Detecting simulation of attention deficits using reaction time tests☆
Bibliographic record
Abstract
The current study examined if a newly developed series of reaction time tests, the Computerized Tests of Information Processing (CTIP) [Tombaugh, T. N., & Rees, L. (2000). Manual for the Computerized Tests of Information Processing (CTIP). Ottawa, Ontario: Carleton University (unpublished test)], were sensitive to simulation of attention deficits commonly caused by traumatic brain injury (TBI). The CTIP consists of three reaction time tests: Simple RT, Choice RT, and Semantic Search RT. These tests were administered to four groups: Control, Simulator, Mild TBI, and Severe TBI. Individuals attempting to simulate attention deficits produced longer reaction time scores, made more incorrect responses, and exhibited greater variability than cognitively-intact individuals and those with TBI. Sensitivity and specificity values were comparable or exceeded those obtained on the Test of Memory Malingering [Tombaugh, T. N. (1996). The Test of Memory Malingering (TOMM). Toronto, Canada: Multi-Health Systems Inc.]. As such, the CTIP offers considerable promise of serving as a viable malingering test that uses a distinctively different paradigm than the two-item, forced-choice procedure employed by traditional symptom validity tests.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".