Effect of aging, education, reading and writing, semantic processing and depression symptoms on verbal fluency
Bibliographic record
Abstract
Verbal fluency tasks are widely used in (clinical) neuropsychology to evaluate components of executive functioning and lexical-semantic processing (linguistic and semantic memory). Performance in those tasks may be affected by several variables, such as age, education and diseases. This study investigated whether aging, education, reading and writing frequency, performance in semantic judgment tasks and depression symptoms predict the performance in unconstrained, phonemic and semantic fluency tasks. This study sample comprised 260 healthy adults aged 19 to 75 years old. The Pearson correlation coefficient and multiple regression models were used for data analysis. The variables under analysis were associated in different ways and had different levels of contribution according to the type of verbal fluency task. Education had the greatest effect on verbal fluency tasks. There was a greater effect of age on semantic fluency than on phonemic tasks. The semantic judgment tasks predicted the verbal fluency performance alone or in combination with other variables. These findings corroborate the importance of education in cognition supporting the hypothesis of a cognitive reserve and confirming the contribution of lexical-semantic processing to verbal fluency.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".