Bibliographic record
Abstract
When people evaluate claims they often rely on what comedian Stephen Colbert calls truthiness, judging claims using subjective feelings of truth, rather than drawing on facts. Over seven experiments I examined how nonprobative photos can manufacture truthiness in just a few seconds. I found that a quick exposure to a photo that relates to, but does not provide any probative evidence about the accuracy of claims can systematically bias people to conclude claims are true. In Experiments 1A and 1B, people saw familiar and unfamiliar celebrity names and, for each, quickly responded "true" or "false" to the claim "This famous person is alive" or (between subjects) "This famous person is dead." Within subjects, some names appeared with a photo of the celebrity engaged in his/her profession whereas other names appeared alone. For unfamiliar celebrity names, photos increased the likelihood that subjects judged the claim to be true. Moreover, the same photos inflated the truth of "Alive" and "Dead" claims, suggesting that photos did not produce an "alive bias," but a "truth bias." Experiment 2 showed that photos and verbal information similarly inflated truthiness, suggesting that the effect is not peculiar to photographs per se. Experiment 3 demonstrated that nonprobative photos can also enhance the truthiness of general knowledge claims (Giraffes are the only mammals that cannot jump). In Experiments 4-6 I examined boundary conditions for truthiness. I found that the semantic relationship between the photo and claim mattered. Experiment 4 showed that in a within-subject design, related photos produced truthiness, but unrelated photos acted just like the no photo condition. But unrelated photos were not always benign, Experiment 5 showed that their effects depended on experimental context. In a mixed design, related photos produced truthiness and unrelated photos produced falsiness. Although the effect of related photos was robust across materials and variation in experimental context, when I used a fully between-subjects design in Experiment 6, the effect of photos (related and unrelated) was eliminated. These effects add to a growing literature on how nonprobative information can influence people’s decisions and suggest that nonprobative photographs do more than simply decorate, they can rapidly manufacture feelings of truth. As with many effects in the cognitive psychology literature, the photo-truthiness effect depends on the way in which people process and interpret photos when evaluating the truth of claims.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".