Bibliographic record
Abstract
When faces are blurred, presenting them at smaller sizes improves recognition. We term this unexpected advantage the blur paradox, which has been replicated in studies where face images are digitally blurred and scaled. To examine whether the blur paradox persists in physically realistic viewing conditions, we conducted two experiments using physical blur filters and varied viewing distances for size manipulation. First, we tested blurry celebrity face recognition at two viewing distances and found that recognition accuracy was significantly greater in the far condition than in the close condition. Second, we examined whether the blur paradox reflects gradual improvement across viewing distances or a sharp change in recognition performance at a particular distance. Across four viewing conditions, we found a significant main effect of viewing distance, with the highest recognition accuracy at the farthest viewing condition and lowest at the closest. Accuracy improved gradually, but nonlinearly, rather than showing an abrupt shift at a boundary. Exploration of participant demographics suggested a stronger effect among older participants (>50 years) and a weaker effect among left-handed participants. No significant sex differences were observed. These findings confirm the small-size advantage for recognition under blur and its persistence in physically realistic conditions, with accuracy improving gradually across a wide range of distances.
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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 | 0.001 | 0.009 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".