Using machine learning analyses to explore relations between eyewitness lineup looking behaviors and suspect guilt.
Bibliographic record
Abstract
OBJECTIVE: We conducted 2 experiments using machine learning to better understand which lineup looking behaviors postdict suspect guilt., Hypotheses: We hypothesized that (a) lineups with guilty suspects would be subject to shorter viewing duration of all images and fewer image looks overall than lineups with innocent suspects, and (b) confidence and accuracy would be positively correlated. The question of which factors would combine to best postdict suspect guilt was exploratory. METHOD: = 21.00; females = 75%) who each made 2 identifications after viewing a video including 2 targets. Participants made identifications using an interactive touchscreen simultaneous lineup in which they were restricted to viewing one image at a time and their interaction with the lineup was recorded. RESULTS: In Experiment 1, five variables (filler look time, suspect look time, number of suspect looks, number of filler looks, and winner look time) together postdicted (with a 67% accuracy score) target presence. In Experiment 2, four variables (number of suspect looks, number of filler looks, number of loser looks, and winner looks) together postdicted (with a 73% accuracy score) target presence. CONCLUSIONS: Further exploration of witness search behaviors can provide context to identification decisions. Understanding which behaviors postdict suspect guilt may assist with interpretation of identification decisions in the same way that decision confidence is currently used. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".