Presentation
Investigating Cognitive Engagement from Training to Application Under Varied Workload Manipulations in Virtual Reality
SessionPoster Session 1
DescriptionThis study examined how neural engagement differs between training and application under varied cognitive workload in a virtual‐reality assembly task. Twenty‐three participants completed the task across four within‐subject conditions (low vs. high intrinsic load and low vs. high extraneous load). We recorded three electroencephalography-derived engagement indicators: Cognitive Effort (parietal α/frontal θ), Sustained Attention (β/(α + θ)), and Integration & Execution (γ power at Fz, Cz, Pz). Linear mixed‐effects models revealed a robust learning phase effect: application values exceeded training for Sustained Attention (b = –0.094, p = .037) and Integration & Execution at Fz (b = –0.485, p < .001), Cz (b = –0.253, p = .009), and Pz (b = –0.238, p = .025). Significant learning phase x workload condition interactions emerged for Sustained Attention (b =+0.210, p=.001) and for Integration & Execution at Fz (b= +0.321, p=.014). A repeated‐measures MANOVA confirmed a multivariate learning phase effect on the combined engagement profile. Findings indicated that unguided application amplifies working‐memory and integrative engagement more consistently than sustained attention, and that this amplification depends on workload type. Our results inform the design of adaptive training systems that monitor phase‐dependent neural engagement to optimize learning transfer.
Contributors
Alternate Presenter
Event Type
Poster
TimeTuesday, October 14th5:30pm - 6:30pm CDT
LocationRiverside East
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