The influence of vision on the estimation of walked distance
Bibliographic record
Abstract
Traversed distance estimation is influenced by both visual cues (particularly optic flow) and locomotor cues (proprioceptive/efferent copy and vestibular). While evidence suggests that locomotor cues alone can be used to estimate distance, little is known about the role of optic flow when both visual and locomotor cues are available simultaneously. The current study employed a consecutive cue-conflict paradigm to compare distance estimates obtained via locomotor cues alone to those obtained via both locomotor and optic flow cues. This experiment took place in a large, open, outdoor environment. Subjects (Ss) were presented with two distances, which they were informed were identical in magnitude; one via blindfolded locomotion (L) and one via locomotion with vision (LV) (with the order of the two randomized). For the majority of the trials, the magnitude of the two stimulus distances was indeed the same (congruent), but for a small subset of trials the two distances differed in magnitude (incongruent). Subsequently, Ss produced an estimate by adjusting the distance of a visual target to match the learned distance. Overall, a small underestimation was observed in all cases. For congruent trials, when the same cues were present in both stimulus distances, estimates were slightly shorter for LV than for L. However, when different cues were present in the two stimulus distances, there was also an effect of cue presentation order. When LV occurred second, distance estimates were much shorter than when LV occurred first. For incongruent trials, the effect of cue was compounded with a powerful effect of distance presentation order. When the longer distance was presented second, estimates more closely approximated the longer distance, whereas when the shorter distance was presented second, estimates more closely approximated the shorter distance. This effect was more prominent when LV occurred second, indicating a dominant effect of vision and an interaction between cue and presentation order.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| 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".