Hippocampal Signal Complexity and Rate-of-Change Predict Navigational Performance: Evidence from a Two-Week VR Training Program
Bibliographic record
Abstract
The hippocampus is believed to be an important region for spatial navigation, helping to represent the environment and plan routes. Evidence from rodents has suggested that the hippocampus processes information in a graded manner along its long-axis, with anterior regions encoding coarse information and posterior regions encoding fine-grained information. Brunec et al. (2018) demonstrated similar patterns in humans in a navigation paradigm, showing that the anterior-posterior gradient in representational granularity and the rate of signal change exist in the human hippocampus. However, the stability of these signals and their relationship to navigational performance remain unclear. In this study, we conducted a two-week training program where participants learned to navigate through a novel city environment. We investigated inter-voxel similarity (IVS) and temporal auto-correlation hippocampal signals, measures of representational granularity and signal change, respectively. Specifically, we investigated how these signals were influenced by navigational ability (i.e., stronger vs. weaker spatial learners), training session, and navigational dynamics. Our results revealed that stronger learners exhibited a clear anterior-posterior distinction in IVS in the right hippocampus, while weaker learners showed less pronounced distinctions. Additionally, lower general IVS levels in the hippocampus were linked to better early learning. Successful navigation was characterized by faster signal change, particularly in the anterior hippocampus, whereas failed navigation lacked the anterior-posterior distinction in signal change. These findings suggest that signal complexity and signal change in the hippocampus are important factors for successful navigation, with IVS representing information organization and auto-correlation reflecting moment-to-moment updating. These findings support the idea that efficient organization of scales of representation in an environment may be necessary for efficient navigation itself. Understanding the dynamics of these neural signals provides insights into the mechanisms underlying navigational learning in humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".