Yüz İfadelerinden Duygu Tanıma ve Çocukluk Çağı Travmaları ile İlişkisinin İncelenmesi
Bibliographic record
Abstract
The purpose of the study was to investigate the emotion recognition from faces in young adults and its linkwith childhood traumas by controlling the level of general psychological symptomatology and alexithymia through using FACES Turkish sample data set, which was used for the first time in the literature. The sample of the study was composed of 94 female (54,7%) and 78 male (45,3%) university students who were from different faculties at Hacettepe University and Middle East Technical University. In this study, “Childhood Trauma Questionnaire”, “Brief Symptom Inventory”, “20-item Toronto Alexithymia Scale”, “FACES Turkish Sample Data Set” and Demographic Information Form were administered to collect data. \nAccording to the examination of percentages of the recognition accuracy and the results of variance analyses which were conducted to answer the research questions, it was concluded that FACES Turkish Sample Data Set is a valid and useful tool for emotion recognition assessment from faces when the photos of middle-aged male and old female displaying anger are excluded from the data set.The results of hierarchical regression analyses indicated that childhood traumas significantly predict the recognition time of fear at positive direction when the level of general psychological symptomatology and alexithymia were controlled. In other words, it was found that young adults with childhood traumas looked longer to those faces to recognize the emotion of fear. Nevertheless, childhood traumas did not predict significantly the recognition accuracy and time of anger, disgust, sadness and happiness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".