Emergent response allocation and outcome ratings in slot machine gambling.

Abstract

The present study describes a contemporary behavior-analytic model of emergent simulated slot machine gambling. Three laboratory experiments investigated the conditions under which stimuli correlated with different slot machine payout probabilities come to have new, emergent functions without those functions being trained directly. After a successful test for verbal relations (A1-B1-C1 and A2-B2-C2), gamblers and nongamblers were exposed to a task in which high- and low-payout probability functions were established for two slot machines labeled with members of the derived relations (B1 and B2). In Experiment 1, participants provided ratings and chose between concurrently presented slot machines labeled with indirectly related stimuli (C1 and C2). In Experiments 2 and 3, participants made ratings and chose under conditions of nonreinforcement and matched payout probabilities, respectively. Across all three experiments, it was predicted that participants would make more selections of, and give higher liking ratings to, the slot machine indirectly related to the trained high-payout probability machine (C2) than the slot machine indirectly related to the trained low-payout probability machine (C1). Findings supported these predictions. The implications for behavior-analytic research on gambling and the development of verbally based interventions for disordered gambling are discussed.

Why are Some Games More Addictive than Others: The Effects of Timing and Payoff on Perseverance in a Slot Machine Game

“…We measured extinction slightly differently to previous studies, asking participants to choose whether to continue or not rather than when they walked away from the machine. Similar effects have been observed previously when asking people to choose between one of two machines ( Dymond et al, 2012 ). It is important to note that it has been contested whether gamblers are able to discriminate between machines with different rates of reinforcement, measured in terms of preference (e.g., time spent on machine) between two or more simulated slot machines ( Weatherly et al, 2004 ; Haw, 2008b ; Dixon et al, 2013 ; Coates and Blaszczynski, 2014 ).…”

What can be learned about gambling from a learning perspective? A narrative review

“…Contextual factors in the gambling environment may also contribute to gambling through the human tendency to behave in accordance with verbal rules and descriptions of possible outcomes. One thing that seems to withstand scrutiny in the experimental situation is the tendency to allocate gambling behavior to a context associated with better chances of winning, despite the chances for different contexts being equal (Dymond et al, 2012;Hoon et al, 2008;Hoon & Dymond, 2013). If the hypothesis that gambling behavior may be largely rule-governed, withstands further empirical examination, it suggests a process that could decrease the sensitivity of gambling behaviors to actual contingencies and impede extinction in situations in which this could reasonably be expected (Catania, 2007;Hayes et al, 1986).…”

“…These findings were later replicated by Hoon and Dymond (2013), who found that, once established in accordance with contextual cues, preferences could also be reversed by further training. Another study found that, when subjects were exposed to different payout probabilities in slot machines (i.e., one high and one low probability), in later trials, the subjects tended to prefer slot machines according to trained relations with stimuli presented together with the machines (i.e., nonsense syllables that had been matched through training; Dymond, McCann, Griffiths, Cox, & Crocker, 2012), despite non-reinforcement or equal probabilities of wins. These findings underscore how prior experiences, rather than reward frequencies, may impact the characteristics of the gambling behavior and, in all, contribute to an insensitivity of actual reinforcement contingencies.…”

‘This slot is hotter than that one’: symbolic generalization of slot machine preference in simulated gambling

“…Behavioral control by structural features in slot-machine gambling may also occur when the stimulus features are symbolically related (Dixon, Bihler, & Nastally, 2011;Dixon et al, 2009;Dymond, McCann, Griffiths, Cox, & Crocker, 2012). Unlike effects based on formal similarity, symbolic generalizations are not constrained by sensory overlap between features and may potentially influence a wider array of behaviors such as slot-machine gambling choices .…”