommitment had an effect on risk-taking at both the earlier and latter phases of the gambling task (i.e., when the participant had more insight on the value associated with each option). In other words, this finding suggests that precommitment might impact on both decision-making under ambiguity (i.e., situations with missing information on the reward/loss probability) and decision-making under risk (which offers full information on reward and loss associated with a choice option). Nevertheless, additional studies are needed to specifically address this question (e.g., by comparing a condition in which exact probabilities are revealed and a condition with missing information on reward/loss probability).

Sensitivity to punishment was negatively associated with risk-taking only for the direct choice trials of the “Direct Choice First” (DCF) condition. Thus, low sensitivity to punishment led to higher monetary risk-taking during an initial phase of non-restricted gambling (i.e., the 50 direct choice trials of the DCF condition), but not during gambling characterized by intermittent phase of precommitment (i.e., the Intermittent Precommitment condition), or when the phase of non-restricted gambling occurred after a phase of gambling with precommitment (i.e., the Precommitment First condition). In addition, there was no association between sensitivity to punishment and risk-taking during the precommitment trials, which suggests that punishment sensitivity may have no impact on risk-taking when people are given the opportunity to precommit their forthcoming choice. This finding also confirms the construct validity of our gambling task.

A main limitation of the present study is that it does not include any measure of the emotional responses triggered by the action of gambling throughout the task (e.g., using online psychophysiological or functional imaging measures). In other words, we do not know if the observed impact of precommitment on risk-taking is either hampered or enhanced by the participants’ emotional involvement while gambling. Moreover, we did not control for the impact of previous decision outcome (loss vs. rewards) on the participants’ subsequent decision. Indeed, decision-making involving independent events is often biased by prior outcomes. For instance, individuals are often more risk-seeking after losing than after winning a gamble (e.g., Xue, Lu, Levin, & Bechara, 2011), a pattern which is often referred to as the “gambler’s fallacy” (Laplace, 1820). Hence, future studies are needed in order to specifically examine whether previous choice outcomes modulate the impact of precommitment on monetary risk-taking. One option would be to use a controlled task that allows manipulating both prior outcomes and subsequent trials (e.g., precommitment trial vs. direct choice trial involving the same bet options; for an example of a task design, see Xue, Lu, Levin, & Bechara, 2010 and Xue et al., 2011).

While these findings remain preliminary, it has some potential clinical relevance. First, the induction of precommitment-oriented thinking could be an effective strategy to ration access to “vices” during online gambling, such as sport betting. This contemporary and increasingly popular form of gambling affords fast sequential betting (e.g., to bet on different games occurring simultaneously; to bet on forthcoming events of an ongoing game) using one or multiple online betting platforms. In this context, one could benefit from sequential inductions of precommitment-oriented strategy to ration access to “vices”. This calls for research on precommitment using more ecological designs. Second, present findings suggest that precommitment might diminish the impact of sensitivity to punishment on monetary risk-taking. Hence, it could be that precommitment diminishes risk-taking in individuals with low level of punishment sensitivity, such as pathological gamblers and casino gamblers in particular (Goudriaan, Oosterlaan, de Beurs, & van den Brink, 2005; van Holst, van den Brink, Veltman, & Goudriaan, 2010). Nevertheless, it remains to examine whether the observed effect of precommitment on monetary risk-taking generalizes to a sample of gamblers, which has both extreme ends of the spectrum of gambling disorder well represented. For instance, with regard to proactive motor response inhibition, recent evidence showed that occasionally stopping a response decreases monetary risk-taking in low-problem gamblers but not in high-problem gamblers (Stevens et al., 2015). Thus, future studies are needed to examine whether precommitment has an impact on monetary risk-taking during more ecological gambling situations and in participants who are used to behave in such context (i.e., frequent/problem gamblers). Another interesting question to examine is whether precommitment could help one at stopping his/her gambling session. One option would be to use a paradigm in which the gambler has some levels of agency on the decision to continue or stop his/her gambling session (e.g., to have the opportunity to precommit the number of trials to play). Ultimately, this line of research might result in the development of new strategies that would enhance gamblers’ capacity to control their gambling, rather than cease it, which is relevant to gamblers generally.

