Investigating the temporal dynamics of dichoptic masking
Bibliographic record
Abstract
In the standard model of binocular combination, the inputs from the two eyes not only sum together but also suppress each other. One way to characterize such interocular suppression is through dichoptic masking, a phenomenon in which the ability to detect a target presented to one eye is reduced by a noise mask presented to the other eye. However, the temporal dynamics of this interocular suppression are not fully understood. In particular, the relationships between simultaneous masking (mask and target presented simultaneously), backward masking (mask presented after target), and forward masking (mask presented before target) are currently unknown for dichoptic stimuli. We hypothesized that simultaneous masking would produce the strongest masking effect since, in this masking condition, the target is never shown in isolation. To test this hypothesis, we employed a dichoptic suppression paradigm using a two-alternative-force-choice task. Stimuli were displayed on a passive 3D screen. Participants indicated the orientation of a target grating presented to one eye while a pink noise mask was presented to the other eye at the same spatial location but at a different time. The contrast of the target was adjusted using a 2-up 1-down staircase. Thirteen interstimulus intervals (-12, -8, -5, -3, -2, -1, 0, 1, 2, 3, 5, 8, and 12 frames) between the mask and the target were used to individually investigate the three masking types. Our results revealed the presence of a masking effect for all three masking types. Surprisingly, the strongest masking effect was not found in the simultaneous masking condition but rather in the backward masking condition. These findings provide novel insight into the temporal dynamics of dichoptic masking, particularly in revealing the strong effect of backward masking, and may have implications in the general understanding of amblyopic suppression and in the development of suppression-related treatments for amblyopia.
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) | 0.001 | 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".