Partially segregated neural networks for spatial and contextual memory in virtual navigation
Bibliographic record
Abstract
Finding our way in a previously learned, ecologically valid environment concurrently involves spatial and contextual cognitive operations. The former process accesses a cognitive map representing the spatial interactions between all paths in the environment. The latter accesses stored associations between landmark objects and their milieu. Here, we aimed at dissociating their neural basis in the context of memory-based virtual navigation. To do so, subjects freely explored a virtual town for 1 h, then were scanned using fMRI while retrieving their way between two locations, under four navigation conditions designed to probe separately or jointly the spatial and contextual memory components. Besides prominent commonalities found in a large hippocampo-neocortical network classically involved in topographical navigation, results yield evidence for a partial dissociation between the brain areas supporting spatial and contextual components of memory-based navigation. Performance-related analyses indicate that hippocampal activity mostly supports the spatial component, whereas parahippocampal activity primarily supports the contextual component. Additionally, the recruitment of contextual memory during navigation was associated with higher frontal, posterior parietal and lateral temporal activity. These results provide evidence for a partial segregation of the neural substrates of two crucial memory components in human navigation, whose combined involvement eventually leads to efficient navigation behavior within a learned environment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".