Visual–spatial episodic memory in schizophrenia: A multiple systems framework.
Bibliographic record
Abstract
OBJECTIVE: Enhanced understanding of cognitive deficits, and the neurobiological abnormalities that mediate them, can be achieved through translational research that employs comparable experimental approaches across species. This study employed a multiple-systems framework derived from the rodent literature to investigate visual-spatial memory abilities associated with schizophrenia. METHOD: Using the bin task, a human analog of rodent maze tasks, everyday objects were hidden in visually identical bins. Following a 1-min filled delay, participants with schizophrenia-spectrum disorders (n = 30) and healthy community controls (n = 30) were asked to identify both the object hidden and bin used on the basis of its spatial location. Three dimensions of visual-spatial memory were contrasted: (a) memory for spatial locations versus memory for objects, (b) allocentric (viewpoint independent) versus egocentric (body-centered) spatial representations, and (c) event (working) memory versus reference memory. RESULTS: Most pronounced was a differential deficit in memory for spatial locations under allocentric (p = .005, d = -0.77) but not egocentric viewing conditions (p = .298, d = -0.28) in the schizophrenia group relative to healthy controls. Similarly, schizophrenia-related spatial memory deficits were pronounced under demands for event memory (p = .004, d = -0.77) but not reference memory (p = .171, d = -0.33). CONCLUSIONS: These results support a heuristic of preferential deficits in hippocampal-mediated forms of memory in schizophrenia. Moreover, the task provides a useful paradigm for translational research and the pattern of deficits suggests that persons with schizophrenia may benefit from mnemonic approaches favoring egocentric representations and consistency when interacting with our visual-spatial world.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".