Dynamic and static faces: Electrophysiological responses to emotion onsets, offsets, and non-moving stimuli
Bibliographic record
Abstract
In real life, facial expressions are fleeting occurrences that appear suddenly, like a burst of happiness or flash of anger, and then, just as quickly, the expression vanishes from the face. What are the brain mechanisms that allow us to discern the rapid onset and offset of facial expressions quickly and effortlessly? In this study, we examine the neural correlates of dynamic facial expressions using event-related potentials (ERPs). Participants were presented with happy, angry or neutral faces while EEG was recorded from 36 scalp electrodes. In the expression onset condition, a neutral face was presented for 500 ms, immediately followed by either a happy or angry face for 500 ms. In the expression offset condition, the happy or angry face was shown for 500 ms, immediately followed by a face with a neutral expression for 500 ms. The onset and offset conditions were compared to a static condition in which a single happy, angry and neutral face was shown for 500 ms. The EEG data showed that in the right posterior scalp sites, the onset of the happy or angry expression elicited a larger potential than their static versions suggesting that dynamic faces are more salient than static images. Moreover, the direction of the dynamics appears to be critical where the onset expressions produced larger brain potentials than offset expressions. These findings indicate that observers are more sensitive to the dynamic expressions than static expressions. However, the direction of the facial dynamics also seems important where the sudden appearance of a facial expression elicited more brain activity than its abrupt disappearance.
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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) | 0.003 | 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".