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. 2025 Jun 12;41(4):1725–1742. doi: 10.1007/s10899-025-10397-9

Effects of Audiovisual Cues on Game Immersion during Simulated Slot Machine Gambling

Fiza Arshad 1,2,✉, Mario A Ferrari 2, W Spencer Murch 3, Mariya V Cherkasova 4, Eve H Limbrick-Oldfield 2, Catharine A Winstanley 1, Luke Clark 1,2
PMCID: PMC12657550  PMID: 40504292

Abstract

Modern slot machines can create immersive experiences for gamblers. Design features, including audiovisual cues, may influence these experiences, potentially interacting with personal risk factors for disordered gambling. According to the incentive salience hypothesis of addiction, reward-paired audiovisual cues strongly motivate behavior. This study manipulated audiovisual cues during simulated slot machine gambling to test the effects of varying intensities on self-reported immersion. Undergraduate students (n = 156) played a realistic slot machine simulation within an authentic cabinet. They experienced three intensities of audiovisual cues: Minus, Intermediate, and Plus. Participants completed self-report questionnaires, including a retrospective game immersion questionnaire. The pre-registered hypothesis was partially supported: the Intermediate cue condition was associated with greater game immersion than the Minus condition (p < 0.05). Exploratory models revealed higher total scores on the depression, anxiety, and stress scale predicted greater immersion in the Intermediate condition (p < 0.001). A Cue x Gender interaction was driven by greater immersion in the Intermediate cue condition among women, but not men (p < 0.05). Problem gambling severity predicted greater immersion across all models (p < 0.001). Audiovisual cues influenced immersion in slot machine gambling, supporting regulatory attention to audiovisual features as an engineered product aspect. Contrary to predictions, immersion was highest at the intermediate not maximal level of stimulation. Gender and affective symptoms also impacted immersion, indicating personal risk factors in susceptibility.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10899-025-10397-9.

Keywords: Slot machine, Gambling, Immersion, Psychological flow, Audiovisual cues

Introduction

Slot machines are a type of gambling product grouped under Electronic Gaming Machines (EGMs), and are recognized as among the highest risk forms of gambling (Binde et al., 2017; Breen & Zuckerman, 1999; Browne et al., 2023; Dowling et al., 2005; Griffiths, 1993). Slot machine games are widely accessible online, as a gambling modality that increased in popularity during the COVID-19 pandemic due to casino closures and social distancing (Xuereb et al., 2021). Globally, 2.43% of adults report moderate- or high- risk of gambling problems, while 1.29% satisfy criteria for clinically significant gambling disorder (Gabellini et al., 2023). People with gambling problems are more likely to experience a lower quality of life, require emergency room treatment, undergo psychiatric hospitalization, report sleep, mood, and stress-related comorbidities, and be diagnosed with cardiovascular or liver disease (Black et al., 2013; Hodgins et al., 2005; Morasco et al., 2006). Among people seeking help for gambling problems, slot machines are often the most preferred and problematic form, and they are further associated with poorer treatment outcomes (Griffiths et al., 1999; Kennedy et al., 2019; Kessler et al., 2008; Ronzitti et al., 2018).

At a psychological level, slot machines are sophisticated products characterized by several distinct design features, termed ‘structural characteristics’ (Griffiths, 1993; Meyer et al., 2011). These design features, which include audiovisual feedback, high event frequency, and the opportunity to increase one’s bet and bet on multiple pay lines, may elevate risk of developing gambling problems, through manipulating reinforcement learning and attentional biases (Yücel et al., 2018). They also interact with risk factors within the individual, such as life stress and early adversity (Roberts et al., 2017; Russell et al., 2022). These interactions between ‘the person and the product’ are proposed to underlie the escalation from recreational to disordered gambling (see also Korn & Shaffer, 1999; Murch & Clark, 2016).

Audiovisual stimulation in modern slot machines is particularly intense following winning outcomes. According to the incentive salience hypothesis of addiction, reward-paired audiovisual cues can induce ‘wanting’ through associative learning and sensitization of underlying dopaminergic reward circuitry (Robinson & Berridge, 1993, 2001). Given the plethora of sensory cues in modern slot machines, researchers have begun to test whether this framework contributes to problematic engagement with gambling (Barrus et al., 2016; Hellberg et al., 2019). In support of this hypothesis, contextual, environmental cues like fast-tempo music increase betting speed (Dixon et al., 2007), especially under conditions of ambient red light (Spenwyn et al., 2010). Pairing winning outcomes with casino-inspired audiovisual cues also increased risky choices in a laboratory-based gambling task (Cherkasova et al., 2018, 2024). Both anticipatory and win-concurrent audiovisual cues increased attractiveness of slot machine choices in a computerized task, and these cues also improved memory recall of winning outcomes (Spetch et al., 2020).

