The discrimination of expressions in facial movements by infants: A study with point-light displays
Bibliographic record
Abstract
Perceiving facial expressions is an essential ability for infants. Although previous studies indicated that infants could perceive emotion from expressive facial movements, the developmental change of this ability remains largely unknown. To exclusively examine infants’ processing of facial movements, we used point-light displays (PLDs) to present emotionally expressive facial movements. Specifically, we used a habituation and visual paired comparison (VPC) paradigm to investigate whether 3-, 6-, and 9-month-olds could discriminate happy versus fear PLDs after being habituated with a happy PLD (happy-habituation condition) or a fear PLD (fear-habituation condition). Three-month-olds discriminated between the happy and fear PLDs in both the happy- and fear-habituation conditions. Six- and nine-month-olds showed discrimination only in the happy- but not the fear-habituation condition. These results indicated a developmental change in processing expressive facial movements. Younger infants tend to process low-level motion signals regardless of the depicted emotions, and older infants tend to process expressions, which emerges in familiar facial expressions (i.e., happy). Additional individual difference analysis and eye movement analysis supported this conclusion. In Experiment 2, we concluded that the findings of Experiment 1 were not due to a spontaneous preference for fear PLDs. Using inverted PLDs, Experiment 3 further suggested that 3-month-olds have already perceived PLDs as face-like stimuli.
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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 | 0.002 |
| 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 | 0.001 |
| 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".