Using Sound to Unmask Losses Disguised as Wins in Multiline Slot Machines
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Losses disguised as wins (LDWs) are slot machine outcomes where participants bet on multiple lines and win back less than their wager. Despite losing money, the machine celebrates these outcomes with reinforcing sights and sounds. Here, we sought to show that psychophysically and psychologically, participants treat LDWs as wins, but that we could expose LDWs as losses by using negative sounds as feedback. 157 participants were allocated into one of three conditions: a standard sound condition where LDWs, despite being losses, are paired with winning sights and sounds; a silent condition, where LDWs are paired with silence; and a negative sound condition where LDWs and regular losses are both followed by a negative sound. After viewing a paytable, participants conducted 300 spins on a slot machine simulator while heart rate deceleration (HRD) and skin conductance responses (SCRs) were monitored. Participants were then shown 20 different spin outcomes including LDWs and asked whether they had won or lost on that outcome. Participants then estimated on how many spins (out of 300) they won more than they wagered. SCRs were similar for losses and LDWs (both smaller than actual wins). HRD, however, was steeper for both wins and LDWs, compared to losses. In the standard condition, a majority of participants (mis)categorized LDWs as wins, and significantly overestimated the number of times they actually won. In the negative sound condition, this pattern was reversed; most participants correctly categorized LDWs as losses, and they gave high-fidelity win estimates. We conclude that participants both think and physiologically react to LDWs as though they are wins, a miscategorization that misleads them to think that they are winning more often than they actually are. Sound can be used to effectively prevent this misconception and unmask the disguise of LDWs.
KeywordsSlot machines Losses disguised as wins Skin conductance responses Heart rate deceleration Sounds Categorization
The researchers acknowledge the generous support of the Ontario Problem Gambling Research Centre for the funding of this project. Special thanks to Lisa Wojtowicz, Sarah Moros and Jeff Templeton for their help in aspects of the data analysis.
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