Understanding Cognitive and Linguistic Deficits in Aphasia Through Naming Reaction Time, Working Memory, and Executive Function
Bibliographic record
Abstract
Objectives: Aphasia, an acquired multimodal language disorder caused by brain damage, impacts various linguistic and cognitive skills. Naming is a key aspect of language processing. This skill relies heavily on cognitive functions, such as reaction time, working memory, and executive functions, which together support effective communication. Understanding the relationships between these components can provide critical insights for improving rehabilitation strategies. Methods: This study included 20 individuals diagnosed with Broca’s aphasia and 20 neurologically healthy controls. The participants were assessed using tasks measuring rapid automatized naming (RAN), reaction time, working memory, and executive function. Results: People with aphasia (PWA) demonstrated significantly lower performance in all assessed domains compared to controls (P<0.001). RAN scores were markedly lower, with performance improving in high-context environments (P<0.001). Reaction times were significantly delayed in linguistic and non-linguistic tasks (P<0.001). Correlation analysis revealed positive relationships between RAN, working memory, and executive functions (P<0.001). However, RAN showed no direct correlation with reaction time (P>0.05). Discussion: The findings indicate a relationship between cognitive and linguistic processes in aphasia, with working memory and executive function significantly related to language performance. Contextual visual cues are also associated with improvements in naming accuracy and speed. These results highlight the potential value of integrated cognitive-linguistic rehabilitation approaches for enhancing communication skills and quality of life (QoL) in individuals with aphasia.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".