Attention and ageing: Measuring effects of involuntary and voluntary orienting in isolation and in combination
Bibliographic record
Abstract
The cueing paradigm provides an established method for eliciting involuntary and voluntary attention shifts. Involuntary orienting is traditionally measured with non-predictive peripheral cues and voluntary orienting with predictive central arrows. Recent studies with young adults have established that predictive central arrows trigger a combination of involuntary and voluntary orienting, raising the possibility that previous studies - including those with older adults - misinterpreted their findings with central arrow cues as isolating the effects of voluntary attention. The present experiment applied different cueing conditions that measured involuntary orienting, voluntary orienting, and involuntary and voluntary orienting in combination in older adults. The results show that past studies of voluntary orienting in older adults confound involuntary and voluntary orienting. Cueing effects in a condition that for the first time isolated voluntary orienting (predictive number cues) with older adults were significant, and comparable to effects for younger adults, demonstrating that older adults successfully utilize cues to direct their spatial attention strategically. A similar normal pattern of orienting was observed for involuntary orienting. Our study provides a methodology that can be applied effectively to isolate and investigate the effects of age on voluntary and involuntary attention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".