A Comparison of Visual and Nonvisual Sensory Inputs to Walked Distance in a Blind-Walking Task
Bibliographic record
Abstract
Two experiments were conducted in order to assess the contribution of locomotor information to estimates of egocentric distance in a walking task. In the first experiment, participants were either shown, or led blind to, a target located at a distance ranging from 4 to 10 m and were then asked to indicate the distance to the target by walking to the location previously occupied by the target. Participants in both the visual and locomotor conditions were very accurate in this task and there was no significant difference between conditions. In the second experiment, a cue-conflict paradigm was used in which, without the knowledge of the participants, the visual and locomotor targets (the targets they were asked to walk to) were at two different distances. Most participants did not notice the conflict, but despite this their responses showed evidence that they had averaged the visual and locomotor inputs to arrive at a walked estimate of distance. Together, these experiments demonstrate that, although they showed poor awareness of their position in space without vision, in some conditions participants were able to use such nonvisual information to arrive at distance estimates as accurate as those given by vision.
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 | 0.000 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".