Neural correlates of own- and other-race face recognition in preschoolers: A functional near-infrared spectroscopy (fNIRS) study
Bibliographic record
Abstract
Previous studies revealed a neural other-race effect (NORE) paralleling the behavioral other-race effect, suggesting that adults asymmetrical experience with own- and other-race faces have a direct impact not only on their behavior but also on neural responses. However, the developmental origin of the neural other-race effect is still unknown. The present study used the functional Near-infrared Spectroscopy (fNIRS) methodology to investigate the neural correlates of preschoolers own- and other-race face processing. An old-new paradigm was used to assess preschoolers recognition ability of own- and other-race faces (N=67, Age: 4.08 to 6.50 Years). FNIRS data revealed that own-race faces elicited significantly greater [oxy-Hb] changes than other-race faces in the left middle frontal gyrus (left MFG, BA10, 46) and the left middle occipital gyrus (left MOG, V2). The [oxy-Hb] activity differences between own- and other-race faces, or the NORE was significantly positively correlated with age in the left MFG, but negatively correlated with age in the left MOG. Moreover, these areas had strong functional connectivity with a large swath of the cortical regions in terms of the NORE. These results taken together suggest that similar to school aged children and adults, preschoolers devote different amounts of neural resources to processing own- and other-race faces. But the size of their neural other-race effect and associated functional regional connectivity undergo developmental changes. Meeting abstract presented at VSS 2014
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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 | 0.001 | 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".