Functional connectivity associated with individual differences on the emotional attentional blink task
Bibliographic record
Abstract
The emotional attentional blink (EAB) task has been used in numerous studies to examine attention capture by emotional stimuli. In this task, participants are instructed to detect a rotated image embedded within a rapid-serial-visual-presentation (RSVP) of images. When an emotional photograph ("critical distractor") appears 200 msec before the target item, participants consistently show a dramatic impairment in target detection. However, the size of the EAB differs across participants. In the current study, we used resting-state fMRI to examine whether differences in functional connectivity were related to individual differences in the size of participants' EAB effects. Twenty-five participants completed a resting-state fMRI scan and an EAB task in different experimental sessions. On each trial of the EAB task, a negative, erotic, or neutral distractor appeared either 200 msec or 800 msec prior to a rotated target image. Accuracy scores were calculated for each distractor type (negative, erotic, and neutral) and lag (200 msec vs. 800 msec). Values representing the negative EAB effect and the erotic EAB effect trials were then entered as covariates in seed-based analyses. The functional connectivity between the right orbitofrontal cortex and parietal regions were positively correlated with the size of both the negative and erotic EAB effects. The erotic EAB was also associated with the functional connectivity between the right orbitofrontal cortex and left middle frontal gyrus.
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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) | 0.002 | 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".