Neurophysiological Evaluation of Right-Ear Advantage During Dichotic Listening
Bibliographic record
Abstract
Right-ear advantage refers to the observation that when two different speech stimuli are simultaneously presented to both ears, listeners report stimuli more correctly from the right ear than the left. It is assumed to result from prominent projection along the auditory pathways to the contralateral hemisphere and the dominance of the left auditory cortex for the perception of speech elements. Our study aimed to investigate the role of attention in the right-ear advantage. We recorded magnetoencephalography data while participants listened to pairs of Japanese two-syllable words (namely, "/ta/ /ko/" or "/i/ /ka/"). The amplitudes of the stimuli were modulated at 35 Hz in one ear and 45 Hz in the other. Such frequency-tagging allowed the selective quantification of left and right auditory cortex responses to left and right ear stimuli. Behavioral tests confirmed the right-ear advantage, with higher accuracy for stimuli presented to the right ear than to the left. The amplitude of the auditory steady-state response was larger when attending to the stimuli compared to passive listening. We detected a correlation between the attention-related increase in the amplitude of the auditory steady-state response and the laterality index of behavioral accuracy. The right-ear advantage in the free-response dichotic listening was also found in neural activities in the left auditory cortex, suggesting that it was related to the allocation of attention to both ears.
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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) | 0.002 | 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".