The development of emotion perception strategies in children.
Bibliographic record
Abstract
Introduction. Typically developing adults use a template-matching strategy when perceiving emotional facial expressions (Skinner & Benton, 2010). Tolerance of expression exaggeration allows a test of template matching: an extremely exaggerated expression would no longer match the stored template, but would still agree with a rule-based emotion perception strategy (Rutherford & McIntosh, 2007; Walsh, Vida, and Rutherford, 2014). The current study examines the emotion perception strategies of children between 6 and 15 years, to examine when the template matching strategy develops. Methods. Participants completed two tasks. In the Emotion task participants viewed pairs of happy or sad faces, blocked by emotion, with varying levels of exaggeration. They were asked to select the face that looked closest to how a happy (or sad) person would really look in real life. In the Realism task, participants saw the same stimuli but were asked to pick the most realistic face. Results. Using proportion of trials on which the more exaggerated face was chosen as the dependent variable, performance differed across age groups for the Emotions task but not the Realism task. A Mann-Whitney revealed that the youngest age group was more likely to select the exaggerated faces (Mdn=0.85) compared to the oldest age group (Mdn=0.125), U = 22.50, p = 0.024. There were no significant differences for the Realism task, χ2(2) = 1.28, p = 0.527. Similiarly, for the Emotions Task, results revealed a significant negative correlation between age and proportion of exaggerated faces selected, (r = -0.350, n = 34, p =.042). Tolerance for exaggerated faces decreases with age in the emotion perception task. This correlation was not significant for the Realism task (r = -.141, n = 34, p = .427). These results suggest that the use of a template-based strategy increases between 6 and 15 years of age. Meeting abstract presented at VSS 2018
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".