The Interaction Efficiency of Different Visual Areas on a Virtual Reality Device Screen: Standing versus Sitting Posture

doi:10.21203/rs.3.rs-4308537/v1

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The Interaction Efficiency of Different Visual Areas on a Virtual Reality Device Screen: Standing versus Sitting Posture

https://doi.org/10.21203/rs.3.rs-4308537/v1

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User posture can have an impact on cognition. Virtual reality (VR) devices extend the traditional two-dimensional interface into a three-dimensional space, necessitating some changes in body posture for users to interact between both. This study evaluated the interaction efficiency of standing versus sitting postures and the four corners of interface in VR environments. Thirty-five participants took part in the experiment, which measured the time they spent on searching and selecting targets under different conditions. With a two-way analysis of variance (ANOVA), the results indicated that the interaction efficiency of participants in a sitting posture was generally higher than that in a standing posture. When searching for targets on the left side, participants spent less time in both standing and sitting postures than on other areas. Specifically, searching for targets in the top-left corner took less time in a standing posture than other areas, while searching for targets in the bottom-left corner took less time in a sitting posture. The results suggested that posture also influenced cognitive performance in VR environments. Moreover, the high interaction efficiency could be achieved when the interface was placed at the top-left corner in a standing posture and at the bottom-left corner in a sitting posture. These findings could provide insights for VR developers on interaction design, enhancing optimal interface design according to different user postures to ensure expected interaction efficiency and user experience.

Virtual reality (VR) devices

Body posture

Interaction efficiency

User interface

Screen visual area

Virtual reality (VR) technology is an interactive technique that enables users to immerse themselves in computer-generated virtual environments. VR devices are pivotal components in achieving such experiences. In recent years, VR devices have been progressively incorporated into everyday life, and their potential in fields such as healthcare, education, and rehabilitation is increasingly being explored (Cipresso et al., 2018; Fu & Li, 2023; Tabbaa et al., 2021). They can present more complex three-dimensional information from multiple perspectives than traditional display devices, thereby offering a more immersive experience (Liebers et al., 2023). The intricate display formations also require users to adopt changing postures when interacting with VR devices.

Studies have indicated that postural changes influenced cognitive and physiological states (Harris et al., 2015). Participants’ attention and working memory differed according to various postures, suggesting that posture affected the efficiency of user interactions with products (Patston et al., 2017). User abilities to resist stimulus interference and their creative capacities varied with different postures (Zhou et al., 2017; Schulman & Shontz, 1971). Thus, the complexity of interaction design should vary according to distinct postures. Meanwhile, participants’ visual areas differed according to postures, which may influence the permissible interaction range during interactions with VR devices, ultimately affecting interaction efficiency (Harrison & Dey, 2008).Therefore, posture changes can influence human cognitive and physiological states, potentially affecting performance efficiency.

Researchers have gradually realized that the use and design logic of VR devices differed from those of traditional display devices. Posture is being integrated into the scope of VR interaction design and research. For example, in VR environments, researchers found that interactions with the ipsilateral arm and ipsilateral interface are more efficient and accurate than other conditions (Lou et al., 2021). By comparing the cognitive state and sensory experience differences between standing and sitting postures in VR, researchers found that the stability and presence of interaction states varied with different postures (Kumar et al., 2023; Kim et al., 2020). Meanwhile, some researchers took interface layout as a starting point, discovering that visual stimuli in VR can induce body movement, and they designed a set of interactive interfaces to reduce unfavorable body postures when using the devices (Fujimoto & Ashida, 2020; Shin et al., 2020; Breazeal et al., n.d.).

In summary, the study of VR devices should consider the impact of posture changes on cognitive and physiological states. The influence of posture changes on interaction efficiency in virtual environments warrants an in-depth exploration due to the frequent posture changes that occur when using VR devices. Therefore, this study focused on high-frequency postures, standing and sitting, in human–VR device interactions and evaluated the performance efficiency of different visual areas on a VR device screen under the two postures, in the hope of providing guidelines for appropriate interface design with different postures in VR environments.

