Reverse correlating trustworthy faces in young and older adults
Bibliographic record
Abstract
Little is known about how older persons determine if someone deserves their trust or not based on their facial appearance, a process referred to as "facial trustworthiness."In the past few years, Todorov and colleagues have argued that, in young adults, trustworthiness judgments are an extension of emotional judgments, and therefore, that trust judgments are made based on a continuum between anger and happiness (Todorov, 2008; Engell et al., 2010). Evidence from the literature on emotion processing suggest that older adults tend to be less efficient than younger adults in the recognition of negative facial expressions (Calder et al., 2003; Firestone et al., 2007; Ruffman et al., 2008; Chaby and Narme, 2009). Based on Todorov';s theory and the fact that older adults seem to be less efficient than younger adults in identifying emotional expressions, one could expect that older individuals would have different representations of trustworthy faces and that they would use different cues than younger adults in order to make such judgments. We verified this hypothesis using a variation of Mangini and Biederman's (2004) reverse correlation method in order to test and compare classification images resulting from trustworthiness (in the context of money investment), from happiness, and from anger judgments in two groups of participants: young adults and older healthy adults. Our results show that for elderly participants, both happy and angry representations are correlated with trustworthiness judgments. However, in young adults, trustworthiness judgments are mainly correlated with happiness representations. These results suggest that young and older adults differ in their way of judging trustworthiness.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".