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Record W2040776588 · doi:10.1007/s10899-015-9538-x

“I was that close”: Investigating Players’ Reactions to Losses, Wins, and Near-Misses on Scratch Cards

2015· article· en· W2040776588 on OpenAlexafffund
Madison Stange, Candice Graydon, Mike J. Dixon

Bibliographic record

VenueJournal of Gambling Studies · 2015
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversity of Waterloo
FundersOntario Problem Gambling Research Centre
KeywordsScratchPsychologyValence (chemistry)Outcome (game theory)Social psychologyArousalCognitive psychologyComputer science

Abstract

fetched live from OpenAlex

While scratch cards are a popular, accessible, and inexpensive form of gambling, very little is known about how they affect and influence the player. This study sought to understand the physiological and subjective experience of scratch card play, with special emphasis on the effect of near-miss outcomes (i.e. uncovering two out of three "grand prize" symbols needed to win said prize), which are remarkably prevalent in scratch card games. Thirty-eight undergraduate students from the University of Waterloo each played two custom scratch card games and experienced three types of outcomes (losses, wins and near-misses) while their skin conductance levels (SCLs) and post-reinforcement pauses were recorded. Each participant also rated each outcome in terms of its subjective level of arousal, valence, and frustration. Our results indicate that players interpreted near-misses as negatively valenced, highly arousing, frustrating losses, and were faster to move onto the next game following this type of outcome than following winning outcomes. Additionally, near-miss outcomes were associated with the largest amount of change in SCLs as the outcome was revealed. This work has implications for the problem gambling literature as it provides evidence of the frustration hypothesis of near-misses in scratch cards, and is the first study to examine the physiological and psychological experiences of scratch card players.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.100
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.271
GPT teacher head0.461
Teacher spread0.189 · 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

Citations33
Published2015
Admission routes2
Has abstractyes

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