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fNIRS with and without VR headset
In the primary study to think about brain activity during visuospatial problem-solving across immersive virtual reality (VR), 2-D computer screens and physical environments, researchers from Drexel’s School of Biomedical Engineering uncovered a surprising revelation – VR-based learning exhibited optimal neural efficiency, a measure that gauges the brain activity required to finish a unit task. This finding, published within the journal Sensors, reveals using virtual reality may foster more efficient learning than real-world environments.
During the study, 30 young adults engaged in roughly 60-minute visuospatial ( participants’ visual perception of the spatial relationships between the objects in front of them) tasks, tackling 3D geometric puzzles as their prefrontal cortex activity was monitored with a wearable neuroimaging sensor called functional near-infrared spectroscopy (fNIRS). This optical brain imaging tool tracks cortical oxygenation changes in specific brain regions corresponding to neuronal activation alterations. Researchers repeatedly monitored participants’ brain activity via fNIRS sensor throughout the session through a rigorously designed protocol of tasks across three presentation mediums.
“The combined evaluation of task-related brain activity and behavioral performance provide a more nuanced assessment, and here, results suggest that for this cognitive task, VR reduced the mental load needed to finish tasks,” said senior writer Hasan Ayaz, PhD, an associate professor within the School of Biomedical Engineering, Science and Health Systems. “This implies that VR furnishes more intelligible 3D visual cues, facilitating higher problem inspection and solution evaluation.”
Participants solved the puzzles faster and were more accurate in VR versus real-world or computer screen environments with comparable mental effort. Authors suggest this profit might come from the augmented feedback in the shape of audio or visual cues. Participants made more errors and spent more time rotating the puzzle while trying to unravel problems within the real-world environment, which authors suggest results in lower neural efficiency.
“Although interaction is essential to learning, taking more time than needed trying to unravel a challenge will be exhausting and should encourage someone to present up before it’s accomplished,” said Ayaz. “In some cases, VR’s capability to create immersive spaces – complete with higher mental imagery and visual cues for learning – may in some cases be a greater option than a conventional real-world or computer screen-based environments.”
The authors argue that such neuroergonomic evaluation is a invaluable approach to studying complex human-machine systems, using brain activity in real-time from the prefrontal cortex during cognitive tasks and these findings may help experts develop VR-based STEM learning and other training materials. Neural efficiency might also be a strategy to evaluate instructional materials and teaching approaches, in addition to potentially personalizing the data delivery for every student’s success, in keeping with the study authors. Previous studies show that developing spatial skills – which will be supported using VR – can improve performance in science, technology, engineering, and math.
The researchers say that real-world and computer screen applications can still be useful for spatial learning, notably for learners who don’t need visual aids.
These findings contribute to a burgeoning body of research on neuroergonomic skilled training, including applications in mission-critical domains corresponding to surgical procedures conducted in VR environments, in addition to aviation training for pilots and air traffic controllers. Ayaz’s research lab focuses on neuroergonomics, researching brain health and performance optimization. A previous study by Ayaz and colleagues, specializing in flight training simulations, demonstrated that tailoring training based on individual performance and fNIRS-based cognitive load measures yielded superior outcomes in comparison with traditional training methods.
In addition to Ayaz, graduate students Raimundo da Silva Soares Jr. of Drexel and Universidade Federal do ABC, Kevin L. Ramirez-Chavez and Altona Tufanoglu, and post-doctoral fellow Candida Barreto within the School of Biomedical Engineering, Science and Health Systems; and João Ricardo Sato from Universidade Federal do ABC contributed to this work.
Editor’s note: The authors note that fNIR Devices, LLC manufactures the optical brain imaging instrument and licensed IP and know-how from Drexel University. Ayaz helped develop the technology and holds a minor share within the firm. The research was supported by Fulbright United States-Brazil and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior and the São Paulo Research Foundation.
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