Long-Term Musical Training Alters Tactile Temporal-Order Judgment
Bibliographic record
Abstract
Long-term musical training is an enriched multisensory training environment that can alter uni- and multisensory substrates and abilities. Amongst these altered abilities are faster reaction times for simple and complex sensory tasks. The crossed arm temporal-order judgement (TOJ) task is a complex tactile task in which TOJ error rate increases when arms are crossed. Reaction times (RTs) for this task are typically proportionate to the difficulty of the stimulus onset asynchrony (SOA) and increase more when the arms are crossed than when uncrossed. The objective of this study was to study the impact of musical training on RTs and accuracy for the crossed arm TOJ task. Seventeen musicians and 20 controls were tested. Musicians had significantly faster RTs for all crossed arm conditions and half of the uncrossed conditions. However, musicians had significantly more TOJ errors for the crossed posture. We speculate that faster musician TOJ RTs leave little time to consolidate conflicting internal and external task-related information when crossing the arms, leading to increased incorrect responses. These results provide novel insights on the potential mechanisms underlying the increased TOJ error rates when arms are crossed. Moreover, they add to the growing literature of altered sensory ability in musicians and propose an unexpected consequence of faster reaction times.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".