Event‐Related Potentials to Facial Expressions Are Related to Stimulus‐Level Perceived Arousal and Valence
Bibliographic record
Abstract
Facial expressions provide critical details about social partners' inner states. We investigated whether event-related potentials (ERP) related to the visual processing of facial expressions are modulated by participants' perceived arousal and valence at the stimulus level. ERPs were recorded while participants (N = 80) categorized the gender of faces expressing fear, anger, happiness, and no emotion. Participants then viewed each face again and rated them on arousal and valence using 1-9 Likert scales. For each participant, ratings of each unique face were linked back to corresponding ERP trials. ERPs were analyzed at all time points and electrodes using hierarchical mass univariate statistics. Three different ANOVA models were employed: the original emotion model, and models with valence or arousal ratings as trial-level regressors. Results from models with ratings highly overlapped with the original model, although they were more temporally restricted. The N170 component was the most impacted by arousal and valence ratings, with four out of six emotion contrasts revealing significant valence or arousal interactions. Emotion effects on the P2 component were mostly unrelated to ratings. On the EPN component, only two contrasts related to both arousal and valence ratings. Thus, ERP emotion effects are related to participants' perceived arousal and valence of the stimuli, although this association depends on the contrast analyzed. These findings, their limitations, and generalizability are discussed in reference to existing theories and literature.
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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 | 0.001 | 0.005 |
| 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".