Recognizing, discriminating, and labeling emotional expressions in a free-sorting task: A developmental story.
Bibliographic record
Abstract
Recognizing emotional expressions across different people and discriminating between them are important social skills. We examined their development using a novel free-sorting task in which children (aged 5 to 10) and adults sorted 20 faces (posing sadness, anger, fear, and disgust) into piles such that all faces in each pile were feeling the same. Participants could make as many or few piles (emotion categories) as they liked and then labeled each pile. There were no age-related changes in the number of piles made. Children made more confusion errors (two emotions in the same pile) than adults, a pattern that decreased with age. Errors were not random, but disproportionately involved placing fearful faces into piles labeled sad and disgusted faces into piles labeled angry-especially among children who did not produce fear and disgust labels, respectively. Our findings are consistent with differentiation and constructivist models of the development of emotion perception. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".