Training of Working Memory Impacts Structural Connectivity
Bibliographic record
Abstract
Working memory is the limited capacity storage system involved in the maintenance and manipulation of information over short periods of time. Individual capacity of working memory is associated with the integrity of white matter in the frontoparietal regions. It is unknown to what extent the integrity of white matter underlying the working memory system is plastic. Using voxel-based analysis (VBA) of fractional anisotropy (FA) measures of fiber tracts, we investigated the effect of working memory training on structural connectivity in an interventional study. The amount of working memory training correlated with increased FA in the white matter regions adjacent to the intraparietal sulcus and the anterior part of the body of the corpus callosum after training. These results showed training-induced plasticity in regions that are thought to be critical in working memory. As changes in myelination lead to FA changes in diffusion tensor imaging, a possible mechanism for the observed FA change is increased myelination after training. Observed structural changes may underlie previously reported improvement of working memory capacity, improvement of other cognitive functions, and altered functional activity following working memory training.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".