Inefficient integration during multiple facial processing in pre-morbid and early phases of psychosis
Bibliographic record
Abstract
We used eye-tracking to evaluate multiple facial context processing and event-related potential (ERP) to evaluate multiple facial recognition in individuals at clinical high risk (CHR) for psychosis. In total, 173 subjects (83 CHRs and 90 healthy controls [HCs]) were included and their emotion perception performances were accessed. A total of 40 CHRs and 40 well-matched HCs completed an eye-tracking task where they viewed pictures depicting a person in the foreground, presented as context-free, context-compatible, and context-incompatible. During the two-year follow-up, 26 CHRs developed psychosis, including 17 individuals who developed first-episode schizophrenia (FES). Eighteen well-matched HCs were made to complete the face number detection ERP task with image stimuli of one, two, or three faces. Compared to the HC group, the CHR group showed reduced visual attention to contextual processing when viewing multiple faces. With the increasing complexity of contextual faces, the differences in eye-tracking characteristics also increased. In the ERP task, the N170 amplitude decreased with a higher face number in FES patients, while it increased with a higher face number in HCs. Individuals in the very early phase of psychosis showed facial processing deficits with supporting evidence of different scan paths during context processing and disruption of N170 during multiple facial recognition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".