The effects of optical compression and magnification on distance estimation
Bibliographic record
Abstract
When moving through space, both visual and non-visual information can be used to monitor distance traveled. It is important, although challenging to dissociate the relative contributions of each of these cues when both are available in natural, cue-rich environments. This study created a conflict between visual and non-visual distance cues by either magnifying (2×) or compressing (0.5×) the information contained in the optic array using spectacle-mounted lenses. The experiment took place in a long, wide hallway, relatively void of visual landmarks. Subjects (Ss) were required to view a static target in the distance (4, 6, 8, 10m) and reproduce this distance by walking. Ss experienced four optical conditions (2×, 0.5×, 1×, or no lenses) either during the visual preview (Exp 1) or during the walked response (Exp 2). In Exp 1, Ss viewed the target distance under each of the four optical conditions and produced their estimates by walking blindfolded. In Exp 2, Ss viewed the target distance without lenses and produced their estimate by walking under each of the four optical conditions. In Exp 1, when wearing the 2× lenses during visual preview, Ss produced estimates that were significantly shorter than those produced when wearing 1× or no lenses. The opposite was true when wearing the 0.5× lenses. In Exp 2, however, regardless of the optical manipulation, Ss' estimates remained essentially unchanged, thus suggesting a reliance on non-visual cues. Such findings may reflect the tendency for subjects to weigh more reliable cues higher in their final estimate.
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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 | 0.000 |
| 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".