A Randomized Controlled Trial of Executive Functioning Training Compared With Perceptual Training for Schizophrenia Spectrum Disorders: Effects on Neurophysiology, Neurocognition, and Functioning
Bibliographic record
Abstract
OBJECTIVE: Cognitive remediation is an efficacious treatment for schizophrenia. However, different theoretical approaches have developed without any studies to directly compare them. This is the first study to compare the two dominant approaches to cognitive remediation (training of executive skills and training of perceptual skills) and employed the broadest assessment battery in the literature to date. METHODS: Outpatients with schizophrenia spectrum disorders were randomly assigned to receive either executive training or perceptual training. Electrophysiological activity, neurocognition, functional competence, case manager-rated community functioning, clinical symptoms, and self-report measures were assessed at baseline, immediately after treatment, and at a 12-week posttreatment follow-up assessment. RESULTS: Perceptual training improved the EEG mismatch negativity significantly more than executive training immediately after treatment, although the effect did not persist at the 12-week follow-up. At the follow-up, executive training improved theta power during an n-back task, neurocognition, functional competence, and case manager-rated community functioning to a greater extent than perceptual training. These effects were not observed immediately after treatment. CONCLUSIONS: Both perceptual training and executive training improved neurophysiological mechanisms specific to their domains of intervention, although only executive training resulted in improvement in neurocognition and functioning. Improvements in favor of executive training did not appear immediately after treatment but emerged 12 weeks after the end of active treatment. Thus, short-term intervention targeting higher-order cognitive functions may prime further cognitive and functional improvement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".