Thin‐Slice Decisions Do Not Need Faces to be Predictive of Election Outcomes
Bibliographic record
Abstract
Rapid decisions about political candidates, made solely on the basis of candidate appearance, associate with real electoral outcomes. A prevailing interpretation is that these associations result from heuristic cognitive processing of cues from the face to yield a judgment about the candidate, processing that is shared by both voters and experimental participants. Here, we report findings suggesting that nonfacial aspects of a candidate's appearance are important cues for voter decision making. We asked participants to look at pairs of candidate images and decide (a) whom to vote for (SimVote), (b) who looks more physically threatening (Threat), and (c) who looks more competent to hold congressional office (Competence). When participants saw only the candidates' faces, there was no association between their decisions and electoral outcomes, except for Threat. Yet when participants saw the candidate images with the faces removed, there was a strong association between their decisions and voters' decisions, for all decision types. This suggests that the appearance‐related heuristics that some voters use to guide their decisions may include mental schemas for processing appearance cues other than those associated with facial features. Such schema‐based processing has implications for understanding the neurobiological system underlying thin‐slice decisions from appearance alone.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".