Does contrast adaptation influence the Pulfrich phenomenon?
Bibliographic record
Abstract
The Pulfrich phenomenon is the visual illusory perception of motion-in-depth caused by a monocular reduction of luminance. Recently we showed that such illusion could also be caused after short-term monocular deprivation or “patching”, presumably caused by a monocular contrast-gain change. Therefore, to investigate this mechanism further, in this study we wanted to determine if it is possible to induce the Pulfrich phenomenon through contrast adaptation. We used a 3D passive screen using 3D glasses to adapt each eye to high contrast separately and display the Pulfrich stimulus. In each trial, participants were exposed to a 3-second-long contrast-adapting stimulus followed by structure-from-motion defined rotating cylinder made of Gabor patches. Adaptator contrast was 100% and stimulus contrast was either 100% or 15%. The differences between the right-eye-adapted and left-eye-adapted points of subjective equality (PSE) were then used to reveal the occurrence of the Pulfrich phenomenon. The results support the idea that adapting one eye to high contrast creates an interocular delay as the PSE values differed significantly after each eye adaptation. All the PSEs arising from left eye adaptation were significantly higher than those from right eye adaptation (p<0.001 for both 100% and 15% stimulus contrast), indicating a link of causation between which eye gets adapted and the direction of the phase shift. Furthermore, the gaps between the PSE values increased when reducing the contrast level of the Gabor patches from 100% to 15% (p<0.001). Contrast adaptation seems to influence the Pulfrich phenomenon through a unilateral increase in visual processing, creating an interocular delay. This finding hints at a key relationship between contrast gain control and the Pulfrich phenomenon.
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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".