The effect of age and sex on clustering and switching during speeded verbal fluency tasks
Bibliographic record
Abstract
Past research has been inconsistent with regard to the effects of normal aging and sex on strategy use during verbal fluency performance. In the present study, both Troyer et al.'s (1997) and Abwender et al.'s (2001) scoring methods were used to measure switching and clustering strategies in 60 young and 72 older adults, equated on verbal ability. Young adults produced more words overall and switched more often during both phonemic and semantic fluency tasks, but performed similarly to older adults on measures of clustering. Although there were no sex differences in total words produced on either fluency task, males produced larger clusters on both tasks, and females switched more frequently than males on the semantic but not on the phonemic fluency task. Although clustering strategies appear to be relatively age-insensitive, age-related changes in switching strategies resulted in fewer overall words produced by older adults. This study provides evidence of age and sex differences in strategy use during verbal fluency tests, and illustrates the utility of combining Troyer's and Abwender's scoring procedures with in-depth categorization of clustering to understand interactions between age and sex during semantic fluency tasks.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".