Early processing of unattended emotional faces increases the brain response to attended emotional expressions: an SSVEP study
Bibliographic record
Abstract
The brain has the ability to evaluate unattended social information, such as facial expressions, and reassign attentional resources to specific relevant features. Two neuronal mechanisms could account for such facial emotional processing: one slow and accurate system that can be measured around 170 ms and one fast and imprecise system that is triggered around 90 ms which could support early negative emotional processing for automatic/unattended and peripheral stimulation. Evidence that these mechanisms exist for positive affective processing is scarce. The present study investigated the neural correlates of unattended negative and positive emotional processing using the rapid presentation of unilateral and bilateral peripheral facial expressions. Hence, we measured the electrophysiological correlates of unattended fear, happy and neutral faces presented in the left and right hemifields of neurotypical individuals using a frequency tagging paradigm and electroencephalography. Frequency stimulations of 5.8 Hz and 11 Hz were chosen to induce Steady-State Visual Evoked Potential (SSVEP) occurring at 170 ms and 90 ms, respectively. The SSVEP amplitudes showed that unattended positive and negative information in the periphery was processed at early stages and increased the brain's response to attended salient emotional stimuli in posterior visual regions. These results suggest that emotional stimuli presented outside the attentional focus elicit increased brain activity, particularly in posterior regions which could be altered in disorders of social recognition.
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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 | 0.000 |
| 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".