Spans attributed to short-term memory are explained by sensitivity to long-term statistics in both musicians and individuals with dyslexia
Bibliographic record
Abstract
Abstract Reduced short-term memory (STM) of individuals with dyslexia (IDDs) and enhanced STM of musicians are well documented, yet their causes are disputed. We hypothesized that their STMs reflect their sensitivities to accumulative long-term stimuli statistics. Indeed, when performing an STM task, IDDs had reduced benefit from syllable frequency, whereas musicians manifested an opposite effect, compared to controls. Interestingly, benefit from sequence-repetition did not significantly differ between groups, suggesting that it relies on different mechanisms. To test the generality of this separation across populations, we recruited a group of good-readers, whose native language contains a smaller fraction of the high-frequency syllables. Their span for these “high-frequency” syllables was small, yet their benefit from sequence-repetition was adequate. These experiments indicate that sensitivity to long-term stimuli distribution, and not to sequential repetition, is reduced in IDDs and enhanced in musicians, and this accounts for differences in their STM performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".