Bubblizing social face perception
Bibliographic record
Abstract
When asked to judge an unknown face on social traits such as trustworthiness and dominance, a high level of agreement is found among people, suggesting that some visual information in faces correlates with these judgements (Oosterhof & Todorov, 2008). We used the Bubbles technique (Gosselin & Schyns, 2001) to reveal the visual information used to judge the trustworthiness (Exp. 1) and dominance (Exp. 2) of 300 faces. Participants (N=50 for each experiment) were presented with either bubblized faces (phase 1) or fully visible faces (phase 2) and were asked to judge the level of trustworthiness or of dominance of the stimuli using a nine-level Likert scale. The number of bubbles was kept constant (i.e., 65 bubbles) across participants and trials. The judgements obtained for each fully visible face were considered "accurate" for a given participant. The judgements obtained with the same bubblized faces were possibly influenced by the available information. Thus, the following analyses allowed us to verify how judgements were influenced by the visual information available for the task. For each experiment, a classification image showing which visual information favoured the percept of trust or of dominance was computed by performing a multiple linear regression on the bubbles' location and on the difference (i.e., transformed into z-scores) between the bubbles judgements and the accurate judgements. For trust judgements, the areas used were: the eyes in the spatial frequency (SF) bands ranging from 21 to 84 cycles per face (cpf); the mouth in the SF bands ranging from 42 to 84 cpf and from 10 to 21 cpf; and most of the face in the two lowest SF bands. The dominance judgement was based mostly on the utilization of the eyebrows area in mid-to-high SFs. Interestingly, these two judgements were based on orthogonal visual information. Meeting abstract presented at VSS 2013
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".