Facial and Vocal Emotion Recognition in Adolescence: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The ability to recognize emotion is important to wellbeing and building relationships with others, making this skill important in adolescence. Research investigating adolescents’ ability to recognize facial and vocal emotion expressions has reported differing conclusions about the pattern of emotion recognition across this developmental period. This systematic review aimed to clarify the pattern of recognition for facial and vocal emotion expressions, and the relationship of performance to different task and emotion expression characteristics. A comprehensive and systematic search of the literature was conducted using six databases. To be eligible for inclusion, studies had to report data for adolescents between 11 and 18 years of age and measure accuracy of the recognition of emotion cues in either the face or voice. A total of 2333 studies were identified and 47 met inclusion criteria. The majority of studies focused on facial emotion recognition. Overall, early, mid-, and late-adolescents showed a similar pattern of recognition for both facial and vocal emotion expressions with the exception of Sad facial expressions. Sex of the participant also had minimal impact on the overall recognition of different emotions. However, analysis showed considerable variability according to task and emotion expression characteristics. Future research needs to increase focus on recognition of complex emotions, and low-intensity emotion expressions as well as the influence of the inclusion of Neutral as a response option.
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.
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 | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.014 |
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 it