Author’s contribution

DB, XN, LC, JZ and AB designed the study and wrote the protocol. DB conducted literature searches and provided summaries of previous research studies. DB, JZ and JJP recruited the participants and collected the data. DB, JZ and JJP conducted the statistical analysis. DB wrote the first draft of the manuscript and all authors contributed to and have approved the final manuscript.

Conflict of interest

The authors report no financial or other relationship relevant to the subject of this article.

Acknowledgements

The authors thank Natalie Poppa for her advice on data analysis.

References

  • Ariely, D. , & Wertenbroch, K. (2002). Procrastination, deadlines, and performance: Self-control by precommitment. Psychological Science, 13, 219224.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashraf, N. , Karlan, D. , & Yin, W. (2006). Tying Odysseus to the mast: Evidence from a commitment savings product in the Philippines. The Quarterly Journal of Economics, 121, 635672.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boyer, T. W. (2006). The development of risk-taking: A multiperspective review. Developmental Review, 26, 291345.

  • Burgess, P. W. , Dumontheil, I. , & Gilbert, S. J. (2007). The gateway hypothesis of rostral prefrontal cortex (area 10) function. Trends in Cognitive Sciences, 11, 290298.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carver, C. S. , & White, T. L. (1994). Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS scales. Journal of Personality Social Psychology, 67, 319333.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohen, J. R. , & Lieberman, M. D. (2010). The common neural basis of exerting self-control in multiple domains. In K. Ochsner, & Y. Trope (Eds.), From society to brain: The new sciences of self-control (pp. 141160). New York, NY: Oxford University Press.

    • Search Google Scholar
    • Export Citation
  • Crockett, M. J. , Braams, B. R. , Clark, L. , Tobler, P. N. , Robbins, T. W. , & Kalenscher, T. (2013). Restricting temptations: Neural mechanisms of precommitment. Neuron, 79, 391401.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dill, B. , & Holton, R. (2014). The addict in us all. Frontiers in Psychiatry, 5, 139.

  • Franken, I. H. A. , & Muris, P. (2005). Individual differences in decision-making. Personality and Individual Differences, 39, 991998.

  • Fujita, K. (2011). On conceptualizing self-control as more than the effortful inhibition of impulses. Personality and Social Psychology Review, 15, 352366.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giné, X. , Karlan, D. , & Zinman, J. (2010). Put your money where your butt is: A commitment contract for smoking cessation. American Economic Journal: Applied Economics, 2, 213235.

    • Search Google Scholar
    • Export Citation
  • Goudriaan, A. E. , Oosterlaan, J. , de Beurs, E. , & van den Brink, W. (2005). Decision making in pathological gambling: A comparison between pathological gamblers, alcohol dependents, persons with Tourette syndrome, and normal controls. Cognitive Brain Research, 23, 137151.

    • 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, 231239.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Helfinstein, S. M. , Schonberg, T. , Congton, E. , Karlsgodt, K. H. , Mumford, J. A. , Sabb, F. W. , Cannon, T. D. , London, E. D. , Bilder, R. M. , & Poldrack, R. A. (2014). Predicting risky choices from brain activity patterns. Proceedings of the National Academy of Sciences, 111, 24702475.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalenscher, T. , & Pennartz, C. M. (2008). Is a bird in the hand worth two in the future? The neuroeconomics of intertemporal decision-making. Progress in Neurobiology, 84, 284315.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khazaal, Y. , Chatton, A. , Bouvard, A. , Khiari, H. , Achab, S. , & Zullino, D. (2013). Internet poker websites and pathological gambling prevention policy. Journal of Gambling Studies, 29, 5159.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, D. Y. , & Lee, J. H. (2011). Effects of the BAS and BIS on decision-making in a gambling task. Personality and Individual Differences, 50, 11311135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knoch, D. , Gianotti, L. R. R. , Pascual-Leone, A. , Treyer, V. , Regard, M. , Hohmann, M. , & Brugger, P. (2006). Disruption of right prefrontal cortex by low-frequency repetitive transcranial magnetic stimulation induces risk-taking behavior. The Journal of Neuroscience, 26, 64696472.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurth-Nelson, Z. , & Redish, A. D. (2010). A reinforcement learning model of precommitment in decision making. Frontiers in Behavioral Neuroscience, 4, 184.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurth-Nelson, Z. , & Redish, A. D. (2012). Don’T let me do that!– models of precommitment. Frontiers in Neuroscience, 6, 138.

