Binaural Advantage Enhances the Mismatch Negativity and Interhemispheric Connectivity During Gap Detection
Bibliographic record
Abstract
not-yet-known not-yet-known not-yet-known unknown Binaural hearing provides a perceptual advantage in detecting brief gaps in sound, yet the neural mechanisms underlying this benefit remain poorly understood. This study examined the cortical dynamics and lateralization associated with the binaural advantage and ear advantage in auditory gap detection using event-related potentials (ERPs) and effective connectivity analysis. Sixteen normal-hearing adults were presented with monaural (left and right ear) and binaural broadband pink noise stimuli containing silent gaps of varying durations, while EEG was recorded. We analyzed the Mismatch Negativity (MMN) to assess auditory gap detection. Source-localized activity and Granger causality were analyzed across ten functionally defined scouts to evaluate cortical dynamics and effective connectivity underlying ear asymmetry and binaural advantage. Results revealed significantly larger and earlier MMN responses in the binaural condition compared to monaural presentations, with stronger activation in contralateral temporal clusters for monaural conditions. Source-localized activity and effective connectivity exploratory analyses showed an overall enhanced activation for binaural stimulation for the standard stimuli. However, despite the stronger MMN observed in the binaural difference wave, source activity revealed a pattern of binaural suppression. Connectivity analyses further showed pronounced variations originating from the left auditory cortex and temporal gyri depending on listening condition, whereas connectivity involving the right auditory cortex varied as a function of gap duration. Together, these findings suggest that the binaural advantage relies on more efficient, facilitated mechanisms, while monaural stimulation requires increased cortical activity and connectivity to support temporal discrimination.
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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".