Impact of sustained lifetime exposure to a racially-heterogenous face-diet
Bibliographic record
Abstract
Face-diets of observers living in racially-homogeneous environments are predominantly composed of own-race faces resulting in a lack of experience with other-race faces (Sugden et al. 2014; Oruc et al. 2019). According to the contact hypothesis, this, in part, leads to a marked impairment in the ability to recognize other-race faces, termed the other-race effect. However, the contact hypothesis does not predict what impact a racially-heterogenous face-diet with plenty of exposure to multiple face races may have on face expertise. To complement and extend the contact hypothesis, we propose three new models: (1) the experience-limited, (2) the capacity-limited, and (3) the enhancement hypotheses for the role of exposure in face expertise. Based on the experience-limited account native-level face recognition can be achieved for multiple face races with sufficient experience. On the other hand, the capacity-limited account predicts exposure to multiple face races may impact face expertise detrimentally. Lastly, based on the enhancement account exposure to a racially-heterogenous face-diet may confer some advantages in face expertise. Here, in two experiments, we compared face recognition in a dual-exposure group (N = 20) with sustained high exposure to Caucasian and East Asian faces to two mono-exposure groups (Ns = 20) with sustained exposure to either Caucasian or East Asian faces only. We found native-like recognition performance in the dual-exposure group regarding face memory and face inversion effect for both Caucasian and East Asian faces. Our results showed neither an advantage, nor a disadvantage for racially-heterogenous face exposure, hence supporting the experience-limited account of face expertise. Consequently, we conclude that exposure to multiple face races is not detrimental to face recognition ability. To achieve native-level face expertise, a racially-homogenous face diet is not a necessity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".