The present study tested whether audiovisual cues can potentiate immersion, defined as a subjective state of consciousness in which an intense focus on the task at hand (e.g. gambling) causes a shift in attention, a dissociation from self-awareness and one’s surroundings, and distorted time perception (Arshad & Clark, 2022; Agrawal & Bech, 2023). This subjective state is related to psychological flow and clinical dissociation (Michailidis et al., 2018; Schluter & Hodgins, 2019). Popularly described in qualitative research by Schüll (2012) as ‘the machine zone’, immersion is a common experiential state during slot machine gambling (Murch et al., 2017; Murch & Clark, 2019; Murch et al., 2020), and has been reliably associated with problematic gambling (Rogier et al., 2021; Schluter & Hodgins, 2019). Moreover, there is some evidence linking immersion to specific design features of gambling. Experienced gamblers reported higher levels of immersion in slot machine sessions that were set to ‘multi-line’ play and a larger bet size, compared to sessions on a single-line setting and a smaller bet size (Dixon et al., 2014b; Murch & Clark, 2019).

The present study investigates the role of audiovisual cues in immersion in the context of modern slot machine gambling. Participants played a highly realistic simulation of a modern multi-line slot machine game (see Ferrari et al., 2022), in which the intensity of the device’s audiovisual features was manipulated across three groups. While most previous work has examined the effect of contextual, environmental cues (e.g. background noise), this study specifically examines win-concurrent sensory feedback (Dixon et al., 2007, 2014b; Spenwyn et al., 2010). Our primary, pre-registered, hypothesis was that immersion would increase with the intensity of in-game audiovisual cues (Arshad et al., 2019). Prior studies on gender effects in gambling have established a robust link with problem gambling risk, and rodent studies further suggest that biological sex is a source of individual differences relevant to the neurobiological mechanisms driving cue-induced risky choices (Allami et al., 2021; Chernoff et al., 2021; Hynes et al., 2020; Orsini et al., 2022). Depression and ADHD symptoms also correlate with immersion (Dixon et al., 2014b, 2018, 2019a, 2019b; Murch & Clark, 2019). Therefore, exploratory models tested the effects of general psychological distress (depression, anxiety, and stress), ADHD symptoms, and gender on reported immersion.

Methods

Participants

Undergraduate students (n = 156) were invited to participate in an in-person test session in exchange for course credit. To be eligible, participants were required to have normal or corrected vision. After consent was obtained, the participants were administered the Problem Gambling Severity Index (PGSI). Any participant who scored in the high-risk range (PGSI > 8) was excluded from the study on ethical grounds, and provided with local resources for gambling support as part of a debrief. One further participant was excluded for participating twice. The final sample size for analysis was 155.

The study was approved by the University of British Columbia’s Behavioural Research Ethics Board. Sample size was determined through an a priori power analysis in R using the pwr.f2.test function from the pwr package, with α set to 0.01 and power (1 − β) = 0.80. Based on a model including 9 predictors, the analysis indicated a required sample size of 154 participants with 144 degrees of freedom for the residuals. To allow for equal distribution across the three cue conditions, the target sample size was increased to 156 (i.e., 52 participants per condition). The initial set of regressors were: Cue Condition, Sequence, Gender, PGSI score, Depression, Anxiety, and Stress scores (DASS), ASRS Part A, ASRS Part B, Positive Affect, and Negative Affect scores. During data analysis, the two ASRS subscales were combined into a single total ASRS score.

Behavioural Testing

Participants played a realistic multi-line slot machine simulation, called “Cleo’s Gold,” which was modified from Ferrari et al. (2022) to incorporate three levels of audiovisual intensity (see Fig. 1). The task was programmed using Custom Slots Framework 1.4 with Unity 2017.3.1 (Lafrontier, 2018; Unity Technologies, 2017), and run on a Windows PC that was fitted inside a commercial slot machine cabinet. The authentic cabinet was provided to the Centre for Gambling Research by the British Columbia Lottery Corporation (Kamloops, Canada). The cabinet comprised two vertically-aligned monitors, a button panel, and a (non-functional) bank note acceptor.

