Individual differences in working memory capacity and their effect on speech processing.
Bibliographic record
Abstract
Though tests of working memory (WM) correlate with scales of language development, it is unclear how WM capacity relates to spoken-language processing. However, Gilbert et al. (2014) have shown that listeners perceptually chunk speech in temporal groups (TGs) and that the span these TGs influences memory of heard items. Assuming that WM capacity links to this processing of speech in groups, listeners with the highest WM spans would be better at recalling items from long TGs. To examine this, we presented two sets of stimuli (utterances and sequences of meaningless syllables) containing long TGs. After each stimuli, listeners had to determine if a target item was previously heard. An analysis using GLME models showed that correct recognition memory of items heard in utterances was significantly better for listeners with high WM spans than for listeners with smaller spans. The effect was marginally significant for sequences of nonsense syllables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".