Effects of transcranial direct current stimulation combined with listening to preferred music on memory in older adults
Bibliographic record
Abstract
Listening to autobiographically-salient music (i.e., music evoking personal memories from the past), and transcranial direct current stimulation (tDCS) have each been suggested to temporarily improve older adults' subsequent performance on memory tasks. Limited research has investigated the effects of combining both tDCS and music listening together on cognition. The present study examined whether anodal tDCS stimulation over the left dorsolateral prefrontal cortex (2 mA, 20 min) with concurrent listening to autobiographically-salient music amplified subsequent changes in working memory and recognition memory in older adults than either tDCS or music listening alone. In a randomized sham-controlled crossover study, 14 healthy older adults (64-81 years) participated in three neurostimulation conditions: tDCS with music listening (tDCS + Music), tDCS in silence (tDCS-only), or sham-tDCS with music listening (Sham + Music), each separated by at least a week. Working memory was assessed pre- and post-stimulation using a digit span task, and recognition memory was assessed post-stimulation using an auditory word recognition task (WRT) during which electroencephalography (EEG) was recorded. Performance on the backwards digit span showed improvement in tDCS + Music, but not in tDCS-only or Sham + Music conditions. Although no differences in behavioural performance were observed in the auditory WRT, changes in neural correlates underlying recognition memory were observed following tDCS + Music compared to Sham + Music. Findings suggest listening to autobiographically-salient music may amplify the effects of tDCS for working memory, and highlight the potential utility of neurostimulation combined with personalized music to improve cognitive performance in the aging population.
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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) | 0.001 | 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".