Introducing the portable AttentionTrip: An engaging tool for measuring the networks of attention
Bibliographic record
Abstract
BACKGROUND: Measurement of the efficacy of the networks of attention is a frequent component of research in cognitive and clinical neuroscience. Developed in 2002, the Attention Network Test (ANT), has become the most widely used tool for this purpose. NEW METHOD: In 2017 a more engaging, game-like tool based on the ANT, called the AttentionTrip was described. The network scores from five studies which used AttentionTrip are shown to be robust. NEWER METHOD: That version of AttentionTrip required a steering wheel and desk-top computer. Here we describe a new, portable version of the AttentionTrip that is administered using a hand-held tablet (iPad) RESULTS: Three samples of participants (total = 44) completed the portable version of AttentionTrip. The network scores generated using the portable AttentionTrip were also robust. Effect sizes compare favourably with those generated by the ANT and the desktop version. CONCLUSIONS: The findings support the use of the portable AttentionTrip as an alternative to the ANT when user engagement is important, such as when participants are prone to boredom, and when repeated administrations are required.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".