Interocular difference detection is facilitated as well as suppressed by surround masks
Bibliographic record
Abstract
Interocular (between-eye) contrast differences (ICDs) elicit an impression of lustre, a cue to their detection. Recently Kingdom, Jennings & Georgeson (JOV, 18(5):9, 2018) provided evidence that ICD detection is an adaptable dimension of vision, in keeping with the idea that ICDs are detected by a dedicated binocular differencing channel, termed B-. Here we study the properties of the putative channel using surround masking rather than adaptation. Observers were required to detect ICDs in the form of interocular phase differences between horizontally-oriented 0.5 cpd test luminance gratings in a circular 2 deg diameter window. The test gratings were surrounded by 0.5 cpd horizontally-oriented luminance mask gratings that were interocularly either in-phase or anti-phase. ICD thresholds for a 10% contrast test increased with the contrast of the anti-phase surround, indicating surround suppression. With the in-phase surrounds ICD thresholds decreased gradually with contrast, indicating surround facilitation. The results are consistent with a B- channel that is subject to inhibition from surrounds containing interocular differences but which benefits from surrounds that are interocularly matched, suggestive of a mechanism that plays a role in perceptually segregating regions with and without interocular differences.
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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 | 0.001 |
| 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 | 0.001 |
| 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".