Examining emotion discrimination in 7-month-old infants and adults using Fast Periodic Visual Stimulation (FPVS)
Bibliographic record
Abstract
The ability to discriminate facial expressions of emotion is important for human communication and interaction. When this ability develops is largely unknown, with the origins believed to lie in infancy. Behavioural and brain-based evidence suggests that infants are capable of differentiating positive and negative facial expressions (i.e., sad vs. happy, surprised vs. angry), however there is little research examining whether infants can make more fine-grained discriminations among negative facial expressions (e.g., fearful vs. angry). In the present paper, two experiments use a novel technique known as Fast Periodic Visual Stimulation (FPVS) to assess discrimination of facial expressions by adults (n = 33) and 7-month-old infants (n = 33). Adults discriminated facial expressions, but 7-month-old infants did not. Reasons why infants did not show a discrimination response are explored and the potential benefits of FPVS are discussed.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".