Distinct Effects of Memory Retrieval and Articulatory Preparation when Learning and Accessing New Word Forms
Bibliographic record
Abstract
Temporal and frontal activations have been implicated in learning of novel word forms, but their specific roles remain poorly understood. The present magnetoencephalography (MEG) study examines the roles of these areas in processing newly-established word form representations. The cortical effects related to acquiring new phonological word forms during incidental learning were localized. Participants listened to and repeated back new word form stimuli that adhered to native phonology (Finnish pseudowords) or were foreign (Korean words), with a subset of the stimuli recurring four times. Subsequently, a modified 1-back task and a recognition task addressed whether the activations modulated by learning were related to planning for overt articulation, while parametrically added noise probed reliance on developing memory representations during effortful perception. Learning resulted in decreased left superior temporal and increased bilateral frontal premotor activation for familiar compared to new items. The left temporal learning effect persisted in all tasks and was strongest when stimuli were embedded in intermediate noise. In the noisy conditions, native phonotactics evoked overall enhanced left temporal activation. In contrast, the frontal learning effects were present only in conditions requiring overt repetition and were more pronounced for the foreign language. The results indicate a functional dissociation between temporal and frontal activations in learning new phonological word forms: the left superior temporal responses reflect activation of newly-established word-form representations, also during degraded sensory input, whereas the frontal premotor effects are related to planning for articulation and are not preserved in noise.
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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 | 0.001 |
| 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) | 0.001 | 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".