Reduced attentional control in older adults leads to deficits in flexible prioritization of visual working memory.
Bibliographic record
Abstract
Visual working memory has been demonstrated to be flexibly distributed across sample items depending on each item’s priority (Emrich, Lockhart, Al-Aidroos, 2017). This ability to flexibly prioritize information may depend on attentional control (Salahub, et al., in-press), which is the ability to select goal-relevant target information and suppress goal-irrelevant non-target information from entering visual working memory. To test this hypothesis, we examined flexible prioritization in a group of older adults, a population known for impairments in attentional control. Participants performed a delayed-recall task in which the number and validity of simultaneously presented spatial cues was varied. On some trials, memory load was manipulated by presenting 1, 2, or 4 cues with 100% validity. In the flexible prioritization condition, 1 item was cued with a 50% valid cue. Errors were modeled with the three-component mixture model to distinguish precision, guess-rate, and non-target errors (Bays, Catalao, & Husain, 2009). In a sample of older adults (ages 65-85), recall precision was consistently lower, and guess-rate was consistently higher than in a group of young adults (ages 18-30) across all conditions. Importantly, older adults, but not young adults, also made significantly more swap errors when flexible prioritization demands increased but memory load remained constant. This deficit was most evident under the highest flexible prioritization demands: when an un-cued item was probed. These results suggest that flexible prioritization is impaired in those with reduced attentional-control. Moreover, these findings are consistent with work showing that working memory impairments observed in older adults are due to a mis-allocation of resources (Hasher & Zacks, 1988; Gazzaley et al., 2005).
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".