Electrical Neuroimaging of Voluntary Audiospatial Attention: Evidence for a Supramodal Attention Control Network
Bibliographic record
Abstract
Previous attempts to investigate the supramodal nature of attentional control have focused primarily on identifying neuroanatomical overlap in the frontoparietal systems activated during voluntary shifts of spatial attention in different sensory modalities. However, the activation of the same neural structures is insufficient evidence for a supramodal system, as the same brain regions could interact with one another in very different ways during shifts of attention in different modalities. Thus, to explore the similarity of the functional networks, it is necessary to identify the neural structures involved and to examine the timing and sequence of activities within the network. To this end, we used an electrical neuroimaging technique to localize the neural sources of electroencephalographic signals recorded from human subjects during audiospatial shifts of attention and to examine the timing and sequence of activities within several regions of interest. We then compared the results to an analogous study of visuospatial attention shifts. Similar frontal and parietal regions were activated during visual and auditory shifts of attention, and the timing of activities within these regions was nearly identical. Following this modality-independent sequence of attention-control activity, activity in the relevant sensory cortex was enhanced in anticipation of the response-relevant target. These results are consistent with the hypothesis that a single supramodal network of frontal and parietal regions mediates voluntary shifts of spatial attention and controls the flow of sensory information in modality-specific sensory pathways.
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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 | 0.002 |
| 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".