Diverse ways of holding verbal information in mind revealed with functional Magnetic Resonance Imaging and Transcranial Magnetic Stimulation: Individual differences in left anterior parietal cortex
Bibliographic record
Abstract
Cognitive neuroscience has identified brain systems that reliably underpin specific abilities, including verbal short-term memory. However, there is no simple mapping between anatomy and function; moreover, brains are differently organised across people. In two functional magnetic resonance imaging studies, and using transcranial magnetic brain stimulation to test causality, we characterised differences in the neural processes supporting verbal short-term memory in healthy participants who varied in their use of semantic information. In group analyses, left anterior inferior parietal cortex showed the expected pattern for a "phonological buffer" — this site was responsive to phonological tasks and verbal short-term memory for meaningless items. However, the functions of this site differed across the sample; individuals with the strongest "semantic reliance" in short-term memory (showing higher imageability effects and poorer nonword performance) showed weaker phonological responses. They also showed more activation when maintaining meaningful word sequences in short-term memory, compared with nonwords. In these semantically-reliant participants, left anterior inferior parietal cortex showed stronger functional connectivity to limbic and default mode regions during nonword rehearsal. While inhibitory stimulation to phonologically responsive regions disrupted nonwords more than words in people with good phonological short-term memory, these effects were reversed when verbal short-term memory was more semantically reliant. These findings show that left anterior inferior parietal cortex supports the maintenance of different kinds of verbal information across individuals.
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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 | 0.001 | 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) | 0.002 | 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".