The contribution of binocular depth information to the perceived size of 3D shapes
Bibliographic record
Abstract
Studies of size perception have tended to focus on the effect of distance and used simple 2D stimuli. Factors affecting the perceived size of volumetric 3D shapes have received less attention. Here, we evaluate the contribution of monocular and binocular depth information to size perception, during passive viewing and active interaction with the stimuli. Using virtual reality (VR) we presented a virtual shape-posting toy. Targets were 3D shapes (triangle, pentagon, square, and quatrefoil) and on each trial, one target appeared alongside a box with identically shaped slots. Participants indicated if the target was larger or smaller than the corresponding slot in a 2AFC task. Shape size varied according to the method of constants; the psychometric data were fit with cumulative normal distributions to compute JNDs and PSEs. We assessed the effects of stereopsis and object motion in two experiments. In Experiment 1 (N=28) the shapes were viewed monocularly or binocularly and were stationary or could be picked up and rotated. In Experiment 2 (N=26) we evaluated the effect of two types of motion (passive rotation vs active movement) and monocular vs. binocular viewing. In Experiment 2, positional data for both head movements and object trajectories were collected. We found that observers discriminated size accurately across all test conditions; PSEs showed no consistent bias. Overall, binocular judgements were more precise (smaller JNDs) than monocular judgements. Surprisingly, in the monocular conditions, discrimination performance worsened when participants interacted with the object. Analysis of target positions during trials showed that observers did not adopt different strategies depending on the depth cues available (e.g. they did not bring monocularly viewed objects closer). Our results underscore the importance of binocular depth information in perception of 3D object size, even when motion or active interaction could theoretically enhance size judgements.
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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 | 0.003 |
| 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 | 0.001 |
| 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".