Influence of confidence inflation and explanations for changes in confidence on evaluations of eyewitness identification accuracy
Bibliographic record
Abstract
Guidelines for conducting police line‐ups typically recommend immediate assessment of eyewitness confidence following identification. This confidence level can presumably be used to estimate accuracy even in the presence of subsequently inflated confidence. In this experiment, we examined students' perceptions of immediate and inflated confidence and whether their reliance on confidence varies as a function of the explanations given by the eyewitness for her inflated confidence. Each of 126 university students viewed one of five versions of a videotaped officer–eyewitness interaction depicting an eyewitness identification and follow‐up interview in which the eyewitness gave a (1) high or (2) moderate level of confidence or inflated her confidence and gave a (3) confidence epiphany, (4) memory contamination, or (5) no explanation for the inflation. The memory contamination and confidence epiphany explanations led to lower ratings of identification accuracy as compared to the high‐confidence control condition, supporting the immediate confidence recommendation but in some ways contradicting previous research on this issue. The results suggest the need for further research to understand the conditions under which confidence inflation influences juror evaluations of eyewitness identification.
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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 | 0.006 | 0.102 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 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".