Eye movements during natural actions in patients with schizophrenia
Bibliographic record
Abstract
BACKGROUND: Visual scanning and planning of actions are reported to be abnormal in patients with schizophrenia. Most studies that monitored eye movements in these patients were performed under free-viewing conditions and used 2- dimensional images. However, images differ from the natural world in several ways, including task demands and the dimensionality of the display. Our study was designed to assess whether abnormalities in visual exploration in patients with schizophrenia generalize to active-viewing tasks in realistic conditions of viewing and to examine whether disturbances in action sequencing in these patients are reflected in their visual scanning patterns while executing natural tasks. METHODS: We monitored visual scan paths in patients with schizophrenia and healthy controls. Participants performed several tasks in which they were asked to look at a realistic scene on a table (free-viewing) and perform 2 active-viewing tasks: a familiar task (sandwich-making) and an unfamiliar task (model-building). The scenes contained both task-relevant and task-irrelevant objects. RESULTS: We included 15 patients and 15 controls in our analysis. Patients exhibited abnormalities in the free-viewing condition. Their patterns of exploration were similar to those of controls in the familiar task, but they showed scanning differences in the unfamiliar task. Patients were also slower than controls to accomplish both tasks. LIMITATIONS: Patients with schizophrenia were taking antipsychotic medications, so the presence of medication effects cannot be excluded. CONCLUSION: People with schizophrenia present a basic psychomotor slowing and seem to establish a less efficient planning strategy in the case of more complex and unfamiliar tasks.
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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 | 0.001 | 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".