Background Music and Memory in Mild Cognitive Impairment: The Role of Interindividual Differences
Bibliographic record
Abstract
BACKGROUND: Recent research has shown that background music may improve memory consolidation and retrieval. Nevertheless, in the clinical conditions preceding dementia such as mild cognitive impairment (MCI), there is no current evidence speaking to what effect background music during memory tasks has on impaired cognition. OBJECTIVE: Across three experiments, we investigated if background music is able to improve memory performance, the most impacted cognitive domain in amnestic MCI. METHODS: We tested the effect of background music by using a face recognition memory task in patients with amnestic MCI. In Experiment 1, we tested the effect of background music on memory when it was played solely during an encoding phase. In Experiment 2, we explored effects of background music when played during both encoding and recognition phases. In Experiment 3, we explored the role of musically induced arousal on memory. RESULTS: The main finding from these three experiments was that background music played during a memory task did not improve or worsen participant performance. However, when exposed to high-arousal music, memory performance was predicted by individual mood regulation. For low-arousal music conditions, there was a negative relationship between rating scores for music pleasantness and performance on the memory task. CONCLUSION: Our results suggest that the benefits of background music on memory in individuals with MCI are modulated by interindividual preferences towards music.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".