Mental space travel: Damage to posterior parietal cortex prevents egocentric navigation and reexperiencing of remote spatial memories.
Bibliographic record
Abstract
The ability to navigate in a familiar environment depends on both an intact mental representation of allocentric spatial information and the integrity of systems supporting complementary egocentric representations. Although the hippocampus has been implicated in learning new allocentric spatial information, converging evidence suggests that the posterior parietal cortex (PPC) might support egocentric representations. To date, however, few studies have examined long-standing egocentric representations of environments learned long ago. Here we tested 7 patients with focal lesions in PPC and 12 normal controls in remote spatial memory tasks, including 2 tasks reportedly reliant on allocentric representations (distance and proximity judgments) and 2 tasks reportedly reliant on egocentric representations (landmark sequencing and route navigation; see Rosenbaum, Ziegler, Winocur, Grady, & Moscovitch, 2004). Patients were unimpaired in distance and proximity judgments. In contrast, they all failed in route navigation, and left-lesioned patients also showed marginally impaired performance in landmark sequencing. Patients' subjective experience associated with navigation was impoverished and disembodied compared with that of the controls. These results suggest that PPC is crucial for accessing remote spatial memories within an egocentric reference frame that enables both navigation and reexperiencing. Additionally, PPC was found to be necessary to implement specific aspects of allocentric navigation with high demands on spontaneous retrieval.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".