2.1 Visual area on a screen

The varying visual areas lead to significantly different user experiences and interaction efficiencies. Klatt et al. (2024) have determined that the limited region in the visual area to which people’s attention can be paid (attention windows) is asymmetric, indicating that participants more easily notice the left and top areas. Otterbring et al. (2013) required participants to search for designated text or images from the interface; their results indicated that the search task took the least time when the text information was located in the upper-left area and the image information was positioned in the upper-right area. Pearson and Van Schaik (2003) asked participants to search for information using different colors and locations, they found that participants responded significantly faster when the blue links were placed at the top than when they were placed at the bottom, and significantly faster when the red links were positioned on the right than when they were positioned on the left. Van Schaik and Ling (2001) examined the impact of navigation panes in different colors and areas on information search tasks; their results suggest that when the navigation pane was located at the top and left area of the interface, participants completed the task more quickly and with higher accuracy. Few studies have been conducted on the visual field in virtual reality environments. For example, Jang et al. (2016) required participants to wear VR headsets and turn their heads to find the targets placed outside of their field of view; the results showed that when the target was located on the left side, participants would complete the search faster. Moreover, Pelz et al. (2001) noted that the horizontal field of human vision is 90 degrees wider than the vertical field of view, which may result in more horizontal than vertical gaze movements. In addition, the interaction efficiency of visual areas can be influenced by cultural factors. For instance, Abed (1991) observed that individuals from different cultural backgrounds exhibit varied preferences when using interfaces with the same visual area, finding that the gaze pattern when using the interface was correlated with their reading habits.

Therefore, visual area could influence users’ cognition, performance, and experience. However, most studies have focused on the real-world environment or the field of regard, leaving the interaction efficiency between different visual areas in the field of view in VR environments unknown. Additionally, the reading habit in Chinese-language contexts may cause individuals to focus their attention on the top-left area when interacting with interfaces, but it remains unclear whether a similar phenomenon occurs in VR environments.

2.2 Body posture

Researchers have noted that posture influenced the way individuals perceive the world (Harris et al., 2015). Standing and sitting are the most common postures when using VR devices. Posture affected the range of effective visual fields; Reed-Jones et al. (2014) discovered that the effective visual field range was reduced in the sitting posture compared to the standing posture, and it further decreased when walking. Some research indicated that users in a standing posture performed better at various tasks than those in a sitting posture; participants’ attention and working memory performance were enhanced when they were standing (Patston et al., 2017). People in a standing posture were more resistant to interference than those in a sitting posture (Rosenbaum et al., 2017). Participants’ creative cognitive performance was better when standing compared to sitting or lying down (Zhou et al., 2017). However, other studies found that participants were better able to focus on solving problems while in a sitting posture (Schulman & Shontz, 1971). Abou Khalil et al. (2024) asked 60 participants to complete different tasks while standing, sitting, and walking; they found that posture had an impact on cognitive status, and participants performed better on the Stroop task while standing, and on the subtraction task while sitting. Some researchers also compared the differences in cognitive states according to standing, sitting, and lying postures; they found that lying posture was more conducive to mind wandering and created lower reading accuracy (Yang et al., 2022). Therefore, the distinction in cognitive status between standing and sitting postures remains inconclusive.

When using VR devices, users need to change their posture frequently in order to interact with objects in the three-dimensional virtual space. In VR environments, Lou et al. (2021) observed that using one arm to interact with an interface on the same side as the central area in VR was more efficient and accurate. Fujimoto and Ashida (2020) discovered that in the VR environment, the identical visual stimuli evoked different head movements when users’ postures varied. Kumar et al. (2023) revealed that posture stability was higher and motion sickness was less likely to occur in a sitting posture. Kim et al. (2020) found that posture significantly affected the sense of presence when playing VR games; compared to sitting and semi-sitting postures, the sense of presence was enhanced when standing.

Relevant research indicated that the use of VR devices may impose additional cognitive load, potentially leading to a greater depletion of cognitive resources (Horak, 2006), which means that differences in cognition occurred when using VR equipment. Studies have shown that posture in VR environments can affect both cognitive and physiological states. However, most studies have focused on user experience, and the impact of standing and sitting postures on interaction efficiency remains unknown.

