Semantic fluency in aphasia: clustering and switching in the course of 1 minute
Bibliographic record
Abstract
BACKGROUND: Verbal fluency tasks are included in a broad range of aphasia assessments. It is well documented that people with aphasia (PWA) produce fewer items in these tasks. Successful performance on verbal fluency relies on the integrity of both linguistic and executive control abilities. It remains unclear if limited output in aphasia is solely due to their lexical retrieval difficulties or has a basis in their executive control abilities. Analysis techniques, such as temporal characteristics of word retrieved, clustering and switching, are better positioned to inform the debate surrounding the lexical and/or executive control contribution for success in verbal fluency. AIMS: To investigate the differences in quantitative (i.e., number of correct words) and qualitative (i.e., switching, clustering and word-retrieval times) performances on animal fluency task as a function of time between PWA and healthy control speakers (CS). METHODS & PROCEDURES: Animal fluency data for 60 s were collected from 34 PWA and 34 CS, and responses were time stamped. The 60-s period was divided into four equal intervals of 15 s each (i.e., 15, 30, 45 and 60 s). The number of correct words, cluster size, number of switches, within-cluster pause and between-cluster pause were evaluated as a function of four 15-s time intervals between PWA and CS. OUTCOMES & RESULTS: Compared with CS, PWA produced fewer words, had smaller cluster sizes and switched a fewer number of times. A decrease in the number of switches correlated with an increase in between-cluster pause durations. PWA showed longer within- and between-cluster pauses than CS. The two groups showed specific differences in the temporal pattern of the responses: as time evolved both PWA and CS showed decreased productivity for the number of correct words, but PWA reached the asymptote earlier in the time course than CS, neither group showed a change in cluster size, and the number of switches decreased as a function of time only for CS. CONCLUSIONS & IMPLICATIONS: The findings suggest that for PWA the search and retrieval process is less productive and more effortful. This is indicated by smaller cluster size, fewer switches associated with increased between-cluster pause durations, as well as overall slowed retrieval times for the words. This shows that the difficulties with verbal fluency performance in aphasia have a strong basis in their lexical retrieval processes, as well as some difficulties in the executive component of the task.
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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 | 0.000 | 0.002 |
| 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".