Emotion Recognition in Adults With a History of Childhood Maltreatment: A Systematic Review
Bibliographic record
Abstract
Child maltreatment has many well-documented lasting effects on children. Among its consequences, it affects children's recognition of emotions. More and more studies are recognizing the lasting effect that a history of maltreatment can have on emotion recognition. A systematic literature review was conducted to better understand this relationship. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was used and four databases were searched, MEDLINE/PubMed, PsycINFO, EMBASE, and FRANCIS, using three cross-referenced key words: child abuse, emotion recognition, and adults. The search process identified 23 studies that met the inclusion criteria. The review highlights the wide variety of measures used to assess child maltreatment as well as the different protocols used to measure emotion recognition. The results indicate that adults with a history of childhood maltreatment show a differentiated reaction to happiness, anger, and fear. Happiness is less detected, whereas negative emotions are recognized more rapidly and at a lower intensity compared to adults not exposed to such traumatic events. Emotion recognition is also related to greater brain activation for the maltreated group. However, the results are less consistent for adults who also have a diagnosis of mental health problems. The systematic review found that maltreatment affects the perception of emotions expressed on both adult and child faces. However, more research is needed to better understand how a history of maltreatment is related to adults' perception of children's emotions.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".