Reevaluating hMT+ and hV4 functional specialization for motion and static contrast using fMRI-guided repetitive transcranial magnetic stimulation
Bibliographic record
Abstract
Although visual areas hMT+ and hV4 are considered to have segregated functions for the processing of motion and form within dorsal and ventral streams, respectively, more recent evidence favors some functional overlap. Here we use fMRI-guided online repetitive transcranial magnetic stimulation (rTMS) to test two associated hypotheses: that area hV4 is causally involved in the perception of motion and hMT+ in the perception of static form. We use variations of a common global stimulus to test two dynamic motion-based tasks and two static form-based tasks in ipsilateral and contralateral visual fields. We find that rTMS to both hMT+ and hV4 significantly impairs direction discrimination and causes a perceptual slowing of motion, implicating hV4 in motion perception. Stimulation of hMT+ impairs motion in both visual fields, implying that disruption to one hMT+ disrupts the other with both needed for optimal performance. For the second hypothesis, we find the novel result that hV4 stimulation markedly reduces perceived contrast of a static stimulus. hMT+ stimulation also produces an effect, implicating it in static contrast perception. Our findings are the first to show that rTMS of hV4 can produce a large perceptual effect and, taken together, suggest a less rigid functional segregation between hMT+ and hV4 than previously thought.
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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 | 0.000 |
| 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 | 0.001 |
| 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".