The Effect of Stimulus Size on Stereoscopic Fusion Limits and Response Criteria
Bibliographic record
Abstract
The stereoscopic fusion limit denotes the largest binocular disparity for which a single fused image is perceived. With few exceptions, this limit has been measured with thin lines or bars employing a criterion of single or double images. We wondered if the fusion limit and criterion for fusion (for example, diplopia or lustre) changed systematically as a function of bar width. We first measured horizontal and vertical disparity fusion limits for thin bars and for the edge of an extended surface. Seven observers increased or decreased the disparity of the target and responded when fusion broke or reappeared, respectively, using either the criterion of diplopia or lustre. Fusion limits were equal for thin bars and extended surfaces in both horizontal and vertical disparity conditions. We next measured fusion limits for a range of bar widths and instructed four observers from experiment 1 to indicate which criterion they employed on each trial. Fusion limits were constant across all stimulus widths. However, there was a sharp change in criterion from diplopia to luster when the bar width was approximately twice the fusion limit. Stereoscopic fusion limits do not depend on stimulus size, but the criterion for fusion does.
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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 | 0.002 | 0.019 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".