Detecting simulation of attention deficits using reaction time tests☆
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 Ottawa, Ontario: Carleton University (unpublished test)], were sensitive to simulation of attention deficits commonly caused by traumatic brain injury (TBI).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.
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.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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 it