Fig. 1.

Fig. 1

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Screenshot of Cleo’s Gold during the Plus (left), Intermediate (middle), and the Minus (right) cue conditions

Prior to starting a session, participants were given the following instructions: “You were given $40 worth of credits to use in today’s study. This has already been loaded into the machine. As it says on the consent form, any winnings you have above the $40 you started with at the end of the session will be paid to you, up to a maximum of 20 dollars. Let me know if you have any questions before the next step”. Each participant played “Cleo’s Gold” seated on an authentic casino stool positioned 50 cm from the screen. The participants wore a pair of non-invasive mobile eye-tracking glasses during the slot machine task, and the room was dimly lit for both eye-tracking and casino authenticity. Eye-tracking data will be reported separately.

The simulation used an ancient Egyptian visual theme, reflected in the icons (see Fig. 1). Participants began the task with 4,000 credits, and their betting strategy was constrained to the minimum bet amount of 1 credit per line across all 40 available lines (termed the ‘mini-max’ setting), thus betting 40 credits per spin. This bet configuration elicits a consistent reinforcement rate characteristic of ‘multi-line’ play, and experienced slot machine gamblers typically use this setting (Livingstone & Woolley, 2007; Templeton et al., 2015). The three audiovisual conditions were Minus, Intermediate, and Plus, broadly representative of modern slot machines. Both the Intermediate and Plus conditions included some form of animated and enhanced feedback, whereas the Minus condition did not (see Fig. 1; Table 1). As well as the audiovisual condition, participants were further counterbalanced to one of three outcome sequences. These sequences were randomly generated, but each produced an overall net loss (representative of ‘house edge’), of a similar magnitude for each sequence, to mitigate the possibility that any effects might be attributable to a particular pattern of trials (see Ferrari et al., 2022).

Table 1.

Participant characteristics across the three audiovisual cue.conditions: Plus, Minus, and Intermediate

Minus (N = 53) Intermediate
(N = 50)		 Plus
(N = 52)
Gender
Women 35 (66.04%) 35 (70.00%) 36 (69.23%)
Men 18 (33.96%) 15 (30.00%) 16 (30.77%)
Chi-Squared (df, p) 0.072 (2, 0.96)
PGSI
Mean (SD) 1.19 ± 0.87 1.34 ± 0.83 1.18 ± 0.70
Median (Q1, Q3) 1.00 (0.57, 1.86) 1.14 (0.61, 1.86) 1.00 (0.57, 1.71)
Kruskal–Wallis χ2 (df, p) 3.18 (2, 0.20)
DASS Total
Mean (SD) 29.06 ± 18.53 28.40 ± 19.54 23.27 ± 17.13
Median (Q1, Q3) 26.00 (12.00, 42.00) 24.00 (14.00, 36.00) 18.00 (9.50, 32.50)
Kruskal–Wallis χ2 (df, p) 3.580 (2, 0.17)
ASRS Total
Mean (SD) 6.21 ± 3.64 5.72 ± 3.30 5.83 ± 2.87
Median (Q1, Q3) 6.00 (3.00, 9.00) 5.00 (3.00, 8.00) 6.00 (4.00, 8.00)
Kruskal–Wallis χ2 (df, p) 0.226 (2, 0.89)
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DASS Depression, Anxiety, and Stress Scale, ASRS ADHD self-reporting scale; PGSI Problem Gambling Severity Index

Following the slot machine session, the participants completed the 7-item Game Immersion Scale (Murch et al., 2017; Murch & Clark, 2019), which combined items from the Dissociation Questionnaire (Jacobs, 1988) and the Game Experience Questionnaire (IJsselsteijn et al., 2013). This scale included items such as “I felt completely absorbed” and “I felt like I took a different identity”. We report average scores for this measure. The Cronbach’s alpha for this questionnaire was 0.85, showing good internal consistency. Participants also completed the 10-item Positive and Negative Affect Scale (PANAS; Crawford & Henry, 2004; Watson et al., 1988), to rate their affective state following the slot machine session (i.e., state-dependant). Participants then completed the 21-item Depression, Anxiety, and Stress Scale (DASS-21) and 18-item ADHD self-report scale (ASRS) (Adler et al., 2006; Henry & Crawford, 2005; Kessler et al., 2005; Lovibond & Lovibond, 1995) as more stable, ‘trait’ measures. Total scores for each of these scales in the analysis were used.

