Age-Related Impairments in the Revision of Syntactic Misanalyses: Effects of Prosody
Bibliographic record
Abstract
Two experiments examined whether young and older adults differ in comprehending sentences that contain temporary syntactic closure ambiguities. Experiment 1 examined age-related differences using the Auditory Moving Window (AMW) task, in which sentences were presented in a segment-by-segment self-paced fashion. Experiment 2 examined age-related differences using a sentence recall task, in which sentences were presented in their entirety. Sentences were constructed to have cooperating prosody (i.e., where prosody is consistent with the syntactic boundaries), baseline prosody (i.e., where prosody is ambiguous in the syntactically ambiguous region), and conflicting prosody (i.e., where cross-splicing relocates the prosodic phrase break at a misleading point in syntactic structure). The results showed that both young and older adults make comparable use of prosodic information to interpret temporary syntactic ambiguities, although younger adults may make use of this information more quickly than older adults. In addition, older adults appeared to be less able than young adults to revise initial syntactic misinterpretations caused by conflicting prosodic information. These results are interpreted with respect to age-related impairments in the allocation of working memory resources and inefficient inhibitory function during spoken language processing.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".