Visual scan paths in first-episode schizophrenia and cannabis-induced psychosis
Bibliographic record
Abstract
OBJECTIVE: Patterns of successive saccades and fixations (scan paths) that are made while viewing images are often spatially restricted in schizophrenia, but the relation with cannabis-induced psychosis has not been examined. We used higher-order statistical methods to examine spatiotemporal characteristics of scan paths to determine whether viewing behaviour was distinguishable on a continuum. METHODS: Patients with early acute first-episode paranoid schizophrenia (SCH; n = 11), cannabis-induced psychosis (CIP; n = 6) and unaffected control subjects (n = 22) undertook a task requiring free viewing of facial, fractal and landscape images for 5 seconds while their eye movements were recorded. Frequencies and distributions of saccades and fixations were calculated in relation to image regions examined during each trial. RESULTS: Findings were independent of image category, indicating generalized scanning deficits. Compared with control subjects, patients with SCH and CIP made fewer saccades and fewer fixations of longer duration. In turn, the spatial distribution of fixations in CIP patients was more clustered than in SCH and control subjects. The diversity of features fixated in subjects with CIP was also lower than in SCH patients and control subjects. CONCLUSION: A continuous approach to characterizing scan path changes in different phenotypes suggests that CIP shares some of the abnormalities of SCH but can be distinguished with measures that are sensitive to cognitive strategies active or inhibited during visual exploration.
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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 | 0.002 |
| 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".