Bimodal cueing can facilitate rhythmic training for sequential upper-limb movements
Bibliographic record
Abstract
Auditory rhythmic training has been shown to enhance motor performance (e.g., walking: Thaut et al., 1996). The current study recruited healthy individuals to examine the effect of unimodal (i.e., auditory or visual cues) vs. bimodal cues on the spatiotemporal adaptations of a sequential upper-limb reaching task with varying movement amplitudes. This was done because congruent bimodal information facilitates perception relative to unimodal information (Ernst & BA¼lthoff, 2004). Participants performed reversal movement sequences that involved 6, 12, and 18 cm amplitudes and were asked to maintain the same movement duration for all amplitudes. Before each trial in the sensory-cued conditions, the rhythm was specified with four auditory beeps, visual flashes, or audiovisual cues. The sequences were also performed without these pre-trial cues (i.e., no-cue conditions). Movement time error (MTE) corresponded to the difference between the participant's sub-movement times and the prescribed rhythm. Within the sensory- cued and no-cue conditions, small amplitude movements yielded the largest MTEs. Critically and as hypothesized, the sensory-cued audiovisual condition yielded lower MTEs relative to the auditory and visual conditions, although this was limited to the small amplitude movements. Interestingly, these lower MTEs for the small movement amplitude were also observed in the no- cue condition following the audiovisual condition vs. those following the auditory-cued conditions. Follow-up analyses involving linear de-trending confirmed that the benefits of bimodal cueing were specific to the sensory training and not a performance improvement over time. Thus, combining auditory with visual cues can enhance rhythmic training, which could also be useful in rehabilitation settings.Acknowledgments: University of Toronto, Ontario Research Fund, Canadian Foundation for Innovation, National Sciences and Engineering Research Council
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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 | 0.002 |
| 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 | 0.000 | 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".