Perceptual asymmetry in stereo-transparency: The role of disparity interpolation
Bibliographic record
Abstract
We have previously described a perceptual asymmetry that occurs when viewing pseudo-transparent random element stereograms. That is, the minimum separation in depth needed to segregate two overlaid surfaces in a random-element stereogram depends on the distribution of elements across the surfaces. With the total element density fixed, significantly larger inter-plane disparities are required for perceptual segregation of overlaid surfaces when the front surface has fewer elements than the back surface than vice versa. In the experiments described here we test the hypothesis that this perceptual asymmetry reflects a fundamental difference in signal strength for the front and back surfaces which results from disparity interpolation. That is, we propose that the blank regions between elements are assigned to the back plane, making it appear opaque. We tested this hypothesis in a series of experiments and find that: the total element density in the stimulus does not affect the asymmetry the perceived relative density of the two surfaces shows a similar asymmetry manipulations favouring perceptual assignment of the spaces into surfaces other than the two overlaid element surfaces reduces the asymmetry. We propose that the interpolation of the spaces between the elements defining the surfaces is mediated by a network of inter-neural connections; excitatory within-disparity, and inhibitory across disparity. Our data suggest that the strength of the inhibitory connections is modulated according to mid-level figure ground assignment. We are using our psychophysical results to inform the development of a computational model of this network.
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 | 0.001 |
| 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".