Dichoptic difference thresholds for familiar and unfamiliar transformations of real scenes
Bibliographic record
Abstract
Aim. We have previously shown that sensitivity to photometric (colour and luminance) transformations in images of real scenes is lower for familiar compared to unfamiliar transformations. This suggests a normalization, or gain reduction, of familiar transformations. At what stage does this normalization occur? We tested whether it occurred before or after the stage of binocular combination by measuring dichoptic difference thresholds, or DDTs (the DDT is the just detectable between-eye difference in a binocularly superimposed image-pair), for photometrically-transformed real scenes, and comparing these with conventional image-difference thresholds. Methods. Stimuli were fifty calibrated color photographs of real scenes. The chromaticity and saturation of every image pixel was represented as a vector in a modified version of the MacLeod-Boynton color space, and could be translated, rotated, compressed or randomly repositioned within that color space. The dichoptic image pairs were presented via a modified Wheatstone stereoscope, while the conventional image pairs were presented with the stereoscope removed. All thresholds were measured using 2AFC. Results. DDTs, unlike the conventional image difference thresholds, were more or less constant, i.e. unaffected by familiarity. Conclusion. The result suggests that the normalization process happens after the stage of binocular combination.
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 | 0.001 |
| 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) | 0.005 | 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".