The importance of decision bias for predicting eyewitness lineup choices: toward a Lineup Skills Test
Bibliographic record
Abstract
ᅟ: We report on research on individual-difference measures that could be used to assess the validity of eyewitness identification decisions. BACKGROUND: The predictive utility of face recognition tasks for eyewitness identification has received some attention from psychologists, but the previous research focused primarily on witnesses' likelihood of correctly choosing the culprit when present in a lineup. Far less discussed has been individual differences in witnesses' proclivity to choose from a lineup that does not contain the culprit. We designed a two-alternative non-forced-choice face recognition task (consisting of mini-lineup test pairs, half old/new and half new/new) to predict witnesses' proclivity to choose for a set of culprit-absent lineups associated with earlier-viewed crime videos. RESULTS: In two studies involving a total of 402 participants, proclivity to choose on new/new pairs predicted mistaken identifications on culprit-absent lineups, with r values averaging .43. The likelihood of choosing correctly on old/new pairs (a measure of face recognition skill) was only weakly predictive of correct identifications in culprit-present lineups (mean r of .22). CONCLUSIONS: Our findings could be the basis for further research aimed at developing a standardized measure of proclivity to choose that could be used, along with other measures, to weigh eyewitnesses' lineup identification decisions.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".