Analyses

The immersion score was used as the dependent variable, based on Murch et al. (2020). Shapiro–Wilk’s tests were conducted to check normality for the immersion score, the PANAS subscale, the DASS-21 and ASRS. As the immersion scores were non-normal and positively skewed, we opted for a gamma log-logistic regression model, as recommended by Hashimoto et al. (2017). To ensure the gamma distribution was a suitable fit, we conducted a density plot analysis, which confirmed that the gamma distribution accurately modeled the immersion scores (see Fig. S1 in Supplementary Information). The gamma distribution is characterized by two parameters: the shape parameter (α) and the rate parameter (β), which is the inverse of the scale parameter. In our analysis, the estimated shape parameter (α) was 7.814, indicating a relatively low level of skewness, while the rate parameter (β) was 3.496, indicating the distribution's spread. These values suggest that the gamma model is a robust choice for capturing the distribution of our immersion scores. Since the gamma-log regression cannot incorporate values around 0, the average immersion scores were shifted by + 1, and the odds ratio calculations accounted for this change. The figures are graphed using the non-shifted values.

All the categorical predictors were transformed into dummy variables. The Intermediate condition served as the reference level for the cue condition, sequence 1 for sequence, and female for gender. Statistical analyses, diagnostics, and gamma distribution fitting were conducted using the broom (Robinson et al., 2023), boot (Canty & Ripley, 2023), car (Fox et al., 2023), DHARMa (Hartig, 2023), ez (Lawrence, 2023), fitdistrplus (Delignette-Muller & Dutang, 2023), MASS (Venables & Ripley, 2023), pscl (Jackman, 2023), and psych (Revelle, 2023) packages in R. Tables and graphs were created using corrplot (Wei & Simko, 2023) and ggplot2 (Wickham et al., 2023). All analyses were performed using R, version 4.2.2 (R Core Team, 2022). Data and analysis scripts can be shared on request.

The project hypotheses and analysis plan were pre-registered on the Open Science Framework (https://osf.io/gf53w). In this study, we tested Hypothesis 1 concerning the effects of audiovisual cue conditions on immersion. Hypothesis 3 and 4, which are related to eye-tracking data, will be published elsewhere. Pre-registered model 1 regressed the average immersion score on Cue condition and Sequence. Exploratory models regressed this outcome variable on Gender (exploratory model 1), DASS (exploratory model 2), and ASRS (exploratory model 3). Equations 1 – 4 display the general linear models (glm function) run in R.

Pre-registered:Immersion∼Cue_Condition+PGSI+Sequence,family=Gamma(link=′′log′′) 1
Exploratory1:Immersion∼Cue_Condition*Gender+PGSI+Sequence,family=Gamma(link=′′log′′) 2
Exploratory2:Immersion∼Cue_Condition*DASS+PGSI+Sequence,family=Gamma(link=′′log′′) 3
Exploratory3:Immersion∼Cue_Condition*ASRS+PGSI+Sequence,family=Gamma(link=′′log′′) 4

Results

Participant Characteristics

Table 1 shows the participant characteristics for each variable across the three audiovisual cue conditions (Minus, Intermediate, and Plus). The PGSI scores across the three cue conditions were not significantly different (χ2(2) = 3.18, p = 0.20). Although there were more women than men in the overall sample, a chi-squared test of the gender ratio across the three conditions was not significant (χ2(2) = 0.072, p = 0.96). Kruskal–Wallis tests comparing DASS (χ2(2) = 3.58, p = 0.17), ASRS scores (χ2(2) = 3.58, p = 0.42), Negative Affect (χ2(2) = 0.122, p = 0.941) and Positive Affect scores (χ2(2) = 1.09, p = 0.581) across the three cue conditions were non-significant.

Pre-registered Model

The pre-registered model (Eq. 1), testing the influence of audiovisual condition on self-reported immersion, was significant (χ2(5) = 2.69, Log Likelihood = −167, McFadden's R2 = 0.063, p = 2.84 × 10–4) and explained significant variance in immersion scores (Residual Deviance [df = 149] = 17.57). Compared to the Intermediate condition, the Minus condition had a significant effect (β =  − 0.132, z =  − 1.98, 95% CI = [−0.233, 0.002], p = 0.048). Participants in the Minus condition reported 12.3% lower immersion scores than those in the Intermediate condition (see Fig. 2 and Table 2). PGSI also significantly predicted immersion (β = 0.093, z = 4.37, 95% CI = [0.052, 0.147], p = 1.24 × 10−5). Each unit increase in PGSI was associated with a 9.8% greater odds of reporting immersion (see Table 2 and Fig. 3). The Plus Cue condition, and Sequence did not significantly influence immersion scores (p > 0.05).

