The effect of pre-event instructions on eyewitness identification
Bibliographic record
Abstract
Research on eyewitness identification often involves exposing participants to a simulated crime and later testing memory using a lineup. We conducted a systematic review showing that pre-event instructions, instructions given before event exposure, are rarely reported and those that are reported vary in the extent to which they warn participants about the nature of the event or tasks. At odds with the experience of actual witnesses, some studies use pre-event instructions explicitly warning participants of the upcoming crime and lineup task. Both the basic and applied literature provide reason to believe that pre-event instructions may affect eyewitness identification performance. In the current experiment, we tested the impact of pre-event instructions on lineup identification decisions and confidence. Participants received non-specific pre-event instructions (i.e., "watch this video") or eyewitness pre-event instructions (i.e., "watch this crime video, you'll complete a lineup later") and completed a culprit-absent or -present lineup. We found no support for the hypothesis that participants who receive eyewitness pre-event instructions have higher discriminability than participants who receive non-specific pre-event instructions. Additionally, confidence-accuracy calibration was not significantly different between conditions. However, participants in the eyewitness condition were more likely to see the event as a crime and to make an identification than participants in the non-specific condition. Implications for conducting and interpreting eyewitness identification research and the basic research on instructions and attention are discussed.
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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 | 0.014 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".