Aging and the Role of Working Memory Resources in Visuospatial Attention
Bibliographic record
Abstract
Visuospatial attention has been shown to be robust to the effects of increasing age. Nonetheless, models linking individual differences in working memory capacity to attentional performance suggest that older adults may experience disruptions in visuospatial attention under conditions of resource load. Two experiments were conducted to investigate the effects of age and concurrent working memory load on two tasks that have been proposed to require posterior attentional processes. The findings suggest that loading working memory resources selectively disrupts performance on a nonintegrated Stroop task, whereas cue utilization remains intact. In addition, imposing a working memory load delays the deployment of visuospatial attention in both experiments. Regarding the effects of age, findings suggest that older adults can effectively perform both attentional tasks despite working memory load. Age differences did emerge in the time course of cue utilization. Findings point to the resilience of visuospatial attention in aging, even under conditions of significant cognitive load. We discuss these results and their implications for models postulating a role for working memory capacity in attentional behaviors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".