Fig. 2.

Fig. 2

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Average immersion scores in Minus, Intermediate, and Plus cue conditions. Error bars represent standard error of the mean. *p < 0.05

Table 2.

Pre-registered and exploratory gamma-log-logistic regression models. The outcome variable is the average immersion score. Minus cue condition, female, and Sequence 1 were used as the reference levels in the regressions

Model Predictors Model Statistics β-estimate z-value p-value Relative Change^ 95% CI
Pre-registered Plus Cue Condition χ2(5) = −2.69, Log Likelihood = −167, McFadden’s R2 = 0.063, p = 2.84 × 10–4*** −0.0861 −1.302 0.193 −0.082 [−0.196, 0.047]
Minus Cue Condition −0.132 −1.98 0.048* −0.123 [−0.233, 0.002]
PGSI 0.093 4.37 1.24 × 10^−5*** 0.098 [0.052, 0.147]
Sequence2 0.064 0.97 0.334 0.066 [−0.066, 0.216]
Sequence3 −0.0307 −0.47 0.638 −0.03 [−0.148, 0.104]
Exploratory Model 1: Gender Plus Cue Condition χ2(5) = 3.38, Log Likelihood = −164, McFadden's R2 = 0.081, p = 1.98 × 10–4*** −0.194 −2.48 0.013* −0.176 [−0.296, −0.037]
Minus Cue Condition −0.166 −2.11 0.035* −0.153 [−0.276, −0.008]
Men −0.122 −1.19 0.232 −0.115 [−0.276, 0.089]
PGSI 0.0902 4.15 3.31 × 10^−05 *** 0.094 [0.048, 0.144]
Sequence2 0.0432 0.658 0.51 0.0442 [−0.0846, 0.191]
Sequence3 −0.0211 −0.327 0.743 −0.0208 [−0.140, 0.114]
Plus Cue x Men 0.344 2.41 0.016* 0.411 [0.059, 0.880]
Minus Cue Men 0.126 0.896 0.37 0.135 [−0.146, 0.506]
Exploratory Model 2: DASS Plus Cue Condition χ2(5) = 4.76; Log Likelihood = −157; McFadden’s R2 = 0.118; p = 2.24 × 10–7*** 0.104 0.959 0.338 0.109 [−0.110, 0.384]
Minus Cue Condition 0.0546 0.481 0.63 0.056 [−0.160, 0.330]
DASS 0.00923 4.01 6.16 × 10^−05 *** 0.009 [0.005, 0.014]
PGSI 0.0784 3.71 2.09 × 10^−4 *** 0.082 [0.036, 0.130]
Sequence2 0.089 1.43 0.152 0.0942 [−0.0350, 0.241]
Sequence3 −0.00853 −0.138 0.89 −0.00849 [−0.124, 0.122]
Plus Cue x DASS −0.00537 −1.56 0.12 −0.00535 [−0.0124, 0.00177]
Minus Cue x DASS −0.0058 −1.72 0.085 −0.00578 [−0.0126, 0.00105]
Exploratory Model 3: ASRS Plus Cue Condition χ2(5) = 3.37, Log Likelihood = −164, McFadden’s R2 = 0.081, p = 2.07 × 10–4*** −0.0625 −0.448 0.654 −0.0606 [−0.289, 0.244]
Minus Cue Condition −0.0982 −0.75 0.453 −0.0935 [−0.303, 0.178]
ASRS 0.0243 1.7 0.09 0.0245 [−0.00396, 0.0541]
PGSI 0.0807 3.7 2.16 × 10^−4*** 0.0841 [0.0376, 0.134]
Sequence2 0.0755 1.14 0.255 0.0784 [−0.0562, 0.232]
Sequence3 −0.0179 −0.279 0.78 −0.0177 [−0.136, 0.117]
Plus Cue * ASRS −0.00389 −0.182 0.856 −0.00388 [−0.0458, 0.0397]
Minus Cue * ASRS −0.00597 −0.31 0.757 −0.00595 [−0.0437, 0.0334]
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Odds ratios and 95% confidence intervals (CIs) are reported for significant main effects. ^Relative Change and Odd Ratios are adjusted and back-transformed by subtracting 1. 95% CIs are reported as [lower bound, upper bound]

DASS Depression, Anxiety, and Stress Scale, ASRS ADHD self-reporting scale, PGSI Problem Gambling Severity Index

*p < 0.05, **p < 0.01, ***p < 0.001

Fig. 3.

