Binding Action and Emotion in First-Episode Schizophrenia
Bibliographic record
Abstract
BACKGROUND/AIMS: Several components of social cognition are compromised in schizophrenia (SCZ) from the early stage of the illness. In this study we first investigated whether mirror neuron-driven embodied simulation (mnES) is altered in first-episode SCZ. Second, we tested whether emotional cues impact on the mnES in SCZ patients. METHODS: Twenty-two SCZ patients and 22 healthy controls (HCs) observed goal-related actions in either a neutral or emotional context during functional magnetic resonance imaging scanning. RESULTS: Observation of neutral action elicited a lower activity in the frontoparietal network in SCZ patients, as compared to HCs. Particularly, activation in the left inferior parietal lobule in response to the same condition negatively correlated with patients' self-experience disturbances. Moreover, observation of an action performed by an angry agent produced poorer neural activity in the right anterior insula in SCZ patients as compared to HCs. This difference was mostly due to the negative β-values shown by SCZ patients, which positively correlated with their empathy scores. No differences were found contingent upon the observation of an action performed by a happy agent. CONCLUSION: Our results show that emotional cues allow SCZ patients to partially recover mnES. However, their understanding of the emotional components of the actions of others will likely remain deficient.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".