Bibliographic record
Abstract
This study examined whether altering the metaphor—or visual skin—of a cognitive assessment game affects its psychometric properties. BrainTagger is a suite of serious games used to assess executive functions such as processing speed, response inhibition, cognitive flexibility, and working memory. While the original version used a Whackamole metaphor, user feedback prompted the development of an alternative Gardening version with identical mechanics but a more mature visual theme. Using a within-subject design, 109 undergraduate participants completed both versions of 4 BrainTagger games. Correlation analyses revealed significant relationships between versions across all domains, particularly for working memory ( r = .38), response inhibition ( r = .36 for d-prime, −.75 for median correct RT), cognitive flexibility ( r = .37), and processing speed ( r = .61). However, the speed accuracy tradeoff in working memory performance was sensitive to metaphor changes, likely due to task demands requiring either the phonological loop or the visuo-spatial sketchpad. These findings suggest that while game metaphors can be adapted to suit user preferences without broadly compromising assessment validity, specific cognitive domains—such as working memory—may be more vulnerable to metaphor-driven variance. Implications for the design and validation of cognitive assessment tools are discussed.
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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 | 0.006 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".