Negative Symptoms in Schizophrenia: Avolition and Occam's Razor
Bibliographic record
Abstract
The identification of schizophrenia's negative symptoms dates back to the earliest descriptions of Kraepelin and Bleuler, who each highlighted the central role of avolition in the phenomenology and course of this illness. Since, there have been numerous advances in our understanding of schizophrenia, and the present review tracks the changes that have taken place in our understanding of negative symptoms, their description and measurement. That these symptoms represent a distinct domain of the illness is discussed in the context of their ties to other symptoms and functional outcome. The underlying structure of the negative symptom construct is explored, including several lines of investigation that point towards diminished expression and amotivation as key underlying subdomains. We also discuss findings of intact emotional experience and consummatory pleasure in individuals with schizophrenia, calling into question the presence of anhedonia in this illness. We conclude with a reconceptualization of the negative symptoms, suggesting amotivation (ie, avolition) represents the critical component, particularly in regard to functional outcome. Further exploration and clarification of this core deficit will ultimately enhance our neurobiological understanding of schizophrenia, as well as strategies that may improve outcome.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".