Dichoptic imbalance of luminance affects the phase component of steady-state MEG signals
Bibliographic record
Abstract
Interocular interaction in a normal visual system occur under dichoptic conditions when contrast and luminance are imbalanced between the eyes. Psychophysically such interaction are well described by a contrast normalization model. However the neural processing underlying such interactions within the visual cortex are still unclear. We set to investigate how an interocular imbalance in contrast or luminance affects visual processing. We used magnetoencephalography to record SSVEP and fMRI to obtain individual retinotopic maps that we used for source computation for each participant. Monocular and dichoptic stimuli (binary noise patterns) were frequency-tagged at 4 and 6 Hz (contrast modulated) and presented at a range of contrasts from 0 to 32%. Monocularly, we reduced the luminance by placing a 1.5 ND filter over one eye in the maximal contrast condition. We used amplitude component of SSVEP averaged per region of interest to describe monocular responses and dichoptic interaction. Phase component (phase angle and its variance) of the strongest vertex per region of interest was used to obtain the temporal estimate of the response. Monotonic increase in SSVEP amplitude reflected the experimental change in contrast from 0 to 32% in V1, V2, V3 and V4. Interocular suppression was seen in both eyes as a decrease in SSVEP amplitude and was well approximated by the normalization model (r2 =0.9). Reducing the mean luminance delayed monocular processing by approximately 35 ms across the areas of interest and increased phase variance. A dramatic increase in phase variance was observed in dichoptic condition for a monocular reduction in luminance. Delaying monocular input also increased suppression from the fellow-fixing eye to the delayed filtered eye and a release of suppression was seen in the opposite direction. Temporally filtering the monocular input prior to binocular combination stage of the normalization model provided a good fit to our experimental data. Meeting abstract presented at VSS 2016
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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 | 0.000 |
| 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".