Spatial reorientation with a geometric array of auditory cues
Bibliographic record
Abstract
A visuocentric bias has dominated the literature on spatial navigation and reorientation. Studies on visually accessed environments indicate that, during reorientation, human and non-human animals encode the geometric shape of the environment, even if this information is unnecessary and insufficient for the task. In an attempt to extend our limited knowledge on the similarities and differences between visual and non-visual navigation, here we examined whether the same phenomenon would be observed during auditory-guided reorientation. Provided with a rectangular array of four distinct auditory landmarks, blindfolded, sighted participants had to learn the location of a target object situated on a panel of an octagonal arena. Subsequent test trials were administered to understand how the task was acquired. Crucially, in a condition in which the auditory cues were indistinguishable (same sound sample), participants could still identify the correct target location, suggesting that the rectangular array of auditory landmarks was encoded as a geometric configuration. This is the first evidence of incidental encoding of geometric information with auditory cues and, consistent with the theory of functional equivalence, it supports the generalisation of mechanisms of spatial learning across encoding modalities.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".