Fig. 3

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Average immersion scores in Women (left) and Men (right) across the three audiovisual cue conditions: Minus, Intermediate, and Plus. Women in the Intermediate condition report significantly greater immersion when compared to the Minus and Plus conditions. Error bars represent standard error of the mean

Exploratory Models

The three exploratory models examined the impact of gender, DASS and ASRS scores. PGSI and Sequence were also entered in these models. PGSI was a significant predictor in all models.

Exploratory model 1 was statistically significant (χ2(5) = 3.38, Log Likelihood = −164, McFadden's R2 = 0.081, p = 1.98 × 10–4) and explained a significant portion of the variance in immersion scores (Residual Deviance [df = 146] = 16.88). Compared to the Intermediate cue, the Minus (β = −0.166, z = −2.11, 95% CI = [−0.276, −0.008], p = 0.035) and Plus (β = −0.194, z = −2.48, 95% CI = [0.296, 0.037], p = 0.013) conditions were significant predictors (Table 2). Participants in the Plus condition reported 17.6% lower immersion scores than those in the Intermediate condition. A significant Gender x Cue interaction was observed between men and the Plus cue condition (β = 0.344, z = 2.41, 95% CI = [0.059, 0.880], p = 0.016, OR = 1.41).

To further examine this interaction, separate models were conducted in the men and women. Among participants who were women, both the Minus and Plus conditions exhibited significantly lower immersion scores than the Intermediate cue condition, with reductions of 15.6% and 17.4%, respectively, based on the odds ratios. However, no significant differences were observed between the cue conditions in men (p > 0.05).

Exploratory Model 2 was a significant model (χ2(5) = 4 0.76, Log Likelihood = −157, McFadden's R2 = 0.118, p = 2.24 × 10 −7) and explained residual deviance in immersion (Residual Deviance [df = 146] = 15.51). The DASS score was a significant predictor of immersion (β = 0.009, z = 4.01, 95% CI = [0.005, 0.014], p = 6.16 × 10−5), in the Intermediate reference category. For every unit increase in the DASS score, the relative chances of reporting higher immersion increased (OR = 0.9%, see Fig. 4). Other than the PGSI predictor, no further predictors in the model were statistically significant (see Table 2).

Fig. 4.

Fig. 4

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Scatterplots showing the relationship of the average immersion score with the total DASS score (left), the total ASRS score (middle) and the total PGSI score (right) in the entire sample (right). DASS = Depression, Anxiety, and Stress Scale; ASRS = ADHD self-reporting scale; PGSI = Problem Gambling Severity Index

Although Exploratory Model 3 was statistically significant (χ2(5) = 3.37, Log Likelihood = −164, McFadden’s R2 = 0.081, p = 2.07 × 10–4) and explained residual deviance in immersion (Residual Deviance [df = 146] = 16.89), the ASRS score and its interaction with cue condition did not predict immersion (p > 0.05, Table 2). PGSI and ASRS were moderately correlated, despite a lack of any relationship between immersion and ASRS (see Figs. 4 and 5).

Fig. 5.

Fig. 5

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Heatmap correlations of the correlations between gender, immersion, PGSI, DASS, and ASRS score, in the overall sample. Spearman rank’s coefficients are displayed. DASS = Depression, Anxiety, and Stress Scale; ASRS = ADHD self-reporting scale; PGSI = Problem. Gambling Severity Index

Discussion

This study aimed to investigate whether audiovisual cues enhance immersion in slot machine gambling. A controlled experimental design with a realistic simulation of a contemporary multi-line game was employed. The simulation enabled us to manipulate the intensity of audiovisual cues during gameplay, over three levels. Our findings partly support our pre-registered hypothesis, as participants in the Intermediate condition reported greater immersion than those in the Minus condition. A significant interaction was further identified between gender and cue condition: women in the Intermediate condition reported higher immersion than both the Minus and Plus conditions, while there were no pairwise differences in immersion among men. Additional analyses revealed a correlation between general psychological distress, as assessed by the DASS, and higher immersion in the Intermediate cue condition, and greater immersion was consistently associated with higher PGSI scores.

