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Record W3005642768 · doi:10.1111/add.15010

Effects of winning cues and relative payout on choice between simulated slot machines

2020· article· en· W3005642768 on OpenAlexafffundabout
Marcia L. Spetch, Christopher R. Madan, Yang S. Liu, Elliot A. Ludvig

Bibliographic record

VenueAddiction · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsUniversity of Alberta
FundersAlberta Gambling Research Institute, University of CalgaryNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyComputer sciencePhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Abstract Background and aims Cues associated with winning may encourage gambling. We assessed the effects on risky choice of slot machine of: (1) neutral sounds paired with winning, (2) casino‐related cues (such as the sound of coins dropping and pictures of dollar signs) and (3) relative payouts. Design Experimental studies in which participants repeatedly chose between safer and riskier simulated slot machines. Safer slot machines paid the same amount regardless of which symbols lined up. Risky machines paid different amounts depending on which symbols lined up. Effects of initially neutral sounds paired with the best payout were assessed between‐groups (experiment 1a) and within‐participants (experiment 1b). In experiment 2, pairing of casino‐related audiovisual cues with payout was assessed within participants, and cue timing was assessed between groups. Setting A university research laboratory in Edmonton, Canada. Participants Undergraduate students (n = 630 across three experiments). Measurements Preference for riskier over safer machines, preference between machines that differed in cues, payout recall and frequency estimates for payouts. Risky choice was calculated as the proportion of choices of the risky machine when presented with a fixed machine of the same expected value. Findings In experiment 1a, risky choice was slightly increased by pairing a sound with the best payout compared with pairing the sound with a lower payout (P = 0.04, d = 0.28) but not compared with no sound [P = 0.36, d = 0.13, Bayes factors (BF)10 = 0.22]. In experiment 1b, people did not prefer a machine with a best‐payout sound over one with a lower‐payout sound (P = 0.67, d = 0.03, BF10 = 0.11). Relative payout affected choice: risky choices were higher for high‐ than low‐payout decisions (P < 0.001, d = 0.53). In experiment 2, people preferred machines with casino‐related cues paired with winning (P < 0.001, r2 = 0.11) and cue timing (at choice or concurrently with the win) had no effect (P = 0.95, r2 = 0.0, BF10 = 0.05). Casino‐related cues also enhanced payout memory (P = 0.013 and 0.006). Cue effects were not specific to risk: people also preferred fixed‐payout machines with casino‐related cues (P < 0.001, r2 = 0.16). Conclusions In a gambling simulation, student participants chose more risky slot machines when payouts were relatively higher and when casino‐related cues were associated with payouts. Pairing a neutral sound with the best payout did not consistently affect slot machine choice, and the effect of casino cues did not depend on their timing. Casino‐related cues enhanced payout memory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.244
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations35
Published2020
Admission routes3
Has abstractyes

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