Eyewitness identification performance on showups improves with an additional-opportunities instruction: Evidence for present–absent criteria discrepancy.
Bibliographic record
Abstract
We tested the proposition that when eyewitnesses find it difficult to recognize a suspect (as in a culprit-absent showup), eyewitnesses accept a weaker match to memory for making an identification. We tie this proposition to the basic recognition memory literature, which shows people use lower decision criteria when recognition is made difficult so as to not miss their chance of getting a hit on the target. We randomly assigned participant-witnesses (N = 610) to a condition in which they were told that if they did not believe the suspect was the culprit, they would have additional opportunities to make an identification later (additional-opportunities instruction). We fully crossed this instruction with the standard admonition (i.e., the culprit may or may not be present) and with the presence or absence of the culprit in a showup identification procedure. The standard admonition had no impact on eyewitness decision-making; however, the additional-opportunities instruction reduced innocent-suspect identifications (from 33% to 15%) to a greater extent than culprit identifications (57% to 51%). The additional-opportunities instruction yielded a better tradeoff between culprit and innocent-suspect identifications as indicated by binary logistic regression and receiver operator characteristic (ROC) analyses. (PsycINFO Database Record
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".