Interactions between the perception of age and ethnicity in faces: an event-related potential study
Bibliographic record
Abstract
Face perception models propose that different facial attributes are processed by anatomically distinct neural pathways that partially overlap. Whether these attributes interact functionally is an open question. Our goal was to determine if there are interactions between age and ethnicity processing and, if so, at what temporal epoch these interactions are evident. We monitored event-related potentials on electroencephalography while subjects categorized faces by age or ethnicity in two conditions: a baseline in which the other of these two properties not being categorized was held constant and an interference condition in which it also varied, as modelled after the Garner interference paradigm. We found that, when participants were categorizing faces by age, variations in ethnicity increased the amplitude of the right face-selective N170 component. When subjects were categorizing faces by ethnicity, variations in age did not alter the N170. We concluded that there is an asymmetric pattern of influence between age and ethnicity on early face-specific stages of visual processing, which has parallels with behavioural evidence of asymmetric interactions between identity and expression processing of faces.
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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 | 0.002 |
| 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 | 0.000 |
| 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".