Increased intra-subject variability in reward behavior relates to symptom severity in schizophrenia
Bibliographic record
Abstract
Schizophrenia (SZ) is a complex disorder characterized by positive and negative symptoms that have been linked to dysfunction in cognition and reward motivation. Recent findings show higher inter-subject variability in SZ in various cognitive functions. This raises the question of whether there is also higher intra-subject variability in SZ at the psychological level, specifically increased variability across the trials of a psychological task within the subject itself, that is, intra-subject variability. To examine fluctuations in behavior during a reward-based discrimination and liking task, we analyzed intra-subject variability in SZ and observed the following: (i) increased intra-subjective variability across all four behavioral measures, that is, response times (RT) for discrimination and liking tasks, as well as accuracy (ACC) and liking ratings; (ii) significant correlation of the different measures' intra-subject variabilities across the distinct tasks, e.g., RT, ACC, and liking ratings among each other; and (iii) relation of the increased intra-subjective variability in the behavioral measures (RT, ACC, liking) with overall and general psychopathological symptom severity, as measured by the positive and negative syndrome scale (PANSS). Together, we demonstrate abnormally increased intra-subjective variability in a reward-motivation task in SZ and its key role in relation to symptom severity. This increased intra-subject variability at the psychological-behavioral level suggests abnormal and imprecise timing in cognitive processing, which aligns with analogous findings of temporal imprecision at the neural level.
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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 | 0.003 |
| 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 | 0.000 |
| 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".