The importance of decision bias for predicting eyewitness lineup choices: toward a Lineup Skills Test
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it