Change blindness and eyewitness identification: Effects on accuracy and confidence
Bibliographic record
Abstract
Purpose Large changes in the visual field often go undetected, an effect referred to as change blindness. We investigated change blindness for an eyewitness event to examine its potential influence on identification accuracy and confidence. Methods Participants viewed a video that started with an innocent person walking through a building and finished with another person committing a theft. Participants subsequently attempted the thief's identification from a line‐up that contained either the thief or the innocent person from the video. Results Most viewers (64%) experienced change blindness and were unaware of the person change. Overall identification accuracy in the change blindness group was significantly lower than in the change detection group. The decrease in accuracy in the change blindness group was primarily driven by poor performance when the line‐up did not contain the thief. However, rather than misidentifying the innocent from the video, most witnesses who experienced change blindness misidentified a filler. Although change detection did not lead to a significant increase in correct identifications, it did lead to a significant increase in post‐identification confidence. Conclusions Our findings suggest that (1) although change blindness increases misidentifications, under these conditions witnesses primarily misidentify known innocents who are not at risk of wrongful conviction; and (2) confidence is inferred not only from recognition strength but also from how well observers believe the event was encoded.
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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 | 0.002 | 0.065 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".