Object-based perception mediates the effect of exogenous attention on temporal resolution
Bibliographic record
Abstract
The effects of target distinctiveness, target placeholders, and target spatial separation on the relation between exogenous attention and temporal resolution were examined in a visual temporal order judgement (TOJ) task. When identical targets were presented at different locations within a cued or uncued placeholder, attention degraded temporal resolution (Experiment 1), but when distinct targets were presented at different locations that cueing effect disappeared (Experiment 2), and when the target placeholders were not used, attention enhanced temporal resolution for distinct targets presented at different locations (Experiment 3). Attention also degraded temporal resolution when distinct targets were presented at the same location (Experiment 4). The latter two results were then replicated in a task in which distinct targets appeared randomly at the same or different locations (Experiment 5). Clearly, the nature of the relation between exogenous attention and temporal resolution is not a straightforward one—the relative location of targets, the similarity of targets, and the presence of placeholders all qualitatively affect the cueing relation. We hypothesize a mediating role for object-related processes. Specifically, exogenous attention enhances feature binding and related object representations, which subsequently degrade temporal resolution.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".