In summary, this study sought to investigate the interaction efficiency between users in most common body postures and interfaces in the four visual areas in VR. To this end, it conducted an experimental evaluation of the time participants spent searching and selecting tasks under different conditions, in the hope of deriving design guidelines for user interactions with VR interfaces and enabling a state of high interaction efficiency in commonly used postures.

According to previous studies, participants in the standing posture have more focused attention and stronger anti-interference ability (Patston et al., 2017; Rosenbaum et al., 2017). Meanwhile, Chinese-language readers read from left to right, which reading habit is a critical factor in cognition and performance (Abed, 1991). Therefore, this study proposes two hypotheses:

H1: The interaction efficiency between user and interface in a VR environment is higher when standing than when sitting.

H2: The interaction efficiency between user and the interface in the VR environment is as follows: top left > top right > bottom left > bottom right, where “>” means “higher.”

The independent variables include the following: 1). Participant posture (standing vs. sitting); 2). Visual area of the screen (top left vs. bottom left vs. top right vs. bottom right). The dependent variable is the time spent searching a task, which is used to measure the interaction efficiency: the shorter the time, the higher the interactive efficiency. Referring to previous research, using time it took to complete tasks to measure interaction efficiency(Gong et al., 2016).

A two-factor within-subjects experiment was designed to test the interaction efficiency. This experiment was validated by the Tianjin University Ethics Committee.

4.1 Participants

Thirty-five participants were invited to join the experiment, including 17 males and 18 females. They were between 20 and 30 years old (M=22.6571, SD=1.45406), and all had normal vision or corrected vision and were right-handed. Before the experiment, all participants were required to read the written introduction to the study, sign the informed consent form, and complete the background information questionnaire.

4.2 Equipment and settings

The VR device used in the experiment was the Oculus Quest 2 glasses, with a display resolution of 4K, a monocular resolution of 1832 x 1920, and a display refresh rate of 90Hz (Fig. 1). The experimental materials were played using the VR device. To record the participants’ actions using the VR glasses, the VR eyes were connected to a laptop using a USB3.0 linked to a Type-C data cable, allowing experimenters to record the time taken for each action in the VR environment.

The experiment was conducted in a quiet indoor room, with a comfortable chair set up for participants to finish tasks while sitting. The seat height could not be adjusted. Participants were required to use their left hands to operate the joystick, to reduce errors caused by differing levels of familiarity with VR devices. After wearing VR glasses, participants could see a laser emitted along the central axis of the joystick from the VR space(Fig. 2).

4.3 Experimental stimuli

The experimental procedure was developed using the Unreal Engine 4 (UE4) software. During the experiment, information was presented on an arc-shaped interface positioned approximately 65 cm in front of the participants. This interface was divided into four areas during the experiment, labeled as “area 0,” “area 1,” “area 2,” and “area 3” (top-left, top-right, bottom-right, and bottom-left). These areas were symmetrical centered on the center of the screen. If we take the top-left area as an example, its base was located 20 cm from the center point, and its side was located 30 cm from the center point of the field of view. Buttons were placed in each area, with a rounded rectangular shape and dimensions of 8x8 cm. Based on the results of the pre-experiment, the number of buttons in each area was set to six. The experimental target was presented in the central area of the field of view, and before the experiment began, it was covered by a 12x12 cm blue square button. During the experiment, participants were required to search for the central character in each of the four regions. To distinguish between practicing session and formal experiments session, numerical targets were used in the practicing phase, while alphabetical targets were used in the formal experimental phase. The order of the letters was randomized during the experiment to avoid any influence on experimental results due to participants’ familiarity with the alphabet(Fig. 3).