We predicted a progressive increase in immersion across the three levels of audiovisual cues (Minus, Intermediate, and Plus conditions). Our findings reveal higher immersion at the Intermediate intensity, but no discernible effect of the maximal (Plus) condition relative to the Intermediate benchmark. Both the Intermediate and Plus cue conditions reflect the contemporary design of commercial slot machines commonly found on casino floors. However, as our participants executed the task individually, in a laboratory environment, this could have restricted the immersion experience relative to real-world gambling, where for example background music and sensory cues from other gamblers may moderate the experience (Rockloff et al. 2011). Alternatively, it is possible that there is a"sweet spot"of audiovisual cue stimulation, as suggested by Graydon et al. (2021). For a sample of student participants with low levels of gambling involvement, the intensity of stimulation in the Plus condition may have been disconcerting or overwhelming, to an extent that prevented participants from becoming immersed in the game. This finding may relate to classic concepts of “optimal arousal” (e.g., Berlyne 1969), and individual disparities in preferred stimulation levels based on personality (Geen, 1984). In line with this idea and prior work (Murch et & Clark, 2019; Murch et al., 2020), a unit increase in the PGSI score was associated with 8–9% greater odds of reporting immersion across all models.

In Model 1, gender displayed a significant interaction with the Plus cue condition. In decomposing this effect, immersion scores among women were higher in the Intermediate condition compared to both the Plus (15.6% lower, based on odds ratios) and Minus (17.4% lower) conditions (see Fig. 3), while among men, there were no pairwise differences between conditions. We note that the lower sample representation of men in our study (less than 30%) likely reduced our power to detect differences in the men. Nevertheless, this gender difference may suggest varying levels of preferred stimulation during gambling by gender, and/or gender-based differences in cognitive tendencies towards immersion. The prevalence of problem gambling, motivations for gambling, and other psychosocial risk factors for gambling problems vary by gender (Afifi et al., 2010; Ledgerwood & Petry, 2006; Wong et al., 2013). Women often gamble to escape, while men typically gamble for sensation seeking and to alleviate boredom (Ledgerwood & Petry, 2006). Previous research on gender differences in performance and flow found that higher flow was related to better performance in males, but not females, during combat identification video game training (Plummer et al., 2017). Similarly, men reported higher flow levels, but not women, during extreme sports (Chang, 2017). We recommend that future research investigate such differences in immersion and audiovisual feedback during gambling using larger sample sizes and a pre-registered design.

In Model 2, higher DASS scores as an index of general psychological distress were associated with greater immersion, in the Intermediate condition as the reference category. This link between negative emotionality and immersion is consistent with previous research (Dixon et al., 2014b, 2018, 2019a, 2019b), and support a negative reinforcement mechanism whereby individuals experiencing depression, anxiety or high stress may be drawn to gambling as a source of escape or relief (e.g. Jacobs, 1986). In the studies conducted by Dixon and colleagues, the depression subscale of DASS was found to be correlated with the gambling expectancy scale (Dixon et al., 2018) and with what the authors describe as “dark flow”- a state of intense absorption in, and focus on, slot machine gambling that is ultimately maladaptive, given the ‘house edge’ of gambling and potential for over-consumption (Dixon et al., 2019a). Indeed, a combination of'dark flow'and depression predicted gambling problems using the PGSI (Dixon et al., 2019b). Unlike in Dixon et al., 2019a, 2019b, the current study did not use a community-based sample of recreational gamblers recruited from a casino. It will be important to verify whether audiovisual cues promote immersion in clinical samples in future studies, given the high comorbidity among depression, anxiety, and problem gambling.

In Model 3, an exploratory analysis was conducted to examine ASRS scores and their interaction with cue conditions as predictors of immersion. No significant effect of ASRS on immersion was observed, although in the full sample, bivariate tests showed a moderate correlation between ASRS and immersion. Among a community sample of gamblers, those with higher ASRS scores reported higher immersion when playing an authentic slot machine game in the laboratory (Murch & Clark, 2019), which featured comparable audiovisual cues to the Plus condition in the current experiment.

