Object versus spatial visual mental imagery in patients with schizophrenia
Bibliographic record
Abstract
OBJECTIVE: Recent research has revealed a larger impairment of object perceptual discrimination than of spatial perceptual discrimination in patients with schizophrenia. It has been suggested that mental imagery may share processing systems with perception. We investigated whether patients with schizophrenia would show greater impairment regarding object imagery than spatial imagery. METHODS: Forty-four patients with schizophrenia and 20 healthy control subjects were tested on a task of object visual mental imagery and on a task of spatial visual mental imagery. Both tasks included a condition in which no imagery was needed for adequate performance, but which was in other respects identical to the imagery condition. This allowed us to adjust for nonspecific differences in individual performance. RESULTS: The results revealed a significant difference between patients and controls on the object imagery task (F(1,63) = 11.8, p = 0.001) but not on the spatial imagery task (F(1,63) = 0.14, p = 0.71). To test for a differential effect, we conducted a 2 (patients v. controls) small ha, Cyrillic 2 (object task v. spatial task) analysis of variance. The interaction term was statistically significant (F(1,62) = 5.2, p = 0.026). CONCLUSIONS: Our findings suggest a differential dysfunction of systems mediating object and spatial visual mental imagery in schizophrenia.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".