Comprehension cueing strategies in elderly: a window into cognitive decline?
Bibliographic record
Abstract
Language abilities gradually decline as we age, but the mechanisms of this decline are not well understood.The present study investigated comprehension of subject vs. object who and which direct questions (DQs), embedded questions (EQs) and relative clauses (RCs) in 39 cognitively healthy native speakers of Spanish.The elderly participants (n = 21) were further classified according to their scores on a general cognitive test, Montreal Cognitive Assessment (MoCA), into a group with low MoCA scores, LM (n = 10), and a group with normal MoCA scores, NM (n = 11).A mixed-model, repeated-measures analysis of variance (ANOVA) showed that the elderly participants achieved significantly worse accuracy and speed than the young participants (Y) in all tasks.Accuracy was significantly lower and reaction times significantly longer in the LM group compared to the NM group in DQs and RCs.Accuracy in comprehension of EQs was also worse in LM compared to NM, with no significant difference in RTs between the two groups.The results are explained within the competition model and reliance on a language-specific cueing strategy.Reliance on cueing strategies in sentence comprehension may be an effective indicator of cognitive decline associated with aging.
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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 | 0.001 |
| 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".