How Age and Linguistic Competence Affect Memory for Heard Information
Bibliographic record
Abstract
The short-term memory performance of a group of younger adults, for whom English was a second language (young EL2 listeners), was compared to that of younger and older adults for whom English was their first language (EL1 listeners). To-be-remembered words were presented in noise and in quiet. When presented in noise, the listening situation was adjusted to ensure that the likelihood of recognizing the individual words was comparable for all groups. Previous studies which used the same paradigm found memory performance of older EL1 adults on this paired-associate task to be poorer than that of their younger EL1 counterparts both in quiet and in a background of babble. The purpose of the present study was to investigate whether the less well-established semantic and linguistic skills of EL2 listeners would also lead to memory deficits even after equating for word recognition as was done for the younger and older EL1 listeners. No significant differences in memory performance were found between young EL1 and EL2 listeners after equating for word recognition, indicating that the EL2 listeners' poorer semantic and linguistic skills had little effect on their ability to memorize and recall paired associates. This result is consistent with the hypothesis that age-related declines in memory are primarily due to age-related declines in higher-order processes supporting stream segregation and episodic memory. Such declines are likely to increase the load on higher-order (possibly limited) cognitive processes supporting memory. The problems that these results pose for the comprehension of spoken language in these three groups are discussed.
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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 | 0.001 | 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".