Investigation of working memory networks for verbal and rhythmic stimuli
Bibliographic record
Abstract
Abstract Auditory working memory is often conceived of as a unitary capacity, with memory for different auditory materials (syllables, pitches, rhythms) thought to rely on similar neural mechanisms. One spontaneous behavior observed in working memory studies is ‘chunking’. For example, individuals often recount digit sequences in groups, or chunks, of 3 to 4 digits, and this chunking improves performance. Chunking may also operate in musical rhythm, with beats acting as chunk boundaries for tones in rhythmic sequences. Similar to chunking, beat-based structure in rhythms also improves performance. Thus, beat processing may rely on the same mechanisms that underlie chunking of verbal material. The current fMRI study examined whether beat perception is a type of chunking, measuring brain responses to chunked and unchunked letter sequences relative to beat-based and nonbeat-based rhythmic sequences. Participants completed a sequence discrimination task, and comparisons between stimulus encoding, maintenance, and discrimination were made for both rhythmic and verbal sequences. Overall, rhythm and verbal working memory networks overlapped substantially. When comparing rhythmic and verbal conditions, rhythms activated basal ganglia, supplementary motor area, and anterior insula, compared to letter strings, during encoding and discrimination. Letter strings compared to rhythms activated bilateral auditory cortex during encoding, and parietal cortex, precuneus, and middle frontal gyri during discrimination. Importantly, there was a significant interaction in the basal ganglia during encoding: activation for beat-based rhythms was greater than for nonbeat-based rhythms, but verbal chunked and unchunked conditions did not differ. The significant interaction indicates that beat perception is not simply a case of chunking, suggesting a dissociation between beat processing and grouping mechanisms that warrants further exploration.
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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 | 0.002 |
| 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) | 0.002 | 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".