The Negative Symptoms of Schizophrenia: A Cognitive Perspective
Bibliographic record
Abstract
Recent reports of improvement in the negative symptoms of schizophrenia following targeted cognitive interventions have prompted interest in the cognitive underpinnings of these symptoms. This review integrates current experimental research with the phenomenological accounts of patients participating in cognitive therapy for these specific symptoms. We propose that, in addition to the well-established role of neurobiological factors in their development and maintenance, specific cognitive appraisals and beliefs play a role in the expression and persistence of negative symptoms. This cognitive model of negative symptoms is based on a diathesis-stress formulation: a continuum of predispositional traits from the premorbid personality to the full-blown negative symptomatology, the incorporation of negative social and performance attitudes within these traits, and low expectancies for pleasure or success in goal-oriented activities. We suggest that negative symptoms represent, in part, a compensatory pattern of disengagement in response to threatening delusional beliefs, perceived social threat, and anticipated failure in tasks and social activities. A psychological aspect of this motivational and behavioural inertia appears to be the patient's perception of limited psychological resources--a perception that motivates patients to conserve energy by minimizing investment in activities requiring effort.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".