TAG-ME again: A serious game for measuring working memory
Bibliographic record
Abstract
BrainTagger (demo version: researcher-demo.braintagger.com) is a suite of Target Acquisition Games for Measurement and Evaluation (TAG-ME). Here we introduce TAG-ME Again, a serious game modeled after the well-established N-Back task, to assess working memory ability across three difficulty levels corresponding to 1-, 2-, and 3-back conditions. We also report on two experiments aimed at assessing convergent validity with the N-Back task. Experiment 1 examined correlations with N-Back task performance in a sample of adults (n = 31, 18–54 years old) across three measures: reaction time; accuracy; a combined RT/accuracy metric. Significant correlations between game and task were found, with the strongest relationship being for the most difficult version of the task (3-Back). In Experiment 2 (n = 66 university students, 18–22 years old), we minimized differences between the task and the game by equating stimulus-response mappings and spatial processing demands. Significant correlations were found between game and task for both the 2-Back and 3-Back levels. We conclude that TAG-ME Again is a gamified task that has convergent validity with the N-Back Task.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".