Reasons to Doubt the Reliability of Eyewitness Memory: Commentary on Wixted, Mickes, and Fisher (2018)
Bibliographic record
Abstract
Wixted, Mickes, and Fisher (this issue) take issue with the common trope that eyewitness memory is inherently unreliable. They draw on a large body of mock-crime research and a small number of field studies, which indicate that high-confidence eyewitness reports are usually accurate, at least when memory is uncontaminated and suitable interviewing procedures are used. We agree with the thrust of Wixted et al.'s argument and welcome their invitation to confront the mass underselling of eyewitnesses' potential reliability. Nevertheless, we argue that there is a comparable risk of overselling eyewitnesses' reliability. Wixted et al.'s reasoning implies that near-pristine conditions or uncontaminated memories are normative, but there are at least two good reasons to doubt this. First, psychological science does not yet offer a good understanding of how often and when eyewitness interviews might deviate from best practice in ways that compromise the accuracy of witnesses' reports. Second, witnesses may frequently be exposed to preinterview influences that could corrupt reports obtained in best-practice interviews.
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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 | 0.022 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.087 | 0.085 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".