Temporal cuing modulates alpha oscillations during auditory attentional blink
Bibliographic record
Abstract
Attentional blink (AB) refers to the phenomenon whereby the correct identification of a visual or auditory target impairs processing of a subsequent probe. Although it has been shown that knowing in advance, when the probe would be presented, reduces the attentional blink and increases the amplitude of event-related potential (ERP) elicited by the probe, the neural mechanism by which attention mitigates the AB remains unclear. Here, we used time-frequency analysis to further explore the mechanism of the auditory attentional blink. Participants were presented a series of rapid auditory stimuli and asked to indicate whether a target and a probe were present in the sequence. In half of the trials, participants were cued to the probe position relative to the target ('Early' or 'Late'). Probe detection and ERP amplitude elicited by the probe decreased when the probe was presented shortly after the target compared to when it was presented later after the target. Importantly, the behavioral and ERP correlates of probe discrimination significantly improved when the 'Early' cue was presented. The improvement in processing the probe in the cued condition was accompanied by the decrease in alpha activity (8-13 Hz) after the time when the probe was expected; suggesting that successfully directing attention to time window where the probe would likely occur reduces the processing resources needed to suppress distractors. This in turn freed up available processing resources for the target and probe at the short-term consolidation stage, which ultimately reduced the auditory attentional blink.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".