Unconstrained, phonemic and semantic verbal fluency: age and education effects, norms and discrepancies
Bibliographic record
Abstract
OBJECTIVE: To present performance norms and discrepancy score of three one-minute verbal fluency tasks (VFTs); to investigate age and education effects; to analyze the differences between time intervals; and to investigate whether these differences varied according to age and education. METHOD: Three hundred adults divided into three age groups (19-39; 40-59; 60-75) and two groups of educational level (2 to 7 years; 8 years or more) performed unconstrained, semantic, and phonemic VFTs. We compared the performance of the groups using two-way ANOVA with post-hoc Bonferroni test. The depression scale score was covariate. The time interval of verbal fluency was the variable used for subjects' comparison (repeated measures ANOVA). RESULTS AND CONCLUSIONS: Our results suggest that there are age and education effects on phonemic and unconstrained VFTs. We also found an interaction between those variables in the semantic VFT (time intervals and total time) and in the differences between semantic and phonemic tasks. The repeated measures analysis revealed age effects on semantic VFTs and education effects on the phonemic and semantic VFTs. Such findings are relevant for clinical neuropsychology, contributing to avoid false-positive or false-negative interpretation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".