Decoding the representational dynamics of emotion concepts and categories during facial emotion perception
Bibliographic record
Abstract
Perceiving specific emotions from others' faces is a crucial ability for flexible and adaptive interaction, but the role of emotion concepts and categories in this perception has been controversial. The present study aims to investigate the precise time course of emotion concepts and categories involved in facial emotion perception. We conducted a behavioral task of conceptual similarity rating for emotional words, and emotion categorization tasks for emotional words and faces while recording electroencephalographic signals. We also performed a representational similarity analysis to assess the degree of correspondence between the representations of emotion concepts and categories with emotional faces over time. The results showed that the behavioral representations of emotion categories and concepts were successively correlated with the neural representations of the late processing stage for emotional faces at ~ 600-800 ms and ~800-1,000 ms, respectively. Furthermore, the representation of visual features of emotional faces was correlated with the neural representation of the early processing stage for faces (120-160 ms). Together, these results suggest that there is a temporal hierarchy in facial emotion perception that proceeds from visual to emotionally categorial to conceptual feature processing, providing electrophysiological evidence in support of basic emotion theory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".