Successful sensitization of 2.5-year-olds to other-race faces through bimodal training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study investigated the potential for sensitizing 2.5-year-old Caucasian infants to other-race faces (Asian faces). In the domain of face perception, infants become less sensitive to facial distinctions of other-race faces through perceptual narrowing at the end of the first year of life. Nevertheless, infants around 12 months can regain their sensitivity to other-race faces. For instance, exposing them to a specific statistical distribution and employing the mechanisms of statistical learning is one way to enhance their discriminatory abilities towards other-race faces. Following this idea, we investigated if even older infants around 2.5 years can be sensitized to other-race faces. We trained the infants with a bimodal distribution of a morphed continuum of Asian female faces with faces closer to the endpoints presented most frequently. We assessed infants’ discrimination of Asian faces by measuring their looking times after the training phase. The 2.5-year-olds showed a difference in looking times after the training, indicating that the exposure to a bimodal frequency distribution led to a successful discrimination between Asian faces. These findings demonstrate that 2.5-year-olds can be sensitized to other-race faces by exposing them to a bimodal distribution of such faces, underlining the plasticity of face perception in childhood. • 2.5-year-olds show successful re-sensitization to other-race faces after undergoing a bimodal training. • Older infants use statistical learning to enhance sensitivity to distinguishing features, even after perceptual narrowing. • Our results indicate the plasticity of face perception in older infants and the possibility to modify the Other-Race-Effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it