Evidence that specific executive functions predict symptom variance among schizophrenia patients with a predominantly negative symptom profile
Bibliographic record
Abstract
INTRODUCTION: Although deficits in executive functioning in schizophrenia have been consistently reported, their precise relationship to symptomatology remains unclear. Recent approaches to executive functioning in nonschizophrenia studies have aimed to "fractionate" the individual cognitive processes involved. In this study, we hypothesised that if these processes are fractionable, then particular symptom syndromes may be selectively related to executive deficits. In particular, it was hoped that this approach could clarify whether negative and positive symptoms of schizophrenia are differentially related to particular aspects of executive/attentional functions. METHODS: A total of 32 patients with schizophrenia and 16 matched controls were assessed on a series of tasks designed to tap the theoretically derived executive functions of Inhibition, Shifting set, Working memory, and Sustained attention. RESULTS: Negative symptoms were significantly predicted by performance on an "Inhibition" task (Stroop), and not by performance on any other task. Furthermore, for a subgroup of patients with predominantly negative symptoms variance in positive symptoms was only significantly predicted by performance on a set-shifting task (Visual Elevator), and not by performance on other tasks, including inhibition. CONCLUSIONS: Our results support the contention that negative symptoms can, at least partly, be conceived of as cognitive behaviours expressing specific executive deficits. Specifically, we discuss the possibility that negative symptoms may, in part, express a failure in response monitoring. We further suggest that the disordered metacognition resulting in positive symptoms may be mediated by cognitive flexibility in patients with a predominantly negative symptom profile.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".