Investigating VR Safety Training Transfer in Construction Hazard Recognition: A Neurocognitive Perspective

Effective construction safety training requires training transfer to enable workers to recognize untrained hazards, a critical capability for adapting to the complex, dynamic environments of construction sites. Current virtual reality (VR) safety training evaluates performance only within trained scenarios, neglecting the mechanisms necessary for transfer and limiting validation of real-world effectiveness. This study utilized magnetoencephalography (MEG) to compare immersive VR and conventional PowerPoint (PPT) training, specifically investigating transfer effectiveness and underlying mechanisms using an untrained hazard scenario protocol. VR training showed significantly better transfer performance (Δd’=0.481 vs. 0.051, p=0.035). This behavioral superiority was linked to distinct neural signatures (248–336 ms post-stimulus) revealing the coordinated emotional-contextual encoding as the core transfer mechanism. These findings challenge rule-based learning assumptions, demonstrating that effective hazard recognition relies on forming robust, generalizable embodied threat memories. Based on these neuroscience-driven insights, we propose the S.A.F.E. neurocognitive framework (Schema matching, Affective salience, Feature detection, Element identification) as the first evidence-based design specification for optimizing VR safety training. This research establishes a methodological foundation for assessing training transfer, identifies its precise neurocognitive mechanisms, and provides design principles to enhance novel hazard recognition in the real world.

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Publication Details

Journal
Advanced Engineering Informatics
Published
2026-02-09
DOI
https://doi.org/10.1016/j.aei.2026.104412
Primary Topic
Occupational Health and Safety Research
Type
article
Field-Weighted Citation Impact
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article

Investigating VR Safety Training Transfer in Construction Hazard Recognition: A Neurocognitive Perspective

Zhikun Ding, Zhaoyang Xiong, Jianfeng Zhang, Yaning Li
Advanced Engineering Informatics
Occupational Health and Safety Research
article

Investigating VR Safety Training Transfer in Construction Hazard Recognition: A Neurocognitive Perspective

Zhikun Ding, Zhaoyang Xiong, Jianfeng Zhang, Yaning Li
article en

Abstract

Effective construction safety training requires training transfer to enable workers to recognize untrained hazards, a critical capability for adapting to the complex, dynamic environments of construction sites. Current virtual reality (VR) safety training evaluates performance only within trained scenarios, neglecting the mechanisms necessary for transfer and limiting validation of real-world effectiveness. This study utilized magnetoencephalography (MEG) to compare immersive VR and conventional PowerPoint (PPT) training, specifically investigating transfer effectiveness and underlying mechanisms using an untrained hazard scenario protocol. VR training showed significantly better transfer performance (Δd’=0.481 vs. 0.051, p=0.035). This behavioral superiority was linked to distinct neural signatures (248–336 ms post-stimulus) revealing the coordinated emotional-contextual encoding as the core transfer mechanism. These findings challenge rule-based learning assumptions, demonstrating that effective hazard recognition relies on forming robust, generalizable embodied threat memories. Based on these neuroscience-driven insights, we propose the S.A.F.E. neurocognitive framework (Schema matching, Affective salience, Feature detection, Element identification) as the first evidence-based design specification for optimizing VR safety training. This research establishes a methodological foundation for assessing training transfer, identifies its precise neurocognitive mechanisms, and provides design principles to enhance novel hazard recognition in the real world.

Advanced Engineering InformaticsVol. 71
Shenzhen University (CN)
Sustainable cities and communities
Openalex Percentile: Top 63%
Occupational Health and Safety Research
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