Testing the fluency account for truth judgments.
Bibliographic record
Abstract
Truthiness is the increased likelihood to rate claims true when claims are paired with conceptually related (but nonprobative) information (usually photos). The fluency account holds that photos facilitate the processing of conceptual information shared between the photos and the claims, increasing the ease of processing the claims relative to no-photo claims (an increase in relative fluency-a cue for familiarity and truth). In three experiments, we tested the fluency account using response time as a separate measure of fluency. In Experiment 1, we manipulated relative fluency by varying the proportion of photo-absent to photo-present claims. Photo-present claims were processed more quickly than photo-absent claims, but our relative fluency manipulation did not impact truthiness. In Experiment 2A, we varied the type of media presented with the claims: photo, audio, or photo + audio. We hypothesized that photo + audio media would better facilitate the processing of the claims and produce larger truthiness effects and faster response times. Instead, in Experiment 2A, we observed equal truthiness across photo, audio, and photo + audio claims and a trend toward faster response times when evaluating photo + audio claims compared to other media types. In Experiment 2B, we replicated the truthiness effect for audio and replicated the response time findings from Experiment 1. Consistent with the fluency account, related photos and audio similarly increase the speed of processing claims and similarly increase belief in claims. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.009 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".