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Video lottery: winning expectancies and arousal

2003· article· en· W2143809191 on OpenAlexaff
Robert Ladouceur, Serge Sévigny, Alex Blaszczynski, Kieron O’Connor, Marc E. Lavoie

Bibliographic record

VenueAddiction · 2003
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversité de MontréalUniversité Laval
Fundersnot available
KeywordsLotteryExpectancy theoryArousalPsychologySocial psychologyVideo gameSession (web analytics)Developmental psychologyAdvertisingMultimedia

Abstract

fetched live from OpenAlex

AIMS: This study investigates the effects of video lottery players' expectancies of winning on physiological and subjective arousal. DESIGN: Participants were assigned randomly to one of two experimental conditions: high and low winning expectancies. SETTING: Participants played 100 video lottery games in a laboratory setting while physiological measures were recorded. Level of risk-taking was controlled. PARTICIPANTS: Participants were 34 occasional or regular video lottery players. They were assigned randomly into two groups of 17, with nine men and eight women in each group. INTERVENTION: The low-expectancy group played for fun, therefore expecting to win worthless credits, while the high-expectancy group played for real money. MEASUREMENTS: Players' experience, demographic variables and subjective arousal were assessed. Severity of problem gambling was measured with the South Oaks Gambling Screen. In order to measure arousal, the average heart rate was recorded across eight periods. FINDINGS: Participants exposed to high as compared to low expectations experienced faster heart rate prior to and during the gambling session. According to self-reports, it is the expectancy of winning money that is exciting, not playing the game. CONCLUSIONS: Regardless of the level of risk-taking, expectancy of winning is a cognitive factor influencing levels of arousal. When playing for fun, gambling becomes significantly less stimulating than when playing for money.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

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

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 head0.052
GPT teacher head0.339
Teacher spread0.287 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations109
Published2003
Admission routes1
Has abstractyes

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