Obtaining quality data using behavioral measures of impulsivity in gambling research with Amazon’s Mechanical Turk
Bibliographic record
Abstract
BACKGROUND AND AIMS: To date, no research has examined the viability of using behavioral tasks typical of cognitive and neuropsychology within addiction populations through online recruitment methods. Therefore, we examined the reliability and validity of three behavioral tasks of impulsivity common in addiction research in a sample of individuals with a current or past history of problem gambling recruited online. METHODS: Using a two-stage recruitment process, a final sample of 110 participants with a history of problem or disordered gambling were recruited through MTurk and completed self-report questionnaires of gambling involvement symptomology, a Delay Discounting Task (DDT), Balloon Analogue Risk Task (BART), Cued Go/No-Go Task, and the UPPS-P. RESULTS: Participants demonstrated logically consistent responding on the DDT. The area under the empirical discounting curve (AUC) ranged from 0.02 to 0.88 (M = 0.23). The BART demonstrated good split-third reliability (ρs = 0.67 to 0.78). The tasks generally showed small correlations with each other (ρs = ±0.06 to 0.19) and with UPPS-P subscales (ρs = ±0.01 to 0.20). DISCUSSION AND CONCLUSIONS: The behavioral tasks demonstrated good divergent validity. Correlation magnitudes between behavioral tasks and UPPS-P scales and mean scores on these measures were generally consistent with the existing literature. Behavioral tasks of impulsivity appear to have utility for use with problem and disordered gambling samples collected online, allowing researchers a cost efficient and rapid avenue for conducting behavioral research with gamblers. We conclude with best-practice recommendations for using behavioral tasks using crowdsourcing samples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".