Integration of Visual Motion Signals in Reduced Visual Conditions
Bibliographic record
Abstract
Purpose: Neural sensory systems continuously tailor themselves to adapt to changes in the surrounding environment. In motion adaptation, a certain period of exposure to consistent motion in one direction (inducer) will alter the perceived direction of motion of the following stimulus. Depending on the timescale of the inducer, two opposite adaptation phenomena can be observed: motion priming for brief inducers, and motion aftereffect for longer inducers. The aim of this study was to investigate how the integration of motion signals during adaptation is affected by externally reduced visual conditions, such as luminance, contrast, and spatial frequency. We then considered how this would apply to the naturally impaired visual system in amblyopia. Methods: We addressed this question by taking advantage of a visual illusion, the High-phi illusion. We measured the High-phi transition point when manipulating the visual conditions (contrast and luminance), the targeted subpopulations of neurons (by varying spatial frequency), and the integration properties of the visual system by changing the viewing conditions (monocular viewing, binocular viewing, and testing amblyopic participants). Results: We found a larger transition point under high spatial frequency, low luminance, low contrast, and monocular viewing conditions. We then propose a model of temporal integration, for the motion signals, that accurately describes those effects. Conclusions: Finally, we validated our model by testing amblyopic participants and demonstrating that the amblyopic visual system exhibits a larger High-phi transition point, thereby characterizing slower temporal integration. Overall, our results show that the integration of visual motion energy could switch adaptation from priming to aftereffect.
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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) | 0.002 | 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".