How far to go in deconstructing negative symptoms? Behavioural and neural level evidence for the amotivation domain
Bibliographic record
Abstract
Negative symptoms in schizophrenia are conceptualised as loading onto two factors: amotivation and diminished expression, which relate to different behavioural and neural markers. This distinction has proven useful for understanding the cognitive, motivational and neural mechanisms involved in negative symptoms, and for the development of treatments. Recently, it has been advocated that an even finer distinction into five subdomains is needed to understand the mechanisms underlying negative symptoms, and to prevent masking specific treatment and intervention effects. However, it is currently unclear whether such a fine-grained approach offers additional insights grounded in theory. In the present work, we focused on the factor amotivation, which has been shown to selectively correlate with the propensity to discount rewards in the face of effort and with the activity in the ventral striatum during reward anticipation. In a reanalysis of these studies we explored whether subdomains of amotivation - avolition, asociality, anhedonia - showed preferential correlation with these previously identified behavioural and neural markers. We show that for both behavioural and neural markers, a fine-grained model with the three subdomains did not better explain the data than a model with the amotivation factor only. Moreover, none of the three subdomains correlated significantly more or less with the behavioural or neural markers. Thus, no additional information was gained on amotivation in schizophrenia by selectively looking at its three subdomains. Consequently, the two-factor solution currently remains a valid option for the study of negative symptoms and further research is needed for behavioural and neural validation of the five-factor model.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".