Online Modulation of Selective Attention is not Impaired in Healthy Aging
Bibliographic record
Abstract
Background/Study Context: Reduced processing speed pervades a great many aspects of human aging and cognition. However, little is known about one aspect of cognitive aging in which speed is of the essence, namely, the speed with which older adults can deploy attention in response to a cue. METHODS: = 68.9 years) adults. On each trial of a short-term memory task, a cue identified which of two briefly presented stimuli was task relevant and which one should be ignored. After a short delay, subjects demonstrated recall by reproducing from memory the task-relevant stimulus. This produced estimates of (i) accuracy with which the task-relevant stimulus was recalled, (ii) the influence of stimuli encountered on previous trials (a prototype effect), and (iii) the influence of the trial's task-irrelevant stimulus. RESULTS: For both groups, errors in recall were considerably smaller when selective attention was cued before rather than after presentation of the stimuli. Both groups showed serial position effects to the same degree, and both seemed equally adept at exploiting the stimuli encountered on previous trials as a means of supplementing recall accuracy on the current trial. CONCLUSION: Younger and older subjects may not differ reliably in capacity for cue-directed temporal modulation of selective attention, or in ability to draw on previously seen stimuli as memory support.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".