P300-BASED MEMORY DETECTION APPLIED TO A MOCK TERRORISM SCENARIO USING THE COMPLEX TRIAL PROTOCOL WITH MULTIPLE PICTORIAL STIMULI
Bibliographic record
Abstract
The Complex Trial Protocol (CTP), a P300-based Concealed Information Test (CIT) is an innovative tool that can be used to identify individuals who possess crime-related information. We tested the CTP in a mock terrorism scenario with three different probes. Forty-one undergraduate participants were randomly assigned to one of three groups, Innocent Control (IC), Simply Guilty (SG), and Guilty Countermeasure (GCM). Individuals in the SG and GCM groups underwent a mock terrorism scenario and were exposed to three pictorial probes, the face of an accomplice, the crime scene, and the mock explosive device. Additionally, the GCM group performed a memory suppression countermeasure. Based on the AUCs generated, the CTP showed a good to very good predictive ability ranging from .63 to .94 depending on the probe presented. The aggregated scores led to an AUC of .79 for the SG participants and of .90 for the GCM, indicating that it may be advantageous to use multiple probes. Overall, hit rates ranged from 54-78% (bomb), 64-93% (crime scene), and 71-93% (male accomplice). Attempting to suppress information had the opposite effect of generating slightly higher P300 amplitudes than in SG individuals. Stimuli quality and ecological issues are discussed.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".