Effects of early institutionalization on the development of emotion processing: a case for <i>relative</i> sparing?
Bibliographic record
Abstract
We tested the capacity to perceive visual expressions of emotion, and to use those expressions as guides to social decisions, in three groups of 8- to 10-year-old Romanian children: children abandoned to institutions then randomly assigned to remain in 'care as usual' (institutional care); children abandoned to institutions then randomly assigned to a foster care intervention; and community children who had never been institutionalized. Experiment 1 examined children's recognition of happy, sad, fearful, and angry facial expressions that varied in intensity. Children assigned to institutional care had higher thresholds for identifying happy expressions than foster care or community children, but did not differ in their thresholds for identifying the other facial expressions. Moreover, the error rates of the three groups of children were the same for all of the facial expressions. Experiment 2 examined children's ability to use facial expressions of emotion to guide social decisions about whom to befriend and whom to help. Children assigned to institutional care were less accurate than foster care or community children at deciding whom to befriend; however, the groups did not differ in their ability to decide whom to help. Overall, although there were group differences in some abilities, all three groups of children performed well across tasks. The results are discussed in the context of theoretical accounts of the development of emotion processing.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".