Measuring the components of attention using the Dalhousie Computerized Attention Battery (DalCAB).
Bibliographic record
Abstract
Using experimentally validated tests to measure the vigilance/alerting, orienting and executive control attention networks, we have developed a novel, theoretically driven battery for measuring attentional abilities, called the Dalhousie Computerized Attention Battery (DalCAB). The current study sought to examine the factor structure of the DalCAB as preliminary evidence for its validation as an assessment tool for the above-named attention networks. One hundred young, healthy adult participants (18 to 31 years) completed the DalCAB (simple reaction time, choice reaction time, dual task, go/no-go, visual search, vertical flanker, and item memory tasks). Exploratory factor analysis of task performance with promax rotation highlighted a 9-factor model, accounting for 54.66% of the shared variance. Factors 1, 2, and 5 are associated with measures reflecting the vigilance/alerting network (response speed, maintenance/preparation and consistency, respectively), Factor 3 is associated with the orienting network (searching measures). Factors 4, 6, 7, and 8 are associated with different aspects of the executive control network including: inhibition, working memory, filtering, and switching. The final factor is associated with vigilance/alerting (fatigue) and executive control (proactive interference). Our model provides preliminary evidence for the validation of our interpretation of the DalCAB as a measure of vigilance/alerting, orienting, and executive control attentional abilities, and contributes to the previously reported evidence for the validation of these tasks for measuring different aspects of attention. We also demonstrate the importance of each of the specific measures derived from the DalCAB tasks, and our results provide further behavioral evidence of the existence of multiple attention-related networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".