Latent Structure of Executive Functioning/Learning Tasks in the CogState Computerized Battery
Bibliographic record
Abstract
This study tested whether executive functioning (EF)/learning tasks from the CogState computerized test battery show a unitary latent structure. This information is important for the construction of composite measures on these tasks for applied research purposes. Based on earlier factor analytic research, we identified five CogState tasks that have been labeled as EF/learning tasks and examined their intercorrelations in a new sample of Finnish birth cohort mothers ( N = 233). Using confirmatory factor analyses, we compared two single-factor EF/learning models. The first model included the recommended summative scores for each task. The second model exchanged summative scores for first test round results for the three tasks providing these data, as initial task performance is expected to load more heavily on EF. A single-factor solution provided a good fit for the present five EF/learning tasks. The second model, which was hypothesized to tap more onto EF, had slightly better fit indices, χ 2 (5) = 1.37, p = .93, standardized root mean square residual (SRMR) = .02, root mean square error of approximation (RMSEA) = .00, 90% CI = [.00–.03], comparative fit index (CFI) = 1.00, and more even factor loadings (.30–.56) than the first model, χ 2 (5) = 4.56, p = .47, SRMR = .03, RMSEA = .00, 90% CI = [.00–.09], CFI = 1.00, factor loadings (.20–.74), which was hypothesized to tap more onto learning. We conclude that the present CogState sum scores can be used for studying EF/learning in healthy adult samples, but call for further research to validate these sum scores against other EF tests.
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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 | 0.000 | 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 | 0.000 | 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".