Two's company, three's a crowd: Inverse other-race categorization advantage in a ternary categorization task
Bibliographic record
Abstract
When performing race categorization, people respond faster to other- compared to own-race faces (Valentine et al., 1992; Levin, 1996; Caldara et al., 2004). The so-called other-race categorization advantage (ORCA) is also accompanied by a category boundary shift resulting in participants needing less other-race signal (i.e.prototypical characteristics) to categorize a face as such (Benton and Skinner, 2015). However, a large majority of ORCA studies have relied on a binary (i.e., own- vs. other-) race categorization task. In this study, we examined whether the typical category boundary shift still occurs when participants have to classify morphed faces into one of three categories. For each of the two gender profiles (i.e. Male, Female), 108 stimuli were generated by morphing triads of face images of different races (i.e. 2 Black, 2 White, 2 Asian), with race prototypicality ranging from 2% to 86%. A total of 286 participants (94 Black [South-African], 96 White [Canadian], and 96 Asian [Chinese]) each completed 432 trials on online platforms (Prolific and Pavlovia). Categorical boundaries (i.e., the threshold at which a face is categorized as e.g., Black, with a 50% probability) were defined separately for each stimulus and participant race. Other-race thresholds were averaged on a participant basis (e.g., [White + Asian]/2 for Black participants). A 2 (stimulus race: own, other) x 3 (participant race) mixed ANOVA was carried. Results show a main effect of stimulus race which interacted with participant race. Strikingly, there was a generalized inverse-ORCA: that is, participants were globally more sensitive to own- vs. other-race information. However, the interaction revealed that this effect was largely driven by the Black subsample, and also marginally by the White subsample. The Asian subsample, on the other hand, showed the typical categorical boundary shift. This highlights the influence of task parameters on social cognition measures.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".