Eyewitness identification across the life span: A meta-analysis of age differences.
Bibliographic record
Abstract
Lineup identifications are often a critical component of criminal investigations. Over the past 35 years, researchers have been conducting empirical studies to assess the impact of witness age on identification accuracy. A previous meta-analysis indicated that children are less likely than adults to correctly reject a lineup that does not contain the culprit, but children 5 years and older are as likely as adults to make a correct identification if the culprit is in the lineup (Pozzulo & Lindsay, 1998). We report an updated meta-analysis of age differences in eyewitness identification, summarizing data from 20,244 participants across 91 studies. Contrary to extant reviews, we adopt a life span approach and examine witnesses from early childhood to late adulthood. Children's increased tendency to erroneously select a culprit-absent lineup member was replicated. Children were also less likely than young adults to correctly identify the culprit. Group data from culprit-absent and culprit-present lineups were used to produce signal detection measures, which indicated young adults were better able than children to discriminate between guilty and innocent suspects. A strikingly similar pattern emerged for older adults, who had even stronger deficits in discriminability than children, relative to adults. Although identifications by young adults were the most reliable, identifications by all witnesses had probative value.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".