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Record W3035418693 · doi:10.1007/s10899-020-09955-0

The Relationship Between Gambling Event Frequency, Motor Response Inhibition, Arousal, and Dissociative Experience

2020· article· en· W3035418693 on OpenAlexfundno aff
Andrew Harris, Georgina Gous, Bobbie de Wet, Mark D. Griffiths

Bibliographic record

VenueJournal of Gambling Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
FundersGambleAwareTrent UniversityNottingham Trent University
KeywordsPsychologyArousalPriming (agriculture)HarmResponse inhibitionDissociativeSocial psychologyCognitive psychologyCognitionClinical psychologyNeuroscience

Abstract

fetched live from OpenAlex

Speed of play has been identified as a key structural characteristic in gambling behaviour, where games involving higher playing speeds enhance the experience of gambling. Of interest in the present study is the consistent finding that games with higher event frequencies are preferred by problem gamblers and are associated with more negative gambling outcomes, such as difficulty quitting the game and increased monetary loss. The present study investigated the impact of gambling speed of play on executive control functioning, focusing on how increased speeds of play impact motor response inhibition, and the potential mediating role arousal and dissociative experience play in this relationship. Fifty regular non-problem gamblers took part in a repeated-measures experiment where they gambled with real money on a simulated slot machine across five speed of play conditions. Response inhibition was measured using an embedded Go/No-Go task, where participants had to withhold motor responses, rather than operating the spin button on the slot machine when a specific colour cue was present. Results indicated that response inhibition performance was significantly worse during faster speeds of play, and that the role of arousal in this relationship was independent of any motor priming affect. The implications of these findings for gambling legislation and gambling harm-minimisation approaches are discussed.

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)0.004
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 score0.030
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.281
GPT teacher head0.479
Teacher spread0.198 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

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