Evidence for dimensional representations and anticipatory dynamics in facial expression perception
Bibliographic record
Abstract
Abstract Expression recognition relies on the ability to distinguish subtle visual differences across a range of facial expressions. Here, we examine the neural representation of dynamic expressions as reflected by electroencephalography (EEG) data in human adults. We find that a wide range of expressions (i.e., 14 emotional and 10 conversational expressions) can be decoded from neural signals, and that their representational structure evinces the classic dimensions of valence and arousal. Critically, we recover, through EEG-based video reconstruction, dynamic representations whose content succeeds in capturing even fine differences across related expressions (e.g., happy-satiated versus schadenfreude). Further, time-resolved decoding reveals anticipatory dynamics that maximize accuracy before the occurrence of an apex expression in the visual stimulus. These results are validated against behavioral data, which yield static reconstructions consistent with their neural counterparts. Thus, our results shed light on the representational basis of expression recognition and serve to recover the dynamic content of visual experience.
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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 | 0.003 |
| 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) | 0.001 | 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".