Hemifield-Specific Motion Extrapolation Reveals Limits of Interhemispheric Integration
Bibliographic record
Abstract
The accurate perception of moving objects is a fundamental challenge for the visual system, which must compensate for neural processing delays. Motion extrapolation is a proposed mechanism whereby the brain predicts an object's future position. We investigated how an object's motion history shapes its perceived position using the flash-jump illusion in humans of either sex, in which a brief color change in a moving bar is mislocalized as further along the direction of motion. Across two experiments, we found that longer preceding motion sequences improved localization accuracy, consistent with motion adaptation. This effect occurred regardless of whether motion continued after the flash. Notably, mislocalization transiently reappeared as the object crossed the vertical midline, suggesting that motion adaptation and motion extrapolation operate independently between within each hemifield. Manipulating the length of the sequence in each hemifield in Experiment 2 confirmed that this adaptation is spatially confined to each hemifield, with limited interhemispheric transfer. The results align with a Bayesian framework in which the brain integrates signals from both hemispheres, with midline crossings triggering a shift from adapted to unadapted neural populations. We identify motion extrapolation, supported by hemifield-specific adaptation in area MT and integration in area MST, as the putative mechanism behind these midline discontinuities. This work reframes smooth pursuit not just as a tracking behavior but as a solution to overcome limitations of interhemispheric motion processing.
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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".