The McGill Diverse Face Database: 92 Complex Mental States Across Socially Perceived Racial Categories
Bibliographic record
Abstract
Theory of Mind (ToM)—the ability to infer others’ mental states—is fundamental to social cognition. Social categorization, the grouping of individuals into in-group or out-group categories, shapes these inferences. These processes co-occur during facial perception, with recent research suggesting both shared neurocognitive mechanisms and modulation of ToM by social category cues. However, existing tools for studying the impact of racial diversity on social cognition are limited: some databases prioritize racial representation but restrict stimuli to the six basic emotions, while others broaden mental state diversity but lack diversity in social appearance. Here we introduce the McGill Diverse Face Database, a validated set of 1,286 images of 14 actors from socially perceived racial categories portraying 92 complex mental states. Validation included three experiments: (1) a four-alternative forced-choice task assessing recognition accuracy, (2) a “point-and-click” task rating valence and arousal in a two-dimensional affective space, and (3) a trait-rating task evaluating perceived actor characteristics. Participants accurately identified mental states across categories (77 % of stimuli). Mean valence–arousal ratings revealed a non-linear one-dimensional manifold structure that correlated with behavioral measures. An interactive online visualization allows users to explore this “Theory of Mind manifold” (https://hctor99.github.io/TheoryofMindManifold/). By integrating social-category diversity with complex emotional expression, this database provides a new resource for studying how socially perceived group membership shapes the perception and inference of mental states.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".