Evidence map›Paper›PMID 42867760›Full record

ArticleApplied psychophysiology and biofeedback2026

Integrating Virtual Reality-Based Simulation and Biofeedback Monitoring into Vocal Training: Investigating Cognitive Mechanisms of Singing and Sensorimotor Adaptation.

Wensheng Tang, Pinqi Zhang

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In one paragraph

Article in Applied psychophysiology and biofeedback, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Wensheng TangSchool of Music and Dance, Nanning Normal University, Nanning, China.
Pinqi ZhangSchool of Music and Dance, Hechi University, Hechi, China. pinqizhang6@chinesefineart.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing complexity of vocal training, particularly in the control of internal physiological processes, necessitates the integration of technologies capable of enhancing sensorimotor learning and self-regulation. Traditional vocal pedagogy remains limited in accessing and monitoring underlying physiological mechanisms, resulting in a gap between subjective perception and objective performance control. The present study aimed to evaluate whether an immersive training environment integrating virtual reality and multimodal biofeedback could improve short-term physiological, acoustic, and cognitive outcomes during vocal training compared with conventional instruction. A randomized controlled pre-post design was implemented across three universities in China (Beijing). The sample comprised 107 students (aged 18-30 years), randomly assigned to a control group (n = 53) and an experimental VR group (n = 54). The findings indicate significant improvements in the VR group: a reduction in muscle activity (- 6.29 vs. -2.93 in the control group; p < 0.001; d = 0.71) and respiratory variability (- 0.048 vs. -0.022; p < 0.001; d = 1.05). Additionally, increased pitch stability (4.91 vs. 2.37; p < 0.001; d = 0.78) and reduced shimmer were observed. A negative correlation was identified between vocal awareness and EMG (r = - 0.273; p = 0.004). Participants receiving VR-based multimodal biofeedback demonstrated greater short-term improvements in physiological, acoustic, and subjective outcomes than those receiving conventional instruction. These findings are consistent with enhanced sensorimotor adaptation during training within an information-rich learning environment. Although further studies are required to evaluate long-term retention, transfer of learning, and the independent contributions of VR and biofeedback, the proposed approach may complement existing vocal pedagogy by providing learners with additional physiological information during practice.

Indexed as

Embodied CognitionMuscle CoordinationRespiratory ControlVirtual EnvironmentsVocal AwarenessVoice

Identifiers

PMID42867760

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.