4.4 Experimental procedure

During the experiment, participants were required to aim the laser at the character they were to select, and then use their non-dominant hand to press the physical button on the joystick to complete the selection operation. They needed to search and select as quickly as possible, and the interface did not automatically move forward unless the participant had completed the task. To ensure that participants started each task from the same starting point, they were first asked to control the joystick to select the blue block located in the central area when each task started. They were able to search and select the target character only after the blue block disappeared. The system then measured the time from the initial selection of the blue block to the selection of the target character. The system feedbacked audibly to the participant to tell them that they had clicked correctly and should proceed to the next task when the selected character was the same as the target one. Otherwise, the system would make a sound to prompt a wrong selection, and the participant needed to continue selecting characters until they made the correct selection. After each search and selection, a cross appeared in the center of the screen for only one second to eliminate the impact of the current task on the next.

Participants were required to join the experiment in both standing and sitting postures (Fig. 4). They were tested randomly. Before the test started, the experimenter introduced the basic requirements to each participant. The participant was guided to sit upright in the designated position, with eyes looking straight ahead, then given VR glasses, which were adjusted until the objects within the field of view were clear. The participant then learned the basic operation introductions. After participants were instructed in the basic operating method, a set of five tasks were used as practice before the formal experiment, and numbers were used in place of letters to avoid affecting the formal experiment. After the practice session, the participant moved to the sitting round of the formal experiment, where they were first required to complete 50 letters’ worth of task searches and selections in a sitting posture. After 50 tasks in the sitting round, the participant was reminded by text and sound prompts that this round was completed and he/she could take off the VR glasses to have five minutes’ rest. After the break, the standing round commenced, where the participant was required to stand at a designated position and re-fit their VR glasses, continuing with 50 letters’ worth of task searches and selections. Upon completion of the 50 standing task rounds, participants were reminded by text and sound indicating that the whole experiment had ended. Lastly, a brief interview was conducted with the participants to understand their feelings when interacting with different areas in different postures. The entire experiment lasted for about 20 minutes (Fig. 5).

Ultimately, a total of 31 valid datasets were obtained, automatically recorded by the program. The other four data sets were deleted due to significant differences in mean reaction times compared to the other data. The experimental data were analyzed using IBM’s SPSS 26 software. First, outliers were removed from the data, and the mean duration of each participant in different postures to search for targets located at various orientations was calculated. Subsequently, a two-way analysis of variance (ANOVA) was used to examine the influence of posture and orientation factors on the time taken for search–selection tasks.

The results of two-way ANOVA indicate significant differences between different areas (F=6.934, p<0.01, Partial η2=0.08), as well as significant differences between different postures (F=9.359, p<0.01, Partial η2=0.038), while the interactive effects are not significant (F=0.207, p>0.05, Partial η2=0.003)(Table1).
Table 1: The ANOVA results

Factors	F	ηp2
Area	6.934***	0.08
Posture	9.359***	0.038
Area × Posture	0.207	0.003
* represents significance at the 0.01 level, represents significance at the 0.05 level, and * represents significance at the 0.10 level.

There was a significant difference in the time taken by users of different postures to complete the search and selection task (F=9.359, p<0.01, Partial η2=0.038). Furthermore, the participants take less time to complete the task when in a sitting posture ( Fig. 6).

For different areas, significant differences were recorded in task completion times (F=6.934, p<0.01, Partial η2=0.08). The mean task completion time was the longest when the target was located at the top-right corner, while the mean time was the shortest when it is at the top-left corner. The time had no significant difference when the target was located at the top-right and bottom-right corners, and the time had no significant difference when it was at both the top-left and bottom-left corners. The interaction effect between posture and orientation was not significant (p>0.05). Thus, the left–right orientation significantly affects the time participants spent on tasks. The time was significantly shorter when the target was located on the left than when it is on the right; however, the impact of top–bottom orientation on the time was not significant when the target was on the same side (Fig. 7).

The interaction effect of visual area indicated that the area significantly affected the time spent (p < 0.05). In the standing posture, the fastest time was spent searching for targets at the top-left corner, while in the sitting posture, the fastest time was spent searching for targets at the bottom-left corner. In both standing and sitting postures, the time taken by participants to search for targets located on the left side was shorter than that taken for targets located on the right side. This result aligns with that obtained without distinguishing postures (Fig. 8).

