Interference, aging, and visuospatial working memory: The role of similarity.
Bibliographic record
Abstract
OBJECTIVE: Older adults' performance on working memory (WM) span tasks is known to be negatively affected by the buildup of proactive interference (PI) across trials. PI has been reduced in verbal tasks and performance increased by presenting distinctive items across trials. In addition, reversing the order of trial presentation (i.e., starting with the longest sets first) has been shown to reduce PI in both verbal and visuospatial WM span tasks. We considered whether making each trial visually distinct would improve older adults' visuospatial WM performance, and whether combining the 2 PI-reducing manipulations, distinct trials and reversed order of presentation, would prove additive, thus providing even greater benefit. METHOD: Forty-eight healthy older adults (age range = 60-77 years) completed 1 of 3 versions of a computerized Corsi block test. For 2 versions of the task, trials were either all visually similar or all visually distinct, and were presented in the standard ascending format (shortest set size first). In the third version, visually distinct trials were presented in a reverse order of presentation (longest set size first). RESULTS: Span scores were reliably higher in the ascending version for visually distinct compared with visually similar trials, F(1, 30) = 4.96, p = .03, η² = .14. However, combining distinct trials and a descending format proved no more beneficial than administering the descending format alone. CONCLUSIONS: Our findings suggest that a more accurate measurement of the visuospatial WM span scores of older adults (and possibly neuropsychological patients) might be obtained by reducing within-test interference.
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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) | 0.000 | 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 | 0.001 |
| 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".