Happy or sad? The effects of age and face race on expression aftereffects
Bibliographic record
Abstract
Emotional facial expressions provide a useful indicator of others' affective states. Adults perceive blends of facial expressions categorically (i.e., separated by a clear boundary) and dynamically; an ambiguous expression is perceived as sad following adaptation to a happy expression, but as happy following adaptation to sad. These expression aftereffects are strong when the adapting and probe expressions share the same facial identity, but are mitigated when they are posed by different identities, indicating that adults' perception of facial expression is integrated with identity (Fox & Barton, 2007). In Experiment 1, we extended these findings by comparing categorical boundaries and expression aftereffects in adults versus children (n = 20 per group). We created two morphed continua of facial expressions (happy-sad, fear-anger) in which contiguous faces differed by 5%. Participants classified each face in one of the two continua in three blocks of trials: no-adaptation, same-identity adaptation and different-identity adaptation. For the happy-sad continuum, both 5- and 7-year-olds showed adult-like category boundaries. All groups showed significant aftereffects in the same-identity condition, but the effects were larger for 5-year-olds (p p p p [[lt]].01), indicating that expression and identity may be less well integrated for other-race faces than own-race faces.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".