Better Phonological Short-Term Memory Is Linked to Improved Cortical Memory Representations for Word Forms and Better Word Learning
Bibliographic record
Abstract
Language learning relies on both short-term and long-term memory. Phonological short-term memory (pSTM) is thought to play an important role in the learning of novel word forms. However, language learners may differ in their ability to maintain word representations in pSTM during interfering auditory input. We used magnetoencephalography (MEG) to investigate how pSTM capacity in better and poorer pSTM groups is linked to language learning and the maintenance of pseudowords in pSTM. In particular, MEG was recorded while participants maintained pseudowords in pSTM by covert speech rehearsal, and while these brain representations were probed by presenting auditory pseudowords with first or third syllables matching or mismatching the rehearsed item. A control condition included identical stimuli but no rehearsal. Differences in response strength between matching and mismatching syllables were interpreted as the phonological mapping negativity (PMN). While PMN for the first syllable was found in both groups, it was observed for the third syllable only in the group with better pSTM. This suggests that individuals with better pSTM maintained representations of trisyllabic pseudowords more accurately during interference than individuals with poorer pSTM. Importantly, the group with better pSTM learned words faster in a paired-associate word learning task, linking the PMN findings to language learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".