Musical training shapes spatial cognition
Bibliographic record
Abstract
Spatial cognition refers to the general ability to represent space, manipulate spatial information, and use concepts relating to this notion. Recent evidence suggests that brief multisensory training might improve this process, but the impact of long-term and intensive multisensory training on spatial cognition remains unexplored. The present study aims to examine the impact of musical training, a multisensory training involving the auditory function, on a body disorientation task with and without auditory cues. Thirty-eight participants were recruited and divided into two groups based on their musical experience (musicians and controls). They were asked to complete the Fukuda-Unterberger stepping task under four conditions: without any auditory input, and with auditory input originating at 0°, 45°, and 90° azimuth. This task is well known to create body disorientation as over the course of the task, the body position in space changes, unbeknownst to the participant. Results suggest that musicians are less susceptible to body disorientation, as measured in the stepping task, both in the absence and in the presence of auditory stimuli. The findings extend beyond recent research indicating that musical training can influence a wide range of auditory abilities, suggesting that it could also modulate a broader cognitive process, specifically spatial cognition. The demonstration that extensive multisensory training significantly enhances spatial cognition has relevance for rehabilitation in clinical settings.
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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) | 0.004 | 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".