Neural substrates of individual differences in learning generalization via combined brain stimulation and multitasking training
Bibliographic record
Abstract
A pervasive limitation in cognition is reflected by the performance costs we experience when attempting to undertake two tasks simultaneously. While training can overcome these multitasking costs, the more elusive objective of training interventions is to induce persistent gains that transfer across tasks. Combined brain stimulation and cognitive training protocols have been employed to improve a range of psychological processes and facilitate such transfer, with consistent gains demonstrated in multitasking and decision-making. Neural activity in frontal, parietal, and subcortical regions has been implicated in multitasking training gains, but how the brain supports training transfer is poorly understood. To investigate this, we combined transcranial direct current stimulation of the prefrontal cortex and multitasking training, with functional magnetic resonance imaging in 178 participants. We observed transfer to a visual search task, following 1 mA left or right prefrontal cortex transcranial direct current stimulation and multitasking training. These gains persisted for 1-month post-training. Notably, improvements in visual search performance for the right hemisphere stimulation group were associated with activity changes in the right hemisphere dorsolateral prefrontal cortex, intraparietal sulcus, and cerebellum. Thus, functional dynamics in these task-general regions determine how individuals respond to paired stimulation and training, resulting in enhanced performance on an untrained task.
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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) | 0.002 | 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".