The interaction effect results indicated significant difference in the time taken by participants to complete tasks under two postures (p<0.05) when the target was located in the bottom-left corner. Furthermore, the time in a sitting posture was significantly less than that in a standing posture (Fig. 9).

In this study, we tested the time users spent on searching and selecting interfaces on the corners of their view while in either standing or sitting posture. The experimental results indicated that the interaction efficiency of participants in a sitting posture was generally higher than that of those in a standing posture, which overturned H1. This result was consistent with the findings of Schulman et al. (1971) conducted in real-world settings, which found that participants’ ability to solve problems was stronger in the sitting posture. The reason for the higher interaction efficiency in the sitting posture compared to the standing posture may be as follows: according to related research on posture control and attention, wearing VR equipment could place additional loads on participants’ bodies, making distracted participants more unable to control their posture when standing (Horak, 2006). Additionally, some researchers suggested that the weight of VR headsets may cause negative feelings in participants (Soh et al., 2021). Thus, we speculated that wearing VR headsets required participants to expend more attention to maintain balance and might evoke negative emotions, leading to a high workload and low arousal in the standing posture, thereby affecting their interaction efficiency.

In both standing and sitting postures, participants took less time to search for targets located on the left side than targets on the right side. This indicated that H2 was partially supported. This finding aligned with research on the impact of cultural background and physiological factors on interface usability (Abed, 1991; Pelz et al., 2001),which found that human reading habits and physiological structures would influence their interaction with the user interface. The results of this experiment were also consistent with the previous real-world experiment, which found that the participants spent a shorter time searching and selecting targets on the left and upper sides (Otterbring et al., 2013;Van Schaik & Ling, 2001).We speculated that the left-to-right reading habit in the Chinese context might have a certain influence on how participants read interface information in the VR environment, causing users to initially focus on the left side of the interface. The observed pattern did not follow the expected pattern of left-top > right-top > left-bottom > right-bottom, possibly because participants faced certain problems in turning their heads when wearing VR headsets (Soh et al., 2021). Consequently, participants tended to search for information on the same side before turning their heads to search for information on the other side. Meanwhile, we also observed that when targets were located in the lower-left corner, participants completed the task significantly faster in a sitting posture than in a standing posture. This was consistent with research indicating that same-hand and same-side interface interactions were more efficient (Lou et al., 2021). We speculated that when selecting targets in the lower-left corner, participants were more likely to find the target due to its location on the same side as the operating hand; furthermore, during the experiment, participants were required to first aim their left hands at the center of their field of view, making it more effortless for them to swing their arms toward the lower corner compared to moving it toward the upper corner. Therefore, the interaction with targets in the bottom-left corner requires minimal attention, leading to higher efficiency in these areas for sitting compared to standing.

In summary, the research findings indicated that the user posture and the visual area could affect the efficiency of user interaction with VR device interfaces. The interaction efficiency of participants in a sitting posture was generally higher than that in a standing posture. Participants spent less time searching and selecting targets on the left side, and in the standing posture, they spent less time selecting targets in the top-left corner, while in the sitting posture, they spent less time selecting targets in the bottom-left corner.

This study extended previous research on body posture and cognition from the traditional environment to the VR space. We discovered that when participants were in the VR space, their posture affected their cognition as it did in traditional space. The interaction efficiency of participants in a sitting posture was generally higher than that in a standing posture. In both standing and sitting postures, participants spent less time searching and selecting targets on the left side, taking less time to search and select targets in the top-left area in the standing posture and less time to search and select targets in the bottom-left area in the sitting posture. People always need to change their body posture when using VR devices, which may affect the interaction efficiency between humans and the interface. Therefore, it is necessary to adjust the interface layout based on the posture. The results of this study contributed to providing more adaptive interface design for different postures when using the VR devices, ensuring usability in the interaction process, and effectively improving human–computer interaction efficiency. It also provided a new direction for research on human interaction with VR devices.

