Attentional Bias Modification With Serious Game Elements: Evaluating the Shots Game
Bibliographic record
Abstract
BACKGROUND: Young adults often experiment with heavy use of alcohol, which poses severe health risks and increases the chance of developing addiction problems. In clinical patients, cognitive retraining of automatic appetitive processes, such as selective attention toward alcohol (known as "cognitive bias modification of attention," or CBM-A), has been shown to be a promising add-on to treatment, helping to prevent relapse. OBJECTIVE: To prevent escalation of regular use into problematic use in youth, motivation appears to play a pivotal role. As CBM-A is often viewed as long and boring, this paper presents this training with the addition of serious game elements as a novel approach aimed at enhancing motivation to train. METHODS: A total of 96 heavy drinking undergraduate students carried out a regular CBM-A training, a gamified version (called "Shots"), or a placebo training version over 4 training sessions. Measures of motivation to change their behavior, motivation to train, drinking behavior, and attentional bias for alcohol were included before and after training. RESULTS: Alcohol attentional bias was reduced after training only in the regular training condition. Self-reported drinking behavior was not affected, but motivation to train decreased in all conditions, suggesting that the motivational features of the Shots game were not enough to fully counteract the tiresome nature of the training. Moreover, some of the motivational aspects decreased slightly more in the game condition, which may indicate potential detrimental effects of disappointing gamification. CONCLUSIONS: Gamification is not without its risks. When the motivational value of a training task with serious game elements is less than expected by the adolescent, effects detrimental to their motivation may occur. We therefore advise caution when using gamification, as well as underscore the importance of careful scientific evaluation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".