Egocentric Navigation Abilities Predict Episodic Memory Performance
Bibliographic record
Abstract
The medial temporal lobe supports both navigation and declarative memory. On this basis, a theory of phylogenetic continuity has been proposed according to which episodic and semantic memories have evolved from egocentric (e.g., path integration) and allocentric (e.g., map-based) navigation in the physical world, respectively. Here, we explored the behavioral significance of this neurophysiological model by investigating the relationship between the performance of healthy individuals on a path integration and an episodic memory task. We investigated the path integration performance through a proprioceptive Triangle Completion Task and assessed episodic memory through a picture recognition task. We evaluated the specificity of the association between performance in these two tasks by including in the study design a verbal semantic memory task. We also controlled for the effect of attention and working memory and tested the robustness of the results by including alternative versions of the path integration and semantic memory tasks. We found a significant positive correlation between the performance on the path integration the episodic, but not semantic, memory tasks. This pattern of correlation was not explained by general cognitive abilities and persisted also when considering a visual path integration task and a non-verbal semantic memory task. Importantly, a cross-validation analysis showed that participants' egocentric navigation abilities reliably predicted episodic memory performance. Altogether, our findings support the hypothesis of a phylogenetic continuity between egocentric navigation and episodic memory and pave the way for future research on the potential causal role of egocentric navigation on multiple forms of episodic memory.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".