Preschoolers’ mapping of in-group social groups versus out-group social groups to morphed faces
Bibliographic record
Abstract
Past research has revealed children as young as six years old and adults show tendencies of dehumanization. For example, by age 5 children perceive ambiguous doll-human face morphs as being “less human” if they thought the faces belonged to an out-group (McLoughlin et al., 2018). However, there remains a gap in understanding whether and when this phenomenon starts earlier in development. The current study aimed to explore whether children as young as three years old display dehumanization tendencies towards out-group members, for which group membership is defined in different ways. Specifically, we will examine preschoolers' attribution of humanness to faces described as their in-group vs out-group. We explore this question in 3-to-5 ½-year-old children as past work suggests that the youngest age this phenomenon has been observed is in 6-year-olds (McLoughlin et al., 2018), suggesting that children may already attribute fewer human qualities to people perceived as their out-group in contrast to their in-group across different types of group membership (i.e. minimal group, language, preference, geographical location). Therefore, if the tendency to dehumanize is already present at that young age, we could observe a tendency to dehumanize developing earlier in development. To test this possibility, English monolingual 3- to 5-year-olds will hear descriptions that reflect four different types of group memberships (same/different minimal group, language identity, geographical location, food preference). For each of the four blocks (i.e. each block belongs to one of the four group memberships), on alternating trials, the description will be of an in-group member (e.g. “This person is also from Canada”) or an out-group member (e.g. “This person is from a far away place called Daxo”). Following each description, children will see a face-morph pairing that differ in their degree of humanness. In low contrast trials, one face will be only slightly more human (~5 to 10% difference in morph, as defined by FantaMorph software, Version 5; Abrosoft Co), whereas in high contrast trials, one face will be much more human (~15 to 20% difference in morph). Children will be asked to pick which of the two face-morphs best fit the description they just heard. As there has been no previous work, to our knowledge, that examines dehumanization in children in this age group; we do not have strong predictions for what might occur. However, we generally predict that children will pick the morph faces that are less human-like after hearing a description of an out-group member than after hearing a description of an in-group member.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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".