1. The high-working load states induced by action real-time strategy gaming: An EEG power spectrum and network study
- Author
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Yi Li, Diankun Gong, Yuening Yan, Weiyi Ma, Dezhong Yao, Yu Gao, Tiejun Liu, and Yutong Yao
- Subjects
Male ,Adolescent ,Cognitive Neuroscience ,Decision Making ,Experimental and Cognitive Psychology ,Electroencephalography ,050105 experimental psychology ,Task (project management) ,Young Adult ,03 medical and health sciences ,Behavioral Neuroscience ,Cognition ,0302 clinical medicine ,Real-time strategy ,Cognitive resource theory ,Neuroplasticity ,medicine ,Humans ,Attention ,0501 psychology and cognitive sciences ,Video game ,medicine.diagnostic_test ,05 social sciences ,Brain ,Workload ,Video Games ,Psychology ,Psychomotor Performance ,030217 neurology & neurosurgery ,Cognitive psychology - Abstract
Action Real-time Strategy Gaming (ARSG) is a cognitively demanding task that requires attention, sensorimotor skills, high-level team coordination, and strategy-making abilities. Thus, ARSG can offer important, new insights into learning-related neural plasticity. However, little research has examined how the brain allocates cognitive resources in ARSG. By analyzing power spectrums and electroencephalograph (EEG) functional connectivity (FC) networks, this study compared multiple conditions (resting, movie watching, ARSG, and Life simulation gaming – LSG) in two experiments. Consistent with previous research, we found that brain waves appeared to be de-assimilated after activation. Furthermore, results showed that ARSG was associated with higher activation and workload as indicated by θ-waves, and required higher attention as reflected by β-waves. Furthermore, as participants began ARSG, the allocation of cognitive resource gradually prioritized the frontal area, which controls attention, decision-making, monitoring, and mnemonic processing, while participants also showed an enhanced ability to process information under the ARSG condition as indicated by network characteristics. These electrophysiological changes observed in ARSG were not found under LSG. Thus, this study applied both power spectrum and EEG FC networks analyses to ARSG research, revealing characteristics of brain waves in typical areas and how the brain gradually changes from low-working load states to high-working load states based on real-time EEG recordings.
- Published
- 2019