Pursuit eye movements and motion prediction in patients with schizophrenia
Bibliographic record
Abstract
GOAL: Tracking moving objects with smooth pursuit eye movements is essential for many everyday tasks. These continuous, slow eye rotations critically support vision by centering and stabilizing moving images on the fovea; they prevent motion blur and enhance visual acuity. We recently discovered a strong perceptual benefit during pursuit eye movements in a trajectory prediction task [Spering et al., J. Neurophysiol., 2011]. Here we investigate the ability to predict motion trajectories in patients with schizophrenia. Previous studies with these patients have established pronounced motion perception deficits and abnormalities in pursuit, most notably, in the velocity gain. METHOD: Observers (11 patients, 12 age-matched controls) judged whether a linearly moving target ("ball") would hit/miss a stationary vertical line segment ("goal"). Ball and goal were shown briefly (200 or 500ms) on a computer monitor and disappeared before the perceptual judgment was prompted. We manipulated eye movements: observers had to track the ball with their eyes (50% trials) or fixate on the goal while the ball was moving towards fixation. RESULTS: We found similarities in pursuit eye movement accuracy (i.e., velocity gain, direction error) between patients and controls. Moreover, both groups equally showed more accurate pursuit in trials with longer presentation duration. In contrast, perceptual motion prediction performance differed between groups: Across conditions, motion prediction was overall significantly better in controls than in patients (75 vs. 68% correct). Motion prediction was also significantly better when stimuli were presented longer than when presented shorter – but only for controls (78 vs. 72% correct); patients showed no such effect of presentation duration. Together, these findings indicate that motion prediction deficits in schizophrenic patients are not mediated by pursuit eye movements alone, suggesting differential impairments in early visual processing for motion perception and pursuit. Meeting abstract presented at VSS 2012
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".