Expectation of Sensory Stimulation Modulates Brain Activation during Visual Motion Stimulation
Bibliographic record
Abstract
The differential effects of visual hemifield motion stimulation during fixation of a stationary target were compared under two conditions: fixation straight ahead without any further instructions and fixation straight ahead with attention shifted to the "dark hemifield." Data from nine right-handed volunteers revealed that striate and extrastriate right hemispheric visual areas exhibited larger activations during left hemifield motion stimulation when attention was shifted to the right dark hemifield. Montreal Neurological Institute (MNI) coordinates (26, -98, -4) of the additional clusters activated in the latter condition corresponded best to the kinetic occipital region, which is known to process both shape and motion information, and to parts of area V3 posterior to V3A, which has been shown repeatedly to mediate motion perception. A simple computational model of transhemispheric visuovisual interaction is proposed. The basic mechanism of this model is a central predictor formed by a feedback loop that detects a mismatch between input to the two hemispheres. Predicted stimulation is then compared with the actual input. If the sensed motion of a visual hemifield is larger than the predicted net motion in the model, activation of the respective neural population is increased; conversely, a smaller actual motion causes less activation.
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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".