Investigating Neural Mechanisms Underlying Landmark and Map-Learning in a Novel Goal-Directed Navigation Research Platform
Bibliographic record
Abstract
When we drive into a supermarket, navigate in a large shopping mall or find our way in an unfamiliar city, we utilize our attention and spatial memory to reach our goal. Previous research shows that the integration of memory, perception, and executive functions is essential for efficient navigation, and its capacity can vary greatly among individuals. It is unclear what specific factors drive individual differences in navigation abilities and spatial cognition. This study investigates the neural and behavioral mechanisms underlying landmark and map-based learning during goal-directed navigation. Our main contributions in this project contain two parts: 1) up to our best knowledge, I successfully developed the first novel goal-directed navigation research platform, NeuroNav, for investigating the neural mechanism in navigation. The maze design of NeuroNav is inspired by Tolman's multiple T-junctions. During testing, participants can observe the environment in the maze via a built-in camera and control their view and location via screw-driven linear slides, a gamepad controller, and an Arduino microcontroller; 2) I systematically examined how individual differences in attention, working memory, and spatial familiarity influence navigation performance. Participants navigated to different goal locations both with and without a map while behavioral metrics (e.g., time to goal, heading changes) and EEG signals (concentration, theta, beta, gamma bands) were recorded. Results showed that familiarity and map use significantly improved navigation efficiency and reduced cognitive load, as reflected in both behavior and neural activity. EEG recordings revealed increased theta and gamma activity during novel landmark encoding and decision-making phases. These findings highlight the interplay between attention, memory, and environmental cues in spatial learning, with implications for assistive navigation technologies and populations with spatial memory deficits.
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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) | 0.000 | 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) | 0.000 | 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".