Are multiple-trial experiments appropriate for eyewitness identification studies? Accuracy, choosing, and confidence across trials
Why this work is in the frame
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Bibliographic record
Abstract
Eyewitness identification experiments typically involve a single trial: A participant views an event and subsequently makes a lineup decision. As compared to this single-trial paradigm, multiple-trial designs are more efficient, but significantly reduce ecological validity and may affect the strategies that participants use to make lineup decisions. We examined the effects of a number of forensically relevant variables (i.e., memory strength, type of disguise, degree of disguise, and lineup type) on eyewitness accuracy, choosing, and confidence across 12 target-present and 12 target-absent lineup trials (N = 349; 8,376 lineup decisions). The rates of correct rejections and choosing (across both target-present and target-absent lineups) did not vary across the 24 trials, as reflected by main effects or interactions with trial number. Trial number had a significant but trivial quadratic effect on correct identifications (OR = 0.99) and interacted significantly, but again trivially, with disguise type (OR = 1.00). Trial number did not significantly influence participants' confidence in correct identifications, confidence in correct rejections, or confidence in target-absent selections. Thus, multiple-trial designs appear to have minimal effects on eyewitness accuracy, choosing, and confidence. Researchers should thus consider using multiple-trial designs for conducting eyewitness identification experiments.
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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 | 0.028 | 0.208 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it