Does attention to a point in time lead to temporal surround suppression?
Bibliographic record
Abstract
The Selective Tuning (ST) model of visual attention proposed that selection of an attended element includes suppression of the visual network portions that surround the attended element. When attending to a particular location or feature, processing of visual information at nearby locations or features is suppressed. Here, we investigated whether attending to a point in time leads to suppression at nearby time points. We presented a sequence of 11 letters at the screen’s center (SOA 100 ms). One of these letters was the target T, and observers indicated its orientation. In the informative blocks, the target appeared in the same frame within the sequence on most of the trials (‘expected’ condition). On the rest of the trials, the target appeared one or two frames before/after the most-probable frame (‘unexpected’ condition). The most-probable frame varied between blocks. The observers were told which is the most-probable frame at the beginning of the block. In the neutral block, the target appeared randomly in one of the frames. We found significantly higher accuracy in the expected condition than in the neutral and unexpected conditions, indicating that participants allocated temporal attention to the most-probable frame. Furthermore, when the target appeared after the expected frame, the accuracy was significantly lower in the unexpected frame compared to the same frame in the neutral condition, suggesting temporal suppression after the attended time. Consistent with ST's predictions, such an attention-driven temporal suppression may play a role in the precise timing required for dynamic visual behaviors.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".