Temporal Dynamics of Selective Attention during Dichotic Listening
Bibliographic record
Abstract
The ability to selectively attend to one sound and ignore other competing sounds is essential for auditory communication. Subjects in our study detected occasional changes in the frequency of amplitude modulation in sounds presented to one ear while ignoring sounds in the other ear. Neuromagnetic source analysis revealed attention-related activity in a cortical network including primary auditory cortices, posterior superior temporal gyri, inferior parietal lobules (IPLs), inferior frontal gyri (IFG), and medial frontal gyri. Time courses of event-related magnetoencephalography responses were analyzed during the interval between stimulus presentation and behavioral response. Enhanced neural responses to targets and standards in the attended ear indicated early modulation of sensitivity in the attended sensory channel. A subsequent process of discriminative stimulus selection was indexed by a response increase over time for targets and decreasing activity for standards. Enhanced responses to deviants in the unattended ear indicated discriminative processing of unattended inputs as well, though to a lesser extent than for attended stimuli. Superior temporal gyrus, planum temporale, and the IPL were prominently involved in stimulus selection, whereas medial frontal regions were linked to initiation of behavioral responses and sustained activity in IFG suggested a role in attentional control.
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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 | 0.001 | 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".