Deciphering Amotivation in Schizophrenia: A Bayesian Computational Analysis of Exploratory Behaviour
Bibliographic record
Abstract
Amotivation significantly influences functional outcomes in schizophrenia (SZ), yet its etiological pathways and effective treatments remain elusive. This thesis builds upon the virtual exploration work in SZ from Siddiqui et al. (2018), by applying a Bayesian computational framework to dissect the components associated with amotivation in SZ. We analyzed data from 24 outpatients with SZ and 26 controls who completed the Virtual Novelty Exploratory Task (VNET) and employed a 3-level Hierarchical Gaussian Filter model to evaluate model parameters under amotivation. Our findings support the aberrant salience hypothesis and highlight underlying cognitive processes associated with amotivation in SZ. Specifically, patients with SZ appear to experience increased uncertainty in their world perception as they assimilate more information, and patients with more severe motivation deficits appear to demonstrate a reduced inclination to minimize uncertainty through exploration and perceive their environment as being less novel.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".