Bibliographic record
Abstract
Depth estimation from stereopsis is biased under many viewing scenarios and for a range of estimation methods, particularly for virtual stimuli. These distortions are often attributed to misestimates of viewing distance that result in incorrect scaling of binocular disparity. The majority of research on depth scaling has considered only visual cues to distance. However, we do not just look at the world, we interact with objects and in this way may have access to proprioceptive cues to distance. There is evidence that stereopsis aids actions such as prehension; is the reverse also true? We assessed the impact of proprioceptive distance from arm’s reach on stereopsis using a ring game that is contingent on accurate absolute distance perception. Observers used hand controllers and their index finger to move rings onto a peg in a virtual environment. They completed the task as quickly as possible while avoiding touching the rings to the peg (errors were signalled via controller vibration). After each block of 5 trials observers were given feedback regarding their completion time and accuracy. To evaluate the impact of this proprioceptive experience we assessed depth magnitude estimation before and after completion of the ring task. Observers were asked to estimate the depth between a rectangle and a reference frame located at the same distance as the peg in an otherwise blank field. We found that depth estimation accuracy and scaling improved with experience. Importantly, in a follow-up experiment we found that this improvement was contingent on performing the reach. Consistent with the assumption that observers underestimate absolute distance, we found that most ring-placement errors were due to underreaches. We conclude that the improvement in depth estimation seen here reflects a cross-modal calibration of visual space that is underappreciated, but potentially important for everyday interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".