The Impact of Lexical-semantic Impairment on Spoken Verb Production in Individuals With Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Although episodic memory is the primary concern in individuals with mild cognitive impairment (MCI), other cognitive functions may also be affected, including language. Language impairment in individuals with MCI has been attributed primarily to the breakdown of semantic representations, difficulties in accessing semantic information, and the weakening of executive functions. However, in most prior studies of word processing in individuals with MCI, researchers have used measures focused on noun production. OBJECTIVE: To investigate how verb production tasks might aid in detecting cognitive impairment in individuals with MCI. METHODS: We compared the performance of 45 individuals with MCI and 45 healthy controls on action naming and action fluency tasks. RESULTS: In the action naming task, the performance of participants with MCI was significantly impaired compared to healthy controls in terms of total score, the number of semantic errors produced, and the use of generic terms. In the action fluency task, participants with MCI produced significantly fewer verbs, fewer clusters, and fewer switches than healthy controls. CONCLUSION: The results of our study emphasize the utility of verb production tasks in the identification of cognitive impairment in individuals with MCI and provide evidence of the importance of including action naming and action fluency tasks in the assessment of individuals with MCI.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".