Attention to Simultaneous Unrelated Auditory and Visual Events: Behavioral and Neural Correlates
Bibliographic record
Abstract
The cognitive and neural bases of the ability to focus attention on information in one sensory modality while ignoring information in another remain poorly understood. We hypothesized that bimodal selective attention results from increased activity in corresponding sensory cortices with a suppression of activity in non-corresponding sensory cortices. In a functional magnetic resonance imaging (fMRI) study, we presented melodies and shapes alone (unimodal) or simultaneously (bimodal). Subjects monitored for changes in an attended modality while ignoring the other. Subsequently, memory for both attended and unattended stimuli was tested. Subjects remembered attended stimuli equally well in unimodal and bimodal conditions, and significantly better than ignored stimuli in bimodal conditions. When a subject focused on a stimulus, the blood-oxygen-level-dependent (BOLD) response increased in sensory cortices corresponding to that modality in both unimodal and bimodal conditions. Additionally, the BOLD response decreased in sensory cortices corresponding to the non-presented modality in unimodal conditions and the unattended modality in bimodal conditions. We conclude that top-down attentional effects modulate the interaction of sensory cortical areas by gating sensory input. This interaction between sensory cortices enhances processing of one modality at the expense of the other during selective attention, and subsequently affects memory encoding.
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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".