However, this study has some limitations. In this experiment, the time taken by participants to select targets from the central region to the boundary region was used as a criterion to evaluate their interaction efficiency. In subsequent research, it may be advisable to use other devices to record participants’ eye movements and hand trajectory, thereby further evaluating the process of participant interaction with the interface. Additionally, postures such as squatting or stepping could be considered in subsequent studies.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Funding

The authors did not receive support from any organization for the submitted work.

Ethics approval

This study was validated by the Tianjin University Ethics Committee, therefore have been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Informed consent

Consent to participate: Informed consent was obtained from all individual participants included in the study.

Author contributions:

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Xiaotian Liang, Jingxuan Yuan. The first draft of the manuscript was written by Jutao Li and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data availability statement:

The authors confirm that the data supporting the findings of this study are available within the supplementary materials.

Abed, F. (1991). Cultural influences on visual scanning patterns. Journal of Cross-Cultural Psychology, 22(4), 525–534.
Abou Khalil, G., Doré-Mazars, K., & Legrand, A. (2024). Stand up to better pay attention, sit down to better subtract: A new perspective on the advantage of cognitive-motor interactions. Psychological Research, 88(3), 735–752. https://doi.org/10.1007/s00426-023-01890-0
Breazeal, C., Wang, A., & Picard, R. (n.d.). Experiments with a Robotic Computer: Body, Affect and Cognition Interactions.
Cipresso, P., Giglioli, I. A. C., Raya, M. A., & Riva, G. (2018). The Past, Present, and Future of Virtual and Augmented Reality Research: A Network and Cluster Analysis of the Literature. Frontiers in Psychology, 9, 2086. https://doi.org/10.3389/fpsyg.2018.02086
Fu, Y., & Li, Q. (2023). A virtual reality–based serious game for fire safety behavioral skills training. International Journal of Human–Computer Interaction, 1–17.
Fujimoto, K., & Ashida, H. (2020). Different Head-Sway Responses to Optic Flow in Sitting and Standing With a Head-Mounted Display. Frontiers in Psychology, 11, 577305. https://doi.org/10.3389/fpsyg.2020.577305
Gong, Y., Zhang, S., Liu, Z., & Shen, F. (2016). Eye movement study on color effects to icon visual search efficiency. J. Zhejiang Univ, 50, 1987–1994.
Harris, L. R., Carnevale, M. J., Dâ€^TMAmour, S., Fraser, L. E., Harrar, V., Hoover, A. E. N., Mander, C., & Pritchett, L. M. (2015). How our body influences our perception of the world. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.00819
Harrison, C., & Dey, A. K. (2008). Lean and zoom: Proximity-aware user interface and content magnification. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 507–510. https://doi.org/10.1145/1357054.1357135
Horak, F. B. (2006). Postural orientation and equilibrium: What do we need to know about neural control of balance to prevent falls? Age and Ageing, 35(suppl_2), ii7–ii11. https://doi.org/10.1093/ageing/afl077
Jang, W., Shin, J.-H., Kim, M., & Kim, K. (Kenny). (2016). Human field of regard, field of view, and attention bias. Computer Methods and Programs in Biomedicine, 135, 115–123. https://doi.org/10.1016/j.cmpb.2016.07.026
Kim, A., Kim, M. W., Bae, H., & Lee, K.-M. (2020). Exploring the Relative Effects of Body Position and Spatial Cognition on Presence When Playing Virtual Reality Games. International Journal of Human–Computer Interaction, 36(18), 1683–1698. https://doi.org/10.1080/10447318.2020.1775373
Klatt, S., Noël, B., & Schrödter, R. (2024). Attentional asymmetries in peripheral vision. British Journal of Psychology, 115(1), 40–50. https://doi.org/10.1111/bjop.12676
