Habit formation is a key process in contemporary models of addictive behaviors but has received limited attention in the context of gambling and problem gambling. Methods for examining habit formation and expression in relation to gambling are also lacking. In this study, 60 participants with no prior slot machine experience attended three sessions spaced 6–8 days apart, during which they played a short 200-spin session on a realistic simulation of a modern multi-line slot machine. Behavioral data were analyzed to characterize habit formation within and between sessions. Fixed-effects regressions, integrating trial- and session-level effects, assessed predictors of gambling speed (spin initiation latencies) and betting rigidity (the likelihood of switching the bet amount), as two putative markers of habit formation. Participants gambled faster and showed less variability in betting strategy as they accumulated experience in the number of trials and sessions gambled. Simultaneously, as the number of sessions gambled Halo69 increased, participants showed a more pronounced tendency to slow their betting after larger wins (i.e. the post-reinforcement pause increased from session 1 to session 3). Our methods provide a basis for future research to examine habits in the context of slot machine gambling.
In the field of gambling studies, the term ‘habit’ is often used colloquially to refer to recurrent or frequent patterns of gambling involvement. Recent studies have begun to characterize habit formation in gambling (Boffo et al., 2018; Dickerson, 1993; Griffiths, 1993; Van Timmeren et al., 2018; Wyckmans et al., 2019). In the Pathways Model of problem gambling (Blaszczynski & Nower, 2002), ‘habituation’ – which could reflect either the development of tolerance to gambling outcomes and/or the formation of habitual behavioral routines – is positioned as a key component between gambling initiation and disordered status. According to the Pathways Model, habitual processes are established gradually as the gambler experiences intermittent operant rewards (i.e. euphoria from winning), with further classical conditioning to gambling cues (e.g. lights and jingles).
In models of problem gambling, habit is presently an underspecified construct, compared to its role in contemporary, neuroscience-oriented models of drug addiction. For example, Everitt and Robbins (2005) hypothesized that habit formation is a key process in the gradual loss of control over drug seeking behaviors, in which initially ‘goal-directed’ drug taking progressively transitions to automatic and stimulus-driven behavior. Used in this way, the term ‘habit’ is operationalized in neurocognitive terms that converge on two defining features (Robbins & Costa, 2017; Wood & Rünger, 2016): 1) a gradual and learned transition to involuntary and cue-driven behavior, and 2) a progressive decoupling of the behavior from the mental representation of its consequences, which allows for persistence of the behavior despite changes in (e.g. removal of) the original reinforcer of the behavior.
As gambling behavior involves both winning and losing outcomes, neurocognitive conceptualizations of habit can be interpreted and applied to the etiology of problem gambling in a number of ways. For example, given that the house edge in gambling produces eventual losses on average, faster or more persistent gambling (e.g. loss chasing) may represent a reduced sensitivity to negative outcomes, and thus habit (Wyckmans et al., 2019). Stated differently, habit formation may be expressed in gambling as a gradual decoupling of betting from the aversiveness of losing. Alternatively, from an appetitive perspective, gambling experience may be accompanied by a devaluation of winning outcomes, such that the behavior is less controlled by the value of wins, and more by gambling-related cues and their learned associations. Cues such as images and sounds of gambling products have been shown to elicit cravings and concomitant neural activation in individuals with problem gambling (see, Brevers et al., 2019). Furthermore, gambling products are host to a wide variety of audiovisual stimuli that may condition habits through repetition during gambling sessions, such as celebratory animations, music, and sound effects generated by electronic gambling machine (EGM) wins. Losing outcomes, by comparison, ostensibly lack these features but can still present clear cues that signal the end of a round (e.g. the sight of the reel’s stopping) or elicit affective cues, such as frustration or boredom, as losses accumulate in succession.
One experimental lens for studying habit is via the effects of repeated practice or familiarity with gambling devices. Gambling practice effects have been observed as within- and between-session increases in risk-taking (e.g. expenditure) during laboratory blackjack and roulette tasks (Bednarz et al., 2013; Blascovich et al., 1973; Ladouceur et al., 1986). EGMs, in contrast, are understudied as a class of gambling product, in relation to practice effects. EGMs are widely recognized to be one of the most harmful types of gambling products (Binde et al., 2017), and these harms are thought to be underpinned by various structural characteristics including a fast speed of gambling and audiovisual feedback that may facilitate habit formation (Griffiths, 1993). Yücel et al. (2018) propose a role of operant conditioning in the establishment of stimulus-response-type gambling habits, which they frame as developing specifically within the context of EGMs. In a brain imaging study in 43 healthy participants, Shao et al. (2013) examined fMRI BOLD responses to the anticipatory (i.e. reel spinning) and outcome phases of a simple three-reel slot machine simulation. Participants were randomly assigned to either practice the task prior to their fMRI scan or complete the task for the first time during their fMRI scan. Compared to the non-practiced participants, those with prior task experience showed altered neural signals in brain reward circuitry, such that anticipatory signaling was enhanced, and the win-related activity was attenuated. Reinforcement learning models are predicated on a similar shift in dopamine cell firing from the unconditioned stimulus (e.g. food) to the conditioned stimulus (e.g. the bell; Schultz et al., 1997).