Appraising the ANT: Psychometric and theoretical considerations of the Attention Network Test.
Bibliographic record
Abstract
OBJECTIVE: The Attention Network Test (ANT) is a tool used to assess the efficiency of the 3 attention networks-alerting, orienting, and executive control. The ANT has become popular in the neuropsychological literature since its first description in 2002, with some form of the task currently appearing in no less than 65 original research papers. Although several general reviews of the ANT exist, none provide an analysis of its psychometric properties. METHOD: Data from 15 unique studies were collected, resulting in a large sample (N = 1,129) of healthy individuals. Split-half reliability, variance structure, distribution shape, and independence of measurement of the 3 attention network scores were analyzed, considering both reaction time and accuracy as dependent variables. RESULTS: Split-half reliabilities of reaction time based attention network scores were low for alerting (rweighted = .20, CI 95%weighted [.14, .27], Spearman-Brown r = .38) and orienting (rweighted = .32, CI 95%weighted [.26, .38], Spearman-Brown r = .55), and moderate high for executive control (rweighted = .65, CI 95%weighted [.61, .71], Spearman-Brown r = .81). Analysis of the variance structure of the ANT indicated that power to find significant effects was variable across networks and dependent on the statistical analysis being used. Both analysis of variance (significant interaction observed in 100% of 15 studies) and correlational analyses (multiple-significant inter-network correlations observed) suggest that the networks measured by the ANT are not independent. CONCLUSIONS: In the collection, analysis and interpretation of any test data, psychometric properties, such as those reported here for the ANT, must be carefully considered.
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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 | 0.026 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".