  • Ladouceur, R. , Blaszczynski, A. , & Lalande, D. R. (2012). Pre-commitment in gambling: A review of the empirical evidence. International Gambling Studies, 12, 116.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laplace, P. (1820). Philosophical essays on probabilities (F. W. Truscott, & F. L. Emory, Trans.). New York: Dover.

  • Lesieur, H. R. , & Blume, S. B. (1987). The South Oaks Gambling Screen (SOGS): A new instrument for the identification of pathological gamblers. American Journal of Psychiatry, 144, 11841188.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newman, J. P. , Patterson, C. M. , & Kosson, D. S. (1987). Response perseveration in psychopaths. Journal of Abnormal Psychology, 96, 145148.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nower, L. , & Blaszczynski, A. (2010). Gambling motivations, money-limiting strategies, and precommitment preferences of problem versus non-problem gamblers. Journal of Gambling Studies, 26, 361372.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petry, N. M. , & Weinstock, J. (2007). Internet gambling is common in college students and associated with poor mental health. The American Journal on Addictions, 16, 325330.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, J. , Mochon, D. , Wyper, L. , Maroba, J. , Patel, D. , & Ariely, D. (2014). Healthier by precommitment. Psychological Science, 25, 538546.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevens, T. , Brevers, D. , Chambers, C. D. , Lavric, A. , McLaren, I. P. L. , Mertens, M. , Noël, X. , & Verbruggen, F. (2015). How does response inhibition influence decision-making when gambling? Journal of Experimental Psychology: Applied, 21, 1536. doi: 10.1037/xap0000039

    • Search Google Scholar
    • Export Citation
  • Studer, B. , & Clark, L. (2011). Place your bets: Psychophysiological correlates of decision-making under risk. Cognitive, Affective, and Behavioral Neuroscience, 11, 144158.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Suhr, J. A. , & Tsanadis, J. (2007). Affect and personality correlates of the Iowa Gambling Task. Personality and Individual Differences, 43, 2736.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, S. , & Corr, P. J. (2013). A feedback-response pause normalises response perseveration deficits in pathological gamblers. International Journal of Mental Health and Addiction, 11, 601610.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, S. , & Corr, P. J. (2014). Pause for thought: Response perseveration and personality in gambling. Journal of Gambling Studies, 30, 889900. doi: 10.1007/s10899-013-9395-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsujimoto, S. , Genovesio, A. , & Wise, S. P. (2011). Frontal pole cortex: Encoding ends at the end of the endbrain. Trends in Cognitive Sciences, 15, 169176.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van Holst, R. J. , van den Brink, W. , Veltman, D. J. , & Goudriaan, A. E. (2010). Why gamblers fail to win: A review of cognitive and neuroimaging findings in pathological gambling. Neuroscience Biobehavioral Review, 34, 87107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verbruggen, F. , Adams, R. , & Chambers, C. D. (2012). Proactive motor control reduces monetary risk taking in gambling. Psychological Science, 23, 805815.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vohs, K. D. , Baumeister, R. F. , Schmeichel, B. J. , Twenge, J. M. , Nelson, N. M. , & Tice, D. M. (2008). Making choices impairs subsequent self-control: A limited-resource account of decision making, self-regulation, and active initiative. Journal of Personality and Social Psychology, 94, 883898.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wertenbroch, K. (1998). Consumption self-control by rationing purchase quantities of virtue and vice. Marketing Science, 17, 317337.

  • Xue, G. , Lu, Z. , Levin, I. P. , & Bechara, A. (2010). The impact of prior risk experiences on subsequent risky decision-making: The role of the insula. NeuroImage, 50, 709716.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, G. , Lu, Z. , Levin, I. P. , & Bechara, A. (2011). An fMRI study of risk-taking following wins and losses: Implications for the gambler’s fallacy. Human Brain Mapping, 32, 271281.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ariely, D. , & Wertenbroch, K. (2002). Procrastination, deadlines, and performance: Self-control by precommitment. Psychological Science, 13, 219224.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashraf, N. , Karlan, D. , & Yin, W. (2006). Tying Odysseus to the mast: Evidence from a commitment savings product in the Philippines. The Quarterly Journal of Economics, 121, 635672.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boyer, T. W. (2006). The development of risk-taking: A multiperspective review. Developmental Review, 26, 291345.

