Difficulty suppressing visual distraction while dual tasking
Bibliographic record
Abstract
Abstract Human beings must often perform multiple tasks concurrently or in rapid succession. Laboratory research has revealed striking limitations in the ability to dual task by asking participants to identify two target objects that are inserted into a rapid stream of irrelevant items. Under a variety of conditions, identification of the second target (T2) is impaired for a short period of time following presentation of the first target (T1). Several theories have been developed to account for this “attentional blink” (AB), but none makes a specific prediction about how processing of T1 might impact an observer’s ability to ignore a salient distractor that accompanies T2. Using event-related potentials (ERPs) to track target and distractor processing, we show that healthy young adults are capable of suppressing a salient visual-search distractor (D2) while dual tasking (as measured by the PDcomponent, which has been associated with suppression) but struggle to do so shortly after the appearance of T1. In fact, the impairment was more severe for distractor processing than it was for target processing (as measured by the N2pc component). Whereas, the T2-elicited N2pc was merely delayed during the AB, the distractor PDwas reduced in magnitude and was found to be statistically absent. We conclude that the inhibitory control processes that are typically engaged to prevent distraction are unavailable while an observer is busy processing a target that appeared earlier.
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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 | 0.001 |
| 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".