Reflector variables in augmented reality lineups: Assessing eyewitness identification reliability in children and adults with confidence, response time, and proximity to the lineup
Bibliographic record
Abstract
Attending to the behaviors of eyewitnesses at police lineups could help to determine whether an eyewitness identification is accurate or mistaken. Eyewitness identification decision processes were explored using augmented reality holograms. Children (n = 143; Mage = 10.79, SD = 1.12 years) and adults (n = 152; Mage = 22.12, SD = 7.47 years) viewed staged crime videos and made identification decisions from sequential lineups. The lineups were presented in augmented reality. Children were less accurate than adults on the lineup task. For adults, fast response times and high post-identification confidence ratings were both reflective of identification accuracy. Fast response times were also reflective of accuracy for children; however, children's confidence ratings did not reflect the likely accuracy of their identifications. A new additional measure, the witness' proximity to the augmented reality lineup, revealed that children who made mistaken identifications moved closer to the lineup than children who correctly identified the person from the crime video. Adults who moved any distance towards the lineup were less accurate than adults who did not move at all, but beyond that, adults' proximity to the lineup was not reflective of accuracy. The findings give further evidence that behavioral indicators of deliberation and information-seeking by eyewitnesses are signals of low lineup identification reliability. The findings also suggest that when assessing the reliability of children's lineup identifications, behavioral measures are more useful than metacognitive reports.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".