Perceiving happiness in an intergroup context: The role of race and attention to the eyes in differentiating between true and false smiles.
Bibliographic record
Abstract
The present research comprises six experiments that investigated racial biases in the perception of positive emotional expressions. In an initial study, we demonstrated that White participants distinguished more in their happiness ratings of Duchenne ("true") and non-Duchenne ("false") smiles on White compared with Black faces (Experiment 1). In a subsequent study we replicated this effect using a different set of stimuli and non-Black participants (Experiment 2). As predicted, this bias was not demonstrated by Black participants, who did not significantly differ in happiness ratings between smile types on White and Black faces (Experiment 3). Furthermore, in addition to happiness ratings, we demonstrated that non-Black participants were also more accurate when categorizing true versus false expressions on White compared with Black faces (Experiment 4). The final two studies provided evidence for the mediating role of attention to the eyes in intergroup emotion identification. In particular, eye tracking data indicated that White participants spent more time attending to the eyes of White than Black faces and that attention to the eyes predicted biases in happiness ratings between true and false smiles on White and Black faces (Experiment 5). Furthermore, an experimental manipulation focusing participants on the eyes of targets eliminated the effects of target race or perceptions of happiness (Experiment 6). Together, the findings provide novel evidence for racial biases in the identification of positive emotions and highlight the critical role of visual attention in this process. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".