The role of working memory in dichotic-listening studies of auditory laterality
Bibliographic record
Abstract
We present data related to the role of working memory in dichotic-listening studies of speech lateralization using consonant–vowel syllable stimuli. A working-memory procedure was actually used in the pioneering dichotic-listening studies by Doreen Kimura in 1960, a fact that was forgotten in later dichotic-listening studies, exclusively focusing on the perceptual aspects of speech sound lateralization. Capitalizing on the original Kimura (1961a Kimura, D. 1961a. Cerebral dominance and the perception of verbal stimuli. Canadian Journal of Psychology, 15: 166–171. [Crossref], [Web of Science ®] , [Google Scholar], 1961b Kimura, D. 1961b. Some effects of temporal-lobe damage on auditory perception. Canadian Journal of Psychology, 15: 156–165. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) studies, we hypothesized that an increase in working-memory load leads to an amplified right-ear advantage (REA) in the dichotic-listening task. A total of 30 participants completed a dichotic-listening task including three working-memory load conditions, each consisting of trials of 3, 4, and 5 dichotically presented letter pairs. Results confirmed an enhanced REA as working-memory load increased. This right-ear effect increased significantly from 3 to 4 stimulus pairs and leveled off with the 5th pair. In addition, the assumption was tested that, within a single load condition, the REA appears mainly in late serial input positions. A detailed analysis of the results revealed that only late positions contributed to the overall REA. However, the highest load condition (5 letter pairs) also produced significant ear differences in the early part of the input position curve. The mechanisms likely to be responsible for these results are discussed in terms of top-down and bottom-up processes in hemispheric asymmetry.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".