Attention to a point in time causes a suppressive wake for subsequent time points
Bibliographic record
Abstract
Abstract The Selective Tuning model proposed that attention to a visual stimulus suppresses interfering portions of the processing hierarchy. This has been shown in spatial and featural domains and the proposal extends to the temporal dimension. We investigated whether attending to a point in time leads to processing suppression of nearby time points. We presented a sequence of letters with an embedded target, and observers indicated the target’s orientation. In the neutral condition, the target appeared randomly in one of the frames. In the informative condition, the target appeared in the same frame on most of the trials (‘expected’ trials), and the expected frame varied between blocks (Experiments 1-3) or groups (Experiment 4). On the remaining trials, it appeared before or after the most-probable frame (‘unexpected’ trials). Four different experiments were conducted to adjust the method and in the final one we found higher accuracy in expected trials than in neutral trials, indicating the allocation of temporal attention to the expected frame. When the target appeared after the expected frame, accuracy was lower than in the same neutral condition frame, suggesting an attentional suppressive wake in time (because the effect of attention cannot be observed until after stimulus onset).
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".