Interocular difference thresholds are mediated by binocular differencing, not summing, channels
Bibliographic record
Abstract
Patterns in the two eyes' views that are not identical in hue or contrast often elicit an impression of luster, providing a cue for discriminating them from perfectly matched patterns. Here we attempt to determine the mechanisms for detecting interocular differences in luminance contrast, in particular in relation to the possible contributions of binocular differencing and binocular summing channels. Test patterns were horizontally oriented multi-spatial-frequency luminance-grating patterns subject to variable amounts of interocular difference in grating phase, resulting in varying degrees of local interocular contrast difference. Two types of experiment were conducted. In the first, subjects discriminated between a pedestal with an interocular difference that ranged upward from zero (i.e., binocularly correlated) and a test pattern that contained a bigger interocular difference. In the second type of experiment, subjects discriminated between a pedestal with an interocular difference that ranged downward from a maximum (i.e., binocularly anticorrelated) and a test pattern that contained smaller interocular difference. The two types of task could be mediated by a binocular differencing and a binocular summing channel, respectively. However, we found that the results from both experiments were well described by a simpler model in which a single, linear binocular differencing channel is followed by a standard nonlinear transducer that is expansive for small signals but strongly compressive for large ones. Possible reasons for the lack of involvement of a binocular summing channel are discussed in the context of a model that incorporates the responses of both monocular and binocular channels.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".