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Record W6962325192 · doi:10.17605/osf.io/qaxts

THE TRANSFER EFFECT OF VIRTUAL BASEBALL BATTING TRAINING ON PERFORMANCE IN YOUNG ATHLETES

2025· other· en· W6962325192 on OpenAlexaff

Bibliographic record

VenueOpen Science Framework · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVirtual realityPerceptionTask (project management)Motor learningSession (web analytics)AthletesTransfer of trainingTraining (meteorology)Motor skill

Abstract

fetched live from OpenAlex

Perceptual-cognitive skills are necessary for the successful completion of interceptive motor tasks, which aim to utilize the body or an implement to intercept a moving item (Davids et al., 2004). For example, for successful baseball batting, the ability of the hitter to interpret visual cues to support the swinging choice and then conduct the motor movement is crucial. Given that most of this perceptual information relies on vision, researchers have studied the effect of perceptual visual training programs on baseball skills. One technology that offers the possibility to practice the perceptual-motor component of the batting skills is virtual reality (VR). It has demonstrated promising potential in creating valid conditions for perceptual-cognitive applications in sports (Gray, 2019). In sum, VR affords controlled conditions, experimental task manipulation, does not require real pitching or pitchers, and increases the accessibility to training (Gray, 2019; Le Noury et al., 2022). According to recent research, batting performance improved with adaptive VR practice, as evidenced by game statistics and players' advancement to higher competition levels throughout the five years after the intervention (Gray, 2017). Only one study, though, has produced these findings, and there is also evidence to the reverse, indicating that more practice in a VR environment does not enhance the acquisition of baseball skills (Kincaid et al., 2021). The divergence between the results of those studies might be explained by the different experimental tasks used. While training session in Gray (2017) included pitch recognition tasks and batting motion in a virtual environment, participants in Kincaid et al. (2021) practiced a pitch recognition drill (ball position and color) that did not require identifying the type of pitch or any actual batting motion. These contradictory findings raise doubts about the effectiveness of VR visuomotor training for baseball batting. Additionally, since the evidence primarily evaluated outcome measures such as pitch recognition and plate discipline, it is limited in explaining the visual mechanisms that support the transfer of learning to real-world environments. In this context, examining oculomotor variables and motion kinematics could help clarify the mechanisms underlying the benefits of perceptual training on batting performance. Thus, the objective of this study is to examine how VR batting practice transfer to batting performance while facing a real pitcher. The performance will be evaluated at pre- and post-tests in a VR baseball batting practice group (VRG) and a real-world practice group (RWG) using a pitch recognition task and a real-world baseball batting test.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.001

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.

Opus teacher head0.018
GPT teacher head0.310
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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