Efference Copy Failure during Smooth Pursuit Eye Movements in Schizophrenia
Bibliographic record
Abstract
Abnormal smooth pursuit eye movements in patients with schizophrenia are often considered a consequence of impaired motion perception. Here we used a novel motion prediction task to assess the effects of abnormal pursuit on perception in human patients. Schizophrenia patients (n = 15) and healthy controls (n = 16) judged whether a briefly presented moving target ("ball") would hit/miss a stationary vertical line segment ("goal"). To relate prediction performance and pursuit directly, we manipulated eye movements: in half of the trials, observers smoothly tracked the ball; in the other half, they fixated on the goal. Strict quality criteria ensured that pursuit was initiated and that fixation was maintained. Controls were significantly better in trajectory prediction during pursuit than during fixation, their performance increased with presentation duration, and their pursuit gain and perceptual judgments were correlated. Such perceptual benefits during pursuit may be due to the use of extraretinal motion information estimated from an efference copy signal. With an overall lower performance in pursuit and perception, patients showed no such pursuit advantage and no correlation between pursuit gain and perception. Although patients' pursuit showed normal improvement with longer duration, their prediction performance failed to benefit from duration increases. This dissociation indicates relatively intact early visual motion processing, but a failure to use efference copy information. Impaired efference function in the sensory system may represent a general deficit in schizophrenia and thus contribute to symptoms and functional outcome impairments associated with the disorder.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".