Facial expressions modulate visual features utilization in unfamiliar face identification
Bibliographic record
Abstract
Flexible face identification requires extracting expression-independent visual information while leaving aside expression-dependant visual features. Most studies in the field suggest that the eye area is by far the most important visual feature for face identification (e.g. Butler et al., 2010; Schyns, Bonnar & Gosselin, 2002; Sekuler, Gaspar, Gold & Bennett, 2004). However, these studies were mostly done with stimuli showing only one (e.g. neutral) or two (e.g. neutral and happy) facial expressions. Here, we investigated the impact of facial expressions on the utilization of facial features in a facial identification task with unfamiliar faces (10 identities; 5 female). Each identity showed six different facial expressions (anger, disgust, fear, happy, neutral, sad). We used the Bubbles technique (Gosselin & Schyns, 2001) to reveal the diagnostic visual features in five non-overlapping spatial frequency bands. Twenty-five participants first learned to recognize the identities until their performance reached 95% correct for each facial expression. After reaching this performance criterion, they performed 1320 trials (220 for each facial expression) with bubblized stimuli. For each facial expression, the number of bubbles was adjusted on a trial-by-trial basis to maintain a correct identification rate of 55%. Overall, we closely replicate other studies (Caldara et al., 2005; Schyns, Bonnar & Gosselin, 2002). However, when each facial expression was analysed independently, the results show clear inter-expression differences. For neutral faces, the eyes are by far the most diagnostic features. However, for other facial expressions, participants showed a clear processing bias for expression-dependant facial features (e.g. the mouth for happy and disgust). In short, our data suggests that facial expression features are closely bound to identification in unfamiliar face recognition. Meeting abstract presented at VSS 2016
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 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) | 0.002 | 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".