An examination of the cognitive processes related to eyewitness lineup decisions
Bibliographic record
Abstract
Given the magnitude of errors in the criminal justice system, it is vital to increase our capacity to predict when an eyewitness is likely to be accurate. The aim of this work was to examine cognitive processes important for correct lineup responses and to develop a theoretically-driven model of the relative strength of these processes and the interactions between them for predicting the likelihood of an accurate lineup decision. We used sleep to manipulate memory strength and assessed decision process objectively, using eye tracking, and subjectively, using a questionnaire. We then modelled the influence of memory strength and decision process on correct identifications in a target-present lineup (Experiment 1) and correct rejections in a target-absent lineup (Experiment 2). Our subjective measure of decision process was the only predictor of correct identifications. Memory strength and decision process predicted the likelihood of correct rejections, and did so largely independently from one another, but the subjective measure was the stronger predictor. Combining the data from both experiments suggested that decision processes mediate the relationship between memory strength and identification accuracy. These results can inform theories of how cognitive processes interact to influence lineup 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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".