  • Burgess, P. W. , Dumontheil, I. , & Gilbert, S. J. (2007). The gateway hypothesis of rostral prefrontal cortex (area 10) function. Trends in Cognitive Sciences, 11, 290298.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carver, C. S. , & White, T. L. (1994). Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS scales. Journal of Personality Social Psychology, 67, 319333.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohen, J. R. , & Lieberman, M. D. (2010). The common neural basis of exerting self-control in multiple domains. In K. Ochsner, & Y. Trope (Eds.), From society to brain: The new sciences of self-control (pp. 141160). New York, NY: Oxford University Press.

    • Search Google Scholar
    • Export Citation
  • Crockett, M. J. , Braams, B. R. , Clark, L. , Tobler, P. N. , Robbins, T. W. , & Kalenscher, T. (2013). Restricting temptations: Neural mechanisms of precommitment. Neuron, 79, 391401.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dill, B. , & Holton, R. (2014). The addict in us all. Frontiers in Psychiatry, 5, 139.

  • Franken, I. H. A. , & Muris, P. (2005). Individual differences in decision-making. Personality and Individual Differences, 39, 991998.

  • Fujita, K. (2011). On conceptualizing self-control as more than the effortful inhibition of impulses. Personality and Social Psychology Review, 15, 352366.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giné, X. , Karlan, D. , & Zinman, J. (2010). Put your money where your butt is: A commitment contract for smoking cessation. American Economic Journal: Applied Economics, 2, 213235.

    • Search Google Scholar
    • Export Citation
  • Goudriaan, A. E. , Oosterlaan, J. , de Beurs, E. , & van den Brink, W. (2005). Decision making in pathological gambling: A comparison between pathological gamblers, alcohol dependents, persons with Tourette syndrome, and normal controls. Cognitive Brain Research, 23, 137151.

    • 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, 231239.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Helfinstein, S. M. , Schonberg, T. , Congton, E. , Karlsgodt, K. H. , Mumford, J. A. , Sabb, F. W. , Cannon, T. D. , London, E. D. , Bilder, R. M. , & Poldrack, R. A. (2014). Predicting risky choices from brain activity patterns. Proceedings of the National Academy of Sciences, 111, 24702475.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalenscher, T. , & Pennartz, C. M. (2008). Is a bird in the hand worth two in the future? The neuroeconomics of intertemporal decision-making. Progress in Neurobiology, 84, 284315.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khazaal, Y. , Chatton, A. , Bouvard, A. , Khiari, H. , Achab, S. , & Zullino, D. (2013). Internet poker websites and pathological gambling prevention policy. Journal of Gambling Studies, 29, 5159.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, D. Y. , & Lee, J. H. (2011). Effects of the BAS and BIS on decision-making in a gambling task. Personality and Individual Differences, 50, 11311135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knoch, D. , Gianotti, L. R. R. , Pascual-Leone, A. , Treyer, V. , Regard, M. , Hohmann, M. , & Brugger, P. (2006). Disruption of right prefrontal cortex by low-frequency repetitive transcranial magnetic stimulation induces risk-taking behavior. The Journal of Neuroscience, 26, 64696472.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurth-Nelson, Z. , & Redish, A. D. (2010). A reinforcement learning model of precommitment in decision making. Frontiers in Behavioral Neuroscience, 4, 184.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurth-Nelson, Z. , & Redish, A. D. (2012). Don’T let me do that!– models of precommitment. Frontiers in Neuroscience, 6, 138.

  • Ladouceur, R. , Blaszczynski, A. , & Lalande, D. R. (2012). Pre-commitment in gambling: A review of the empirical evidence. International Gambling Studies, 12, 116.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laplace, P. (1820). Philosophical essays on probabilities (F. W. Truscott, & F. L. Emory, Trans.). New York: Dover.

