Conducting Research with Non-clinical Healthy Undergraduates: Does Effort Play a Role in Neuropsychological Test Performance?
Bibliographic record
Abstract
Poor effort by examinees during neuropsychological testing has a profound effect on test performance. Although neuropsychological experiments often utilize healthy undergraduate students, the test-taking effort of this population has not been investigated previously. The purpose of the present study was to determine whether undergraduate students exercise variable effort in neuropsychological testing. During two testing sessions, participants (N = 36) were administered three Symptom Validity Tests (SVTs), the Test of Memory Malingering, the Dot Counting Test, and the Victoria Symptom Validity Test (VSVT), along with various neuropsychological tests. Analyses revealed 55.6% of participants in Session 1 and 30.8% of participants in Session 2 exerted poor effort on at least one SVT. Poor effort on the SVTs was significantly correlated with poor performance on various neuropsychological tests and there was support for the temporal stability of effort. These preliminary results suggest that the base rate of suboptimal effort in a healthy undergraduate population is quite high. Accordingly, effort may serve as a source of variance in neuropsychological research when using undergraduate students.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".