Bibliographic record
Abstract
Background and aims.-Biologicalmotion (BM) processing constitutes part of social cognition, and a specialized network evolved for its accurate and automatic identification.Neuroimaging studies suggest prominent role for several brain areas in BM processing, including the posterior superior temporal sulcus (pSTS) with right hemisphere bias, the extrastriate body area (EBA) and the kinetic occipital (KO) region.Although the structural underpinnings are relatively well defined, the temporal dimension of the processing is unclear.Patients with schizophrenia with lower score on Zigler social competence scale show deficits in BM and scrambled motion discrimation.Our aim to identify differences between patients with schizophrenia and healhty controls in electrophysiological correlates of BM recognition.Methods.-EEGs were acquired using high-density 256-channel EEG-system from 40 patients with schizophrenia and 45 healthy controls.We conducted time-frequency analysis of the EEG, and applied random-regression hierarchical linear modelling to identify differences between study groups across all electrodes.Results.-In patients with schizophrenia, the accuracy of BM recognition was significantly lower and reaction time was slower than in healthy controls, (in patients and controls, respectively, the mean accuracy was 80% and 92%; and reaction time was 765 and 690ms).Spectral amplitudes in theta (4-7 Hz) and gamma (30-50 Hz) bands were significantly reduced in patients with schizophrenia compared to controls.Furthermore, BM elicited larger amplitudes in alpha (8-12 Hz) and beta (13-30 Hz) bands.Conclusions.-Biologicalmotion recognition in patients with schizophrenia is impaired.Alterations in theta and gamma frequency bands may indicate disconnectivity in the underlying neural networks, which may lead to altered BM perception.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.775 | 0.597 |
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".