Vestibular cues improve landmark-based route navigation: A simulated driving study
Bibliographic record
Abstract
It is well established that humans use self-motion and landmark cues to successfully navigate their environment. Existing research has demonstrated a critical role of the vestibular system in supporting navigation across many species. However, less is known about how vestibular cues interact with landmarks to promote successful navigation in humans. In the present study, we used a motion simulator to manipulate the presence or absence of vestibular cues during a virtual navigation task. Participants learned routes to a target destination in three different landmark blocks in a virtual town: one with proximal landmarks, one with distal landmarks, and one with no landmarks present. Afterwards, they were tested on their ability to retrace the route and find the target destination. We observed a significant interaction between vestibular cues and proximal landmarks, demonstrating that the potential for vestibular cues to improve route navigation is dependent on landmarks that are present in the environment. In particular, vestibular cues significantly improved route navigation when proximal landmarks were present, but this was not significant when distal landmarks or no landmarks were present. Overall, our results indicate that landmarks play an important role in the successful incorporation of vestibular cues to human spatial navigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".