Acute Stress and Perceptual Load Consume the Same Attentional Resources: A Behavioral-ERP Study
Bibliographic record
Abstract
Stress and perceptual load affect selective attention in a paradoxical manner. They can facilitate selectivity or disrupt it. This EEG study was designed to examine the reciprocal relations between stress, load and attention. Two groups of subjects, one that performed the Trier Social Stress Test (TSST), and a control group, were asked to respond to a target letter under low and high perceptual load in the absence or presence of a distractor. In the control group, the distractor increased response times (RTs) for high and low load. In the TSST group, distractor increased RTs under low load only. ERPs showed that distractor's presentation attenuated early visual P1 component and shortened its latency. In the TSST group, distractor reduced P1 component under high load but did not affect its latency. Source localization demonstrated reduced activation in V1 in response to distractors presence in the P1 time window for the TSST group compared to the control group. A behavioral replication revealed that in the TSST group distractors were less perceived under high load. Taken together, our results show that stress and perceptual load affect selectivity through the early stages of visual processing and might increase selectivity in a manner that would block conscious perception of irrelevant stimuli.
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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 | 0.000 |
| 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 | 0.001 |
| 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".