  • Lesieur, H. R. , & Blume, S. B. (1987). The South Oaks Gambling Screen (SOGS): A new instrument for the identification of pathological gamblers. American Journal of Psychiatry, 144, 11841188.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newman, J. P. , Patterson, C. M. , & Kosson, D. S. (1987). Response perseveration in psychopaths. Journal of Abnormal Psychology, 96, 145148.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nower, L. , & Blaszczynski, A. (2010). Gambling motivations, money-limiting strategies, and precommitment preferences of problem versus non-problem gamblers. Journal of Gambling Studies, 26, 361372.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petry, N. M. , & Weinstock, J. (2007). Internet gambling is common in college students and associated with poor mental health. The American Journal on Addictions, 16, 325330.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, J. , Mochon, D. , Wyper, L. , Maroba, J. , Patel, D. , & Ariely, D. (2014). Healthier by precommitment. Psychological Science, 25, 538546.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevens, T. , Brevers, D. , Chambers, C. D. , Lavric, A. , McLaren, I. P. L. , Mertens, M. , Noël, X. , & Verbruggen, F. (2015). How does response inhibition influence decision-making when gambling? Journal of Experimental Psychology: Applied, 21, 1536. doi: 10.1037/xap0000039

    • Search Google Scholar
    • Export Citation
  • Studer, B. , & Clark, L. (2011). Place your bets: Psychophysiological correlates of decision-making under risk. Cognitive, Affective, and Behavioral Neuroscience, 11, 144158.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Suhr, J. A. , & Tsanadis, J. (2007). Affect and personality correlates of the Iowa Gambling Task. Personality and Individual Differences, 43, 2736.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, S. , & Corr, P. J. (2013). A feedback-response pause normalises response perseveration deficits in pathological gamblers. International Journal of Mental Health and Addiction, 11, 601610.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, S. , & Corr, P. J. (2014). Pause for thought: Response perseveration and personality in gambling. Journal of Gambling Studies, 30, 889900. doi: 10.1007/s10899-013-9395-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsujimoto, S. , Genovesio, A. , & Wise, S. P. (2011). Frontal pole cortex: Encoding ends at the end of the endbrain. Trends in Cognitive Sciences, 15, 169176.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van Holst, R. J. , van den Brink, W. , Veltman, D. J. , & Goudriaan, A. E. (2010). Why gamblers fail to win: A review of cognitive and neuroimaging findings in pathological gambling. Neuroscience Biobehavioral Review, 34, 87107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verbruggen, F. , Adams, R. , & Chambers, C. D. (2012). Proactive motor control reduces monetary risk taking in gambling. Psychological Science, 23, 805815.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vohs, K. D. , Baumeister, R. F. , Schmeichel, B. J. , Twenge, J. M. , Nelson, N. M. , & Tice, D. M. (2008). Making choices impairs subsequent self-control: A limited-resource account of decision making, self-regulation, and active initiative. Journal of Personality and Social Psychology, 94, 883898.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wertenbroch, K. (1998). Consumption self-control by rationing purchase quantities of virtue and vice. Marketing Science, 17, 317337.

  • Xue, G. , Lu, Z. , Levin, I. P. , & Bechara, A. (2010). The impact of prior risk experiences on subsequent risky decision-making: The role of the insula. NeuroImage, 50, 709716.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, G. , Lu, Z. , Levin, I. P. , & Bechara, A. (2011). An fMRI study of risk-taking following wins and losses: Implications for the gambler’s fallacy. Human Brain Mapping, 32, 271281.

    • Crossref
    • Search Google Scholar
    • Export Citation
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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

2022  
Web of Science  
Total Cites
WoS
5713
Journal Impact Factor 7.8
Rank by Impact Factor

Psychiatry (SCIE) 18/155
Psychiatry (SSCI) 13/144

Impact Factor
without
Journal Self Cites
7.2
5 Year
Impact Factor
8.9
Journal Citation Indicator 1.42
Rank by Journal Citation Indicator

Psychiatry 35/264

Scimago  
Scimago
H-index
69
Scimago
Journal Rank
1.918
Scimago Quartile Score Clinical Psychology Q1
Medicine (miscellaneous) Q1
Psychiatry and Mental Health Q1
Scopus  
Scopus
Cite Score
11.1
Scopus
Cite Score Rank
Clinical Psychology 10/292 (96th PCTL)
Psychiatry and Mental Health 30/531 (94th PCTL)
Medicine (miscellaneous) 25/309 (92th PCTL)
Scopus
SNIP
1.966

 

 
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 990 EUR/article for articles submitted after 30 April 2023 (850 EUR for articles submitted prior to this date)
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts 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

  • Stephanie ANTONS (Universitat Duisburg-Essen, Germany)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Ruth J. van HOLST (Amsterdam UMC, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Gyöngyi KÖKÖNYEI (ELTE Eötvös Loránd University, Hungary)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • 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)

 

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