Selective deficit in spatial memory strategies contrast to intact response strategies in patients with schizophrenia spectrum disorders tested in a virtual navigation task
Bibliographic record
Abstract
Spatial memory is impaired among persons with schizophrenia (SCZ). However, different strategies may be used to solve most spatial memory and navigation tasks. This study investigated the hypothesis that participants with schizophrenia-spectrum disorders (SSD) would demonstrate differential impairment during acquisition and retrieval of target locations when using a hippocampal-dependent spatial strategy, but not a response strategy, which is more associated with caudate function. Healthy control (CON) and SSD participants were tested using the 4-on-8 virtual maze (4/8VM), a virtual navigation task designed to differentiate between participants' use of spatial and response strategies. Consistent with our predictions, SSD participants demonstrated a differential deficit such that those who navigated using a spatial strategy made more errors and took longer to locate targets. In contrast, SSD participants who spontaneously used a response strategy performed as well as CON participants. The differential pattern of spatial-memory impairment in SSD provides only indirect support for underlying hippocampal dysfunction. These findings emphasize the importance of considering individual strategies when investigating SSD-related memory and navigation performance. Future cognitive intervention protocols may harness SSD participants' intact ability to navigate using a response strategy and/or train the deficient ability to navigate using a spatial strategy to improve navigation and memory abilities in participants with SSD.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".