Different effects of aging and gender on the temporal resolution in attentional tracking
Bibliographic record
Abstract
The current study examined the role of temporal resolution of attention in the decline in multiple object tracking abilities with healthy aging. The temporal resolution of attention is known to limit attentional tracking of one and multiple targets (Holcombe & Chen, 2013). Here, we examined whether aging is associated with a lower temporal resolution of attention when tracking one target, the efficiency of splitting attention across multiple targets, or both. Stimuli comprised three concentric rings containing five or 10 equally spaced dots. While maintaining central fixation, younger and older participants tracked a target dot on one, two, or three rings while the rings rotated around fixation in random directions for 5 s. Rotational speed was varied to estimate speed or temporal frequency thresholds in six conditions. Results showed that younger and older participants had similar temporal frequency thresholds for tracking one target, but the addition of one and two more targets reduced thresholds more in the older group compared to the younger group. Gender also affected performance, with men having higher temporal frequency thresholds than women, independently of the number of targets. These findings indicate that the temporal resolution of attention for a single target depends on gender but is not affected by aging, whereas aging specifically affects the efficiency of dividing attention across multiple targets.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".