Game immersion was measured following the game, using a subjective scale. While the scale itself is reliable (Murch et al., 2020), subjective measurement of immersion is limited by its retrospective nature. Eye tracking data were collected in this study, and will be reported separately. Future studies could further enhance immersion measurement by incorporating additional objective methods, such as neuroimaging during gambling tasks. As a further limitation, we note that although our slot machine simulation was highly realistic, it did not include all components of modern games, such as bonus rounds, which are known to shape game preferences and experiences among regular gamblers (Landon et al., 2018). It is also possible that the three audiovisual cue conditions were not subjectively experienced as equidistant from one another in terms of cue complexity or intensity, which could also explain why the Plus condition was not more immersive than the Intermediate condition.

In conclusion, our data partially support the hypothesis that audiovisual cues promote a sense of immersion during slot machine gambling. Our exploratory analyses suggested that problem gambling severity, psychological distress, and ADHD tendencies also shape self-reported immersion using slot machines. Given the widespread use of audiovisual features in commercial gambling products, further description of their impact on cognitive states such as immersion, may help us understand their role in facilitating problematic gambling engagement.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 139 KB) (138.5KB, docx)

Acknowledgements

LC, MF, and SM designed the study. SM set-up the eye-tracker and trained the research assistants. MC advised on the analyses. The following research assistants collected data: Kiyan Abhari, Samara Al-Omaishi, Balreet Dhatt, and Alex Lenardon. SM assisted with data cleaning and pre-processing. FA supervised data collection, pre-processed, and analyzed the data, and wrote the chapter. LC and CW advised FA on data cleaning, data analyses, and edited the chapter. The slot machine cabinet was loaned to the Centre for Gambling Research by the British Columbia Lottery Corporation (Kamloops, Canada).

Funding

This project was supported by the core funding of the Centre for Gambling Research at UBC, a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (NSERC; RGPIN-2023–03528), and an operating grant awarded to CAW and LC from the Canadian Institutes for Health Research (CIHR; PJT-056012). This work took place at a UBC campus situated on the traditional, ancestral, and unceded land of the xʷməθkʷəy̓əm (Musqueam), sə̓lílwətaʔɬSelilwitulh (Tsleil-Waututh) and Sḵwx̱wú7 mesh (Squamish) Peoples. We acknowledge and are grateful for their stewardship of this land for thousands of years.

Declarations

Conflict of interest

LC is the Director of the Centre for Gambling Research at UBC, which is supported by funding from the Province of British Columbia and the British Columbia Lottery Corporation (BCLC), a Canadian Crown Corporation. The Province of BC government and the BCLC had no role in the preparation of this article and impose no constraints on publishing. LC has received remuneration from the International Center for Responsible Gaming (travel and speaker honoraria; reviewing), the Institut fur Glucksspiel und Gesellschaft (Germany; travel and speaker honoraria), GambleAware (UK; academic services), Gambling Research Australia (academic services), Alberta Gambling Research Institute (Canada; academic services), German Foundation for Gambling Research (advisory board; travel and consultancy). He has been remunerated for legal consultancy by the BCLC. He has not received any further direct or indirect payments from the gambling industry or groups substantially funded by gambling. LC receives an honorarium for his role as Co-Editor-in-Chief for International Gambling Studies from Taylor & Francis, and he has received royalties from Cambridge Cognition Ltd. relating to neurocognitive testing. FA has received a commission from Gambling Research Exchange of Ontario (GREO) to write a white paper. CAW has received research funding from the International Centre for Responsible Gaming. WSM previously received training and funding from The Centre for Gambling Research at UBC, a research laboratory jointly supported by the Government of British Columbia and the British Columbia Lottery Corporation (BCLC; a Canadian Crown Corporation). WSM has been awarded a Postdoctoral Fellowship from the Alberta Gambling Research Institute, a joint initiative between Alberta universities and the Government of Alberta. MVC has received research funding from the International Centre for Responsible Gaming and has received a speaker’s honorarium from the Responsible Gaming Association of New Mexico. MAF is an employee of Skillsetter Inc., a software company specializing in counselling and interpersonal skills building; this work began as a paid consulting role. MAF has received doctoral fellowship funding from the Natural Sciences and Engineering Research Council of Canada (NSERC). He has received a speaker honorarium from the British Columbia Lottery Corporation (BCLC). MAF has not received any further direct or indirect payments from the gambling industry or groups substantially funded by gambling.

Footnotes

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