Impact of disguise on identification decisions and confidence with simultaneous and sequential lineups.
Post-publication record
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Bibliographic record
Abstract
OBJECTIVE: Prior research indicates disguise negatively affects lineup identifications but the mechanisms by which disguise works have not been explored and different disguises have not been compared. We investigated how two different types of disguise, four levels of varying degrees of coverage, and lineup type influence eyewitnesses' identification decisions, accuracy, and confidence. HYPOTHESES: We predicted that identification accuracy would decrease as the disguise covered more of a perpetrator's face. We also predicted that type of disguise-stocking mask versus sunglasses and/or toque (i.e., knitted hat)-would influence identifications, but we had conflicting predictions about which disguise would impair their performance more. METHOD: In two experiments (Ns = 87 and 91) we manipulated degree of coverage by two different types of disguise: a stocking mask or sunglasses and toque. Participants viewed mock-crime videos followed by simultaneous or sequential lineups. RESULTS AND CONCLUSIONS: Disguise and lineup type did not interact. In support of the view that disguise prevents encoding, identification accuracy generally decreased with degree of disguise. For the stocking disguise, however, full and 2/3 coverage led to approximately the same rate of correct identifications-which suggests that disrupting encoding of specific features may be as detrimental as disrupting a whole face. Accuracy was most affected by sunglasses and we discuss the role meta-cognitions may have played. Lineup selections decreased more slowly than accuracy as coverage by disguise increased, indicating witnesses are insensitive to the effect of encoding conditions on accuracy. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.042 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".