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Task performance is a significant focus within the domain of Human-Computer Interaction (HCI), particularly with the advent of technologies like Virtual Reality (VR). Understanding how individuals leverage VR for productive task execution is crucial as researchers continually seek ways to optimize human performance in digital spaces. Various studies have delved into factors influencing human performance in VR settings, including the engagement of primary senses - visual, auditory, and tactile (haptic). However, a notable gap exists in understanding how the intensity and availability of haptic feedback via VR controllers impact users cognitively during task performance. This research project aims to identify the optimal degree of haptic feedback that enhances productivity without causing discomfort. Addressing the research gap surrounding the utilization of varied haptic feedback intensities and availability via VR controllers is pivotal to creating more immersive user experiences. This improvement has the potential to offer more engaging experiences to VR users, particularly those engaged in cognitive-based applications. The primary challenge lies in determining the most effective combination of haptic feedback to enhance user productivity and effectiveness. By tackling this challenge, users can achieve greater success in VR-based tasks, especially in less-than-ideal environments outside the lab. The findings from this research illustrated that participants demonstrated higher performance and reduced frustration when exposed to moderate and consistent haptic feedback. The study suggested that while haptics can be beneficial, their usage should be moderated to avoid overwhelming or discomforting users.
Abstract
Task performance is a significant focus within the domain of Human-Computer Interaction (HCI), particularly with the advent of technologies like Virtual Reality (VR). Understanding how individuals leverage VR for productive task execution is crucial as researchers continually seek ways to optimize human performance in digital spaces. Various studies have delved into factors influencing human performance in VR settings, including the engagement of primary senses - visual, auditory, and tactile (haptic). However, a notable gap exists in understanding how the intensity and availability of haptic feedback via VR controllers impact users cognitively during task performance.
This research project aims to identify the optimal degree of haptic feedback that enhances productivity without causing discomfort. Addressing the research gap surrounding the utilization of varied haptic feedback intensities and availability via VR controllers is pivotal to creating more immersive user experiences. This improvement has the potential to offer more engaging experiences to VR users, particularly those engaged in cognitive-based applications.
The primary challenge lies in determining the most effective combination of haptic