Bibliographic record
Abstract
Biases in suprathreshold depth estimation from stereopsis have been reported over a wide variety of viewing distances, stimulus geometry, and estimation methods - particularly for virtual stimuli. These depth distortions are often attributed to incorrect scaling of binocular disparity via absolute distance. This assumption supported by the fact that physical stimuli tend to produce more accurate depth scaling, but systematic errors often persist. However, we do not just look at the world around us; we interact with objects and potentially obtain proprioceptive cues to distance. There is strong evidence that stereopsis aids actions such as reaching and grasping (Loftus et al., 2004); is the reverse also true? Here we assessed the impact of proprioceptive distance information from arm’s reach on depth estimation. We compared depth magnitude estimates before and after observers performed reaching movements in a virtual environment. Observers estimated the relative depth between a rectangle and reference frame using a pressure-sensitive strip before and after performing a reaching task. When reaching, observers used hand-held controlllers with their index finger extended. The finger tip was tracked and represented by a dot while they touched a virtual square within a reference frame presented at 50cm for a total of 60 trials. In a control experiment, observers performed this same task without reaching, using head movements alone. We found that depth estimation accuracy improved after observers engaged with the target in the reaching task. However, without reaching movements observers showed no such improvement. Further, there was no change in the precision of depth estimates after either type of task (reach or no-reach). The observed improvement in depth estimation reflects a cross-modal calibration of visual space that may be important for everyday interactions. Further, it is likely that this relationship could be exploited in immersive environments to improve accuracy of visuomotor performance.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 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".