Examining levels of processing using verbal & pictorial stimuli with the complex trial protocol in a mock theft scenario
Bibliographic record
Abstract
The Complex Trial Protocol (CTP) is an EEG-based Concealed Information Test (CIT). Depth of processing influences memorability where deeper processing increases recollection. The CTP's performance as a function of shallow versus deep levels of processing has not been explored. Two experiments were conducted, one with verbal stimuli and the other with their pictorial referents. In both experiments, participants were randomly assigned to three groups, Innocent Control (Control) condition, Guilty Immediate Shallow Processing (Shallow) condition, and Guilty Immediate Deep Processing (Deep) condition. Shallow and Deep participants from both experiments underwent the same mock theft scenario and all three groups were later exposed to either a verbal (N = 41) or pictorial (N = 43) stimulus on a computer monitor. In the word study, no differences in CIT effect were found between any of the groups. Areas under the curve (AUCs) did not differ from chance (.624 and .679 for Shallow and Deep groups respectively). In the image study, the CIT effect for the Shallow and Deep groups differed from the Control one. The AUCs (.755 and .943 for the Shallow and Deep groups respectively) differed significantly from each other. Levels of Processing (LOP) did not appear to have any bearing on CTP performance when words were used as probes but did have an effect when images were used. The findings may hint at some of the limitations of the CTP and fail to replicate similar experiments, especially when words are used as probes, from the late Peter Rosenfeld's laboratory.
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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 | 0.001 | 0.010 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".