Judging faces on trustworthiness and emotions
Bibliographic record
Abstract
Determining whether a person is trustworthy or not is a task of importance on a daily basis. This type of judgment based on a face helps determine the course of our social interactions and prevents people from encountering dangers. Oosterhof and Todorov (2008) have shown that judging a face on its level of trustworthiness relies on two principal dimensions: dominance and valence. Judgments along these two dimensions rely in turn on certain characteristics of a face such as inner eyebrows, cheekbones, chins and nose sellion (Todorov et al., 2008). Todorov (2008) has argued that trustworthiness judgments are an extension of emotional judgments, and that a face judged as trustworthy would be judged as happier than a face judged as untrustworthy, which in turn would be judged as angrier. However, this theory is mainly based on studies investigating explicit trustworthiness judgments, which could be driven by subjectivity. We sought to investigate this theory by using a reverse correlation technique that would help reveal and compare the implicit representations of four categories of judgments based on a face: anger, fear, happiness and trustworthiness. Our results show that the region of the mouth is of particular importance in the representation of happiness and trustworthiness judgments, whereas the region of the mouth and the region of the eyes are important in the representations of anger and fear. Results are discussed in terms of comparisons between trustworthiness judgments and emotional judgments. Meeting abstract presented at VSS 2012
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".