Behavioural phenotypes of intrinsic motivation in schizophrenia determined by cluster analysis of objectively quantified real-world performance
Bibliographic record
Abstract
Intrinsic motivation deficits are a prominent feature of schizophrenia that substantially impacts functional outcome. This study used cluster analysis of innate real-world behaviours captured during two open-field tasks to dimensionally examine heterogeneity in intrinsic motivation in schizophrenia patients (SZ) and healthy controls (HC). Wireless motion capture quantified participants' behaviours aligning with distinct aspects of intrinsic motivation: exploratory behaviour and effortful activity in the absence of external incentive. Cluster analysis of task-derived measures identified behaviourally differentiable subgroups, which were compared across standard clinical measures of general amotivation, cognition, and community functioning. Among 45 SZ and 47 HC participants, three clusters with characteristically different behavioural phenotypes emerged: low exploration (20 SZ, 19 HC), low activity (15 SZ, 8 HC), and high exploration/activity (10 SZ, 20 HC). Low performance in either dimension corresponded with similar increased amotivation. Within-cluster discrepancies emerged for amotivation (SZ > HC) within the low exploration and high performance clusters, and for functioning (SZ < HC) within all clusters, increasing from high performance to low activity to low exploration. Objective multidimensional characterization thus revealed divergent behavioural expression of intrinsic motivation deficits that may be conflated by summary clinical measures of motivation and overlooked by unidimensional evaluation. Deficits in either aspect may hinder general motivation and functioning particularly in SZ. Multidimensional phenotyping may help guide personalized remediation by discriminating between intrinsic motivation impairments that require amelioration versus unimpaired tendencies that may facilitate remediation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".