Abstract:Some evidence suggests that experiencing a given scenario using virtual reality (VR) may engage greater attentional resources than experiencing the same scenario on a 2D computer monitor. However, the underlying neural processes associated with these VR-related effects, especially those pertaining to current consumer-friendly head-mounted displays of virtual reality (HMD-VR), remain unclear. Here, two experiments were conducted to compare task performance and EEG-based neural metrics captured during a perceptu… Show more
“…Head-mounted-display devices with virtual reality environments extend SoP and attention, leaving behind 2D applications without immersion and user interaction (Li et al, 2020b). A recent work (Wan et al, 2021) also defends that, FIGURE 3 | Main predictors for identification of attention allocation, higher immersion states and higher cognitive load effects as found in the database.…”
Section: Discussionmentioning
confidence: 96%
“…Frequency band (Hz) Delta (δ ) 0.5-3.5 Hz (Cowley and Ravaja, 2014); 0-3.9 Hz (Hazarika et al, 2018); 0.1-3 Hz (Coenen et al, 2020); 0-5 Hz (Mathewson et al, 2012); 1-3 Hz (Wang Y. K. et al, 2015); 1-4 Hz (Wang Y. et al, 2020); 2-4 Hz (Heyselaar et al, 2018) Theta (θ ) 3-8 Hz (Jaquess et al, 2017);3.5-8 Hz (Cowley and Ravaja, 2014); 3.9-7.8 Hz (Hazarika et al, 2018); 4-8 Hz (Wang Q. et al, 2011;Matthews et al, 2015;Vortmann et al, 2019); 3-10 Hz (Savage et al, 2013); 4-7 Hz (Clemente et al, 2014;Jagannath and Balasubramanian, 2014;Wang Y. K. et al, 2015;Wang Y. et al, 2020;Fuentes-García et al, 2019;Coenen et al, 2020;Li et al, 2020b); 5-8 Hz (Heyselaar et al, 2018) Alpha (α ) 7-12 Hz (Mathewson et al, 2012);7.8-15.6 Hz (Hazarika et al, 2018); 8-12 Hz (Clemente et al, 2014;Jagannath and Balasubramanian, 2014;Berger and Davelaar, 2018;Coenen et al, 2020;Wang Y. et al, 2020); 8-13 Hz (Cowley and Ravaja, 2014;Wang Y. K. et al, 2015;Jaquess et al, 2017;Li et al, 2020b); 9-13 Hz (Matthews et al, 2015); 8-14 Hz (Heyselaar et al, 2018;Vortmann et al, 2019) Beta (β ) 12-30 Hz (Wang Q. et al, 2011); 13-25 Hz (Coenen et ...…”
The competitive demand for attention is present in our daily lives, and the identification of neural processes in the EEG signals associated with the demand for specific attention can be useful to the individual’s interactions in virtual environments. Since EEG-based devices can be portable, non-invasive, and present high temporal resolution technology for recording neural signal, the interpretations of virtual systems user’s attention, fatigue and cognitive load based on parameters extracted from the EEG signal are relevant for several purposes, such as games, rehabilitation, and therapies. However, despite the large amount of studies on this subject, different methodological forms are highlighted and suggested in this work, relating virtual environments, demand of attention, workload and fatigue applications. In our summarization, we discuss controversies, current research gaps and future directions together with the background and final sections.
“…Head-mounted-display devices with virtual reality environments extend SoP and attention, leaving behind 2D applications without immersion and user interaction (Li et al, 2020b). A recent work (Wan et al, 2021) also defends that, FIGURE 3 | Main predictors for identification of attention allocation, higher immersion states and higher cognitive load effects as found in the database.…”
Section: Discussionmentioning
confidence: 96%
“…Frequency band (Hz) Delta (δ ) 0.5-3.5 Hz (Cowley and Ravaja, 2014); 0-3.9 Hz (Hazarika et al, 2018); 0.1-3 Hz (Coenen et al, 2020); 0-5 Hz (Mathewson et al, 2012); 1-3 Hz (Wang Y. K. et al, 2015); 1-4 Hz (Wang Y. et al, 2020); 2-4 Hz (Heyselaar et al, 2018) Theta (θ ) 3-8 Hz (Jaquess et al, 2017);3.5-8 Hz (Cowley and Ravaja, 2014); 3.9-7.8 Hz (Hazarika et al, 2018); 4-8 Hz (Wang Q. et al, 2011;Matthews et al, 2015;Vortmann et al, 2019); 3-10 Hz (Savage et al, 2013); 4-7 Hz (Clemente et al, 2014;Jagannath and Balasubramanian, 2014;Wang Y. K. et al, 2015;Wang Y. et al, 2020;Fuentes-García et al, 2019;Coenen et al, 2020;Li et al, 2020b); 5-8 Hz (Heyselaar et al, 2018) Alpha (α ) 7-12 Hz (Mathewson et al, 2012);7.8-15.6 Hz (Hazarika et al, 2018); 8-12 Hz (Clemente et al, 2014;Jagannath and Balasubramanian, 2014;Berger and Davelaar, 2018;Coenen et al, 2020;Wang Y. et al, 2020); 8-13 Hz (Cowley and Ravaja, 2014;Wang Y. K. et al, 2015;Jaquess et al, 2017;Li et al, 2020b); 9-13 Hz (Matthews et al, 2015); 8-14 Hz (Heyselaar et al, 2018;Vortmann et al, 2019) Beta (β ) 12-30 Hz (Wang Q. et al, 2011); 13-25 Hz (Coenen et ...…”
The competitive demand for attention is present in our daily lives, and the identification of neural processes in the EEG signals associated with the demand for specific attention can be useful to the individual’s interactions in virtual environments. Since EEG-based devices can be portable, non-invasive, and present high temporal resolution technology for recording neural signal, the interpretations of virtual systems user’s attention, fatigue and cognitive load based on parameters extracted from the EEG signal are relevant for several purposes, such as games, rehabilitation, and therapies. However, despite the large amount of studies on this subject, different methodological forms are highlighted and suggested in this work, relating virtual environments, demand of attention, workload and fatigue applications. In our summarization, we discuss controversies, current research gaps and future directions together with the background and final sections.
