Justin Asbee Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Postdoctoral Fellow
postdoc
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Justin Asbee's research focuses on the intersection of virtual reality, cognitive function, and neural adaptations. He investigates the use of virtual environments, such as the virtual reality Stroop task and a virtual city, to study cognitive and affective load. His work also explores the application of neurofeedback techniques, specifically frontal theta neurofeedback training, and its neural and behavioral effects.
Asbee has examined the impact of transcranial direct current stimulation on cognitive and affective outcomes using virtual stimuli. Additionally, his research extends to computational and statistical modeling of virtual reality tasks, including machine learning classification for adaptive tasks and feasibility studies for identifying predictors in virtual environments. He also studies the relationship between video game usage, substance use, and sleep patterns among college students using frequentist and Bayesian approaches.
Metrics
- h-index: 5
- Publications: 12
- Citations: 199
Selected Publications
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Feasibility study to identify machine learning predictors for a Virtual Environment Grocery Store (2024)
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Neural and behavioral adaptations to frontal theta neurofeedback training: A proof of concept study (2023)
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Using a frequentist and Bayesian approach to examine video game usage, substance use, and sleep among college students (2023)
Collaboration Network
Top Collaborators
- Machine learning classification analysis for an adaptive virtual reality Stroop task
- Neural and behavioral adaptations to frontal theta neurofeedback training: A proof of concept study
- Interaction of Cognitive and Affective Load Within a Virtual City
- Effects of Transcranial Direct Current Stimulation on Cognitive and Affective Outcomes Using Virtual Stimuli: A Systematic Review
- Factor analysis of the virtual reality Stroop task
Showing 5 of 6 shared publications
- Machine learning classification analysis for an adaptive virtual reality Stroop task
- Factor analysis of the virtual reality Stroop task
- Feasibility study to identify machine learning predictors for a Virtual Environment Grocery Store
- Machine learning classification analysis for an adaptive virtual reality Stroop task
- Factor analysis of the virtual reality Stroop task
- Interaction of Cognitive and Affective Load Within a Virtual City
- Using a frequentist and Bayesian approach to examine video game usage, substance use, and sleep among college students
- Using a frequentist and Bayesian approach to examine video game usage, substance use, and sleep among college students
- Using a frequentist and Bayesian approach to examine video game usage, substance use, and sleep among college students
- Neural and behavioral adaptations to frontal theta neurofeedback training: A proof of concept study
- Neural and behavioral adaptations to frontal theta neurofeedback training: A proof of concept study
- Neural and behavioral adaptations to frontal theta neurofeedback training: A proof of concept study
- Neural and behavioral adaptations to frontal theta neurofeedback training: A proof of concept study
- Neural and behavioral adaptations to frontal theta neurofeedback training: A proof of concept study
- Neural and behavioral adaptations to frontal theta neurofeedback training: A proof of concept study
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