Kumar, N., Lim, C. H., Sardar, S. K., Park, S. H., & Lee, S. C. (2023). Effects of posture and locomotion methods on postural stability, cybersickness, and presence in a virtual environment. International Journal of Human–Computer Interaction, 1–13.
Liebers, C., Prochazka, M., Pfützenreuter, N., Liebers, J., Auda, J., Gruenefeld, U., & Schneegass, S. (2023). Pointing It Out! Comparing Manual Segmentation of 3D Point Clouds between Desktop, Tablet, and Virtual Reality. International Journal of Human–Computer Interaction, 1–15. https://doi.org/10.1080/10447318.2023.2238945
Lou, X., Li, X. A., Hansen, P., & Du, P. (2021). Hand-adaptive user interface: Improved gestural interaction in virtual reality. Virtual Reality, 25(2), 367–382. https://doi.org/10.1007/s10055-020-00461-7
Otterbring, T., Shams, P., Wästlund, E., & Gustafsson, A. (2013). Left isn’t always right: Placement of pictorial and textual package elements. British Food Journal, 115(8), 1211–1225. https://doi.org/10.1108/BFJ-08-2011-0208
Patston, L. L. M., Henry, A. N., McEwen, M., Mannion, J., & Ewens-Volynkina, L. A. (2017). Thinking While Standing: An exploratory study on the effect of standing on cognitive performance. Unitec ePress. https://doi.org/10.34074/ocds.32017
Pearson, R., & Van Schaik, P. (2003). The effect of spatial layout of and link colour in web pages on performance in a visual search task and an interactive search task. International Journal of Human-Computer Studies, 59(3), 327–353. https://doi.org/10.1016/S1071-5819(03)00045-4
Pelz, J., Hayhoe, M., & Loeber, R. (2001). The coordination of eye, head, and hand movements in a natural task. Experimental Brain Research, 139(3), 266–277. https://doi.org/10.1007/s002210100745
Reed-Jones, J. G., Reed-Jones, R. J., & Hollands, M. A. (2014). Is the size of the useful field of view affected by postural demands associated with standing and stepping? Neuroscience Letters, 566, 27–31.
Rosenbaum, D., Mama, Y., & Algom, D. (2017). Stand by Your Stroop: Standing Up Enhances Selective Attention and Cognitive Control. Psychological Science, 28(12), 1864–1867. https://doi.org/10.1177/0956797617721270
Schulman, D., & Shontz, F. C. (1971). Body Posture and Thinking. Perceptual and Motor Skills, 32(1), 27–33. https://doi.org/10.2466/pms.1971.32.1.27
Shin, J. G., Kim, D., So, C., & Saakes, D. (2020). Body Follows Eye: Unobtrusive Posture Manipulation Through a Dynamic Content Position in Virtual Reality. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3313831.3376794
Soh, D. J. H., Ong, C. H., Fan, Q., Seah, D. J. L., Henderson, S. L., Jeevanandam, L., & Doshi, K. (2021). Exploring the Use of Virtual Reality for the Delivery and Practice of Stress-Management Exercises. Frontiers in Psychology, 12, 640341. https://doi.org/10.3389/fpsyg.2021.640341
Tabbaa, L., Ang, C. S., Siriaraya, P., She, W. J., & Prigerson, H. G. (2021). A Reflection on Virtual Reality Design for Psychological, Cognitive and Behavioral Interventions: Design Needs, Opportunities and Challenges. International Journal of Human–Computer Interaction, 37(9), 851–866. https://doi.org/10.1080/10447318.2020.1848161
Van Schaik, P., & Ling, J. (2001). Design Parameters in Web Pages: Frame Location and Differential Background Contrast in Visual Search Performance. International Journal of Cognitive Ergonomics, 5(4), 459–471. https://doi.org/10.1207/S15327566IJCE0504_6
Yang, X., Qian, B., Zhou, X., Zhao, Y., Wang, L., & Zhang, Z. (2022). The effects of posture on mind wandering. Psychological Research, 86(3), 737–745. https://doi.org/10.1007/s00426-021-01531-4
Zhou, Y., Zhang, Y., Hommel, B., & Zhang, H. (2017). The Impact of Bodily States on Divergent Thinking: Evidence for a Control-Depletion Account. Frontiers in Psychology, 8, 1546. https://doi.org/10.3389/fpsyg.2017.01546

No competing interests reported.

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The Interaction Efficiency of Different Visual Areas on a Virtual Reality Device Screen: Standing versus Sitting Posture

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