DLPFC controls the rapid neural response to visual threat: An ERP and rTMS study
Bibliographic record
Abstract
Abstract Individuals are faster at detecting threatening stimuli than neutral stimuli. While generally considered a rapid bottom-up response, this threat superiority effect is also modulated by top-down mechanisms known to rely on the dorsolateral prefrontal cortex (DLPFC). What remains unclear is whether the response is modulated only at later stages of processing, or whether rapid attention to threat itself is controlled in a top-down manner. To test this, we used repetitive transcranial magnetic stimulation (rTMS) to inhibit activity in the DLPFC, and measured EEG to index the immediate neural response to threat. Participants attended two sessions where they performed a visual search task with threatening or neutral targets. Prior to this, they received 15 minutes of 1 Hz inhibitory or sham rTMS targeting the right DLPFC. We measured the impact of rTMS on the P1, a rapid visually-evoked potential that is modulated by attention. We found that threatening targets increased the amplitude of the P1 in the sham condition, but inhibition of the DLPFC abolished this increase. These results suggest that the neural signature of rapid attentional detection of threat, even at its earliest stage, is influenced in a top-down fashion by the right DLPFC.
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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".