Attentional Modulation of Neuromagnetic Evoked Responses in Early Human Visual Cortex and Parietal Lobe following a Rank-Order Rule
Bibliographic record
Abstract
Top-down voluntary attention modulates the amplitude of magnetic evoked fields in the human visual cortex. Whether such modulation is flexible enough to adapt to the demands of complex tasks in which abstract rules must be applied to select a target in the presence of distracters remains unclear. We recorded brain neuromagnetic activity using whole-head magnetoencephalography in 14 human subjects during a rule-guided target selection task, and applied event-related Synthetic Aperture Magnetometry to image instantaneous changes in neuromagnetic source activity throughout the brain. During the task subjects selected one of two stimuli (the target) and ignored the other (the distracter) based on a color-rank rule (color 1 > color 2 > color 3). Our results revealed that in early visual color-sensitive areas and the parietal cortex visual stimuli evoke activity that scaled following the rank-order rule. This effect was stronger and occurred later in the parietal lobe (~200 ms after target/distracter onset) relative to early visual areas (~180 ms). Moreover, we found that transient changes in the target's motion direction evoked stronger responses relative to similar changes in the distracter at ~180 ms from change onset in contralateral areas hMT+/V5. These results suggest that during target selection and allocation of attention to a stimulus, top-down signals adjust their intensity following complex selection rules according to the organism's priorities, thereby differentially modulating neuromagnetic activity across visual cortical areas.
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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".