Neural dynamics of the attentional blink revealed by encoding orientation selectivity during rapid visual presentation
Bibliographic record
Abstract
Abstract The human brain is inherently limited in the information it can make consciously accessible. When people monitor a rapid stream of visual items for two targets, they can typically report the first, but not the second target, if these appear within 200-500 ms of each other, a phenomenon known as the attentional blink (AB). No work has determined the neural basis for the AB, partly because conventional neuroimaging approaches lack the temporal resolution to adequately characterise the neural activity elicited by each item in a rapid stream. Here we introduce a new approach that can identify the precise effect of the AB on behaviour and neural activity. Specifically, we employed a multivariate encoding approach to extract feature-selective information carried by randomly-oriented gratings within a rapid serial stream. We show that feature selectivity is enhanced for correctly reported targets and suppressed when the same items are missed. By contrast, no such effects were apparent for irrelevant distractor items. Our findings point to a new theoretical account that involves both short- and long-range temporal interactions between visual items competing for consciousness.
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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 | 0.002 |
| 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".