The Contributions of Static Visual Cues, Nonvisual Cues, and Optic Flow in Distance Estimation
Bibliographic record
Abstract
By systematically varying cue availability in the stimulus and response phases of a series of same-modality and cross-modality distance matching tasks, we examined the contributions of static visual information, idiothetic information, and optic flow information. The experiment was conducted in a large-scale, open, outdoor environment. Subjects were presented with information about a distance and were then required to turn 180 before producing a distance estimate. Distance encoding and responding occurred via: (i) visually perceived target distance, or (ii) traversed distance through either blindfolded locomotion or during sighted locomotion. The results demonstrated that subjects performed with similar accuracy across all conditions. In conditions in which the stimulus and the response were delivered in the same mode, when visual information was absent, constant error was minimal; whereas, when visual information was present, overestimation was observed. In conditions in which the stimulus and response modes differed, a consistent error pattern was observed. By systematically comparing complementary conditions, we found that the availability of visual information during locomotion (particularly optic flow) led to an 'under-perception' of movement relative to conditions in which visual information was absent during locomotion.
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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 | 0.004 |
| 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 | 0.000 |
| 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".