“…The use of 3D stimulation provides more realistic experiences (Riva et al., 2007), especially relevant for the study of looming motion effects. Also, 3D stimulation effectively limits the influence of external distraction, engaging selective attention more effectively than traditional 2D methodology (Li et al., 2020).…”
Previous research shows that endogenous attention (the controlled selection of certain aspects of our environment) is enhanced toward emotional stimuli due to its biological relevance. Although looming affective stimuli such as threat seem even more critical for survival, little is known about their effect on endogenous attention. Here, we recorded neural (event‐related potentials, ERPs) and behavioral responses (errors and reaction times) to explore the combined effect of emotion and looming motion. 3D‐recreated static and moving animals assessed as emotionally positive, negative, and neutral, were presented to participants (n = 71), who performed an indirect categorization task (vertebrate vs. invertebrate). Behavioral results showed better task performance, as reflected by lower number of errors and reaction times, in response to threatening stimuli. Neural indices revealed significant early (P1p, 150 milliseconds), intermediate (P2p, 240), and late (LPP, 450) effects, the latter being more intensely associated with behavior, as revealed by regression analyses. In general, neural indexes of attention to both static and dynamic stimuli showed a positivity offset in early stages and a negativity bias in subsequent phases. However, and importantly, the progressive inclusion of negative stimuli in the attentional focus is produced earlier in the case of dynamic (at P2p latency) than in static versions (at LPP). These results point to an enhancement of attention, particularly in temporal terms, toward stimuli combining motion and biological significance.
“…Successful interventions have depended upon how effectively this demanding technology was targeted to engage and sustain the attention of participants during their tasks 30 , 31 . Importantly, using an HMD VR drives greater engagement and increased subsequent success for participants, relative to their performance on the same task using a flat-screen-monitor version 32 – 34 . Figure 1 Illustrations of the virtual reality game in Experiment 2 (A) Via a head-mounted virtual reality display, participants had a first-person view of wayfinding trials.…”
Section: Introductionmentioning
confidence: 99%
“…their tasks 30,31 . Importantly, using an HMD VR drives greater engagement and increased subsequent success for participants, relative to their performance on the same task using a flat-screen-monitor version [32][33][34] .…”
Therapeutic interventions have not yet been shown to demonstrate restorative effects for declining long-term memory (LTM) that affects many healthy older adults. We developed a virtual reality (VR) spatial wayfinding game (Labyrinth-VR) as a cognitive intervention with the hypothesis that it could improve detailed, high-fidelity LTM capability. Spatial navigation tasks have been used as a means to achieve environmental enrichment via exposure to and learning about novel and complex information. Engagement has been shown to enhance learning and has been linked to the vitality of the LTM system in the brain. In the current study, 48 older adults (mean age 68.7 ± 6.4 years) with average cognitive abilities for their age were randomly assigned to 12 h of computer game play over four weeks in either the Labyrinth-VR or placebo control game arms. Promptly before and after each participant’s treatment regimen, high-fidelity LTM outcome measures were tested to assess mnemonic discrimination and other memory measures. The results showed a post-treatment gain in high-fidelity LTM capability for the Labyrinth-VR arm, relative to placebo, which reached the levels attained by younger adults in another experiment. This novel finding demonstrates generalization of benefits from the VR wayfinding game to important, and untrained, LTM capabilities. These cognitive results are discussed in the light of relevant research for hippocampal-dependent memory functions.
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