Stimulating prefrontal cortex facilitates training transfer by increasing representational overlap
Bibliographic record
Abstract
Abstract Difficulties in multitasking may be the price humans pay for our ability to generalise learning to new tasks. Mitigating these costs through training has been associated with reduced overlap of constituent task representations within a task-related brain network. Transcranial direct current stimulation (tDCS), which can modulate neural activity, has shown promise in generalising training gains. Whether tDCS influences the changes in task-associated representations to produce such training generalisation remains unexplored. Here, we paired prefrontal cortex tDCS with multitasking training, and collected functional magnetic resonance imaging data pre- and post- training. We found that 1mA tDCS enhanced visual search performance, and using machine learning to assess the overlap of brain activity related to the training, show that these generalised gains were predicted by changes in classification accuracy for patterns of frontal, parietal, and cerebellar activity in participants who received left prefrontal cortex stimulation. Thus, prefrontal cortex tDCS interacts with training related changes in task representations, potentially driving the generalisation of learning.
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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) | 0.003 | 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".