Bibliographic record
Abstract
Most research investigating children’s recognition of facial expressions has involved static and isolated face stimuli. However, in the real world facial expressions are dynamic and viewed in the context of body movements, background scenes, etc. We examined the influence of bodies on children’s and adults’ perception of emotional expressions using both static and dynamic stimuli. Children’s recognition of statically presented facial expressions is influenced by the accompanying postural expression (Mondloch, 2012; Mondloch, Horner, & Mian, 2012). However, recognition of dynamically presented facial expressions is not influenced by postural expressions (Nelson & Russell, 2011). That postural information influenced children’s recognition when stimuli were static – but not dynamic – is surprising. To resolve these discrepant findings we examined the extent to which attention allocation is influenced by a) the expression cues available and b) whether the stimulus is static or dynamic. Children (4-9 years) and adults viewed four video clips in three conditions: face-only, body-only (i.e. face blurred), and face-body. Stimuli were presented on a Tobii eye tracker and participants freely labeled each video. For dynamic stimuli in the face-body condition, both groups looked almost exclusively to the face (>88% of the time for all emotions). In the body-only condition, both groups showed a reduction in looking to the face, with an especially large drop observed in children (from 90% to 50%). For static stimuli, adults looked less at the face both in the face-body condition (71%) and in the body-only condition (32%) than they did for dynamic stimuli. Children are currently being tested. These results indicate that for adults – and perhaps for children – attentional allocation varies for static versus dynamic emotional expressions. These data may explain why dynamic bodies do not influence emotion recognition whereas static bodies do, providing a more complete understanding of how emotion recognition develops in childhood. Meeting abstract presented at VSS 2013
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".