Effects of Working Memory Load and Age on the Comprehension of Passive Sentences
Bibliographic record
Abstract
The ability of older adults to comprehend sentences may decline due to the cognitive changes in working memory. Therefore, an increase in working memory demands during sentence comprehension would result in poorer performance among older adults. To test this hypothesis, the present study explored sentence comprehension as a result of manipulations of age and working memory loads using a sentence-picture matching task. 35 older adults and 35 younger adults were required to match Mandarin passive sentences (high working memory load) and active sentences (low working memory load) with pictures. Passive sentences were found to be more difficult than active sentences for all participants. Older adults responded to passive sentences more slowly than younger adults. However, no significant age difference was found in accuracy of responses. Accuracy on passive sentence comprehension was marginally correlated with syntactic complexity effect among older adults. Compared with younger adults, older adults seem to be more disrupted by the increased WM load in passive sentence comprehension, but they can compensate for the decline in the accuracy of comprehension by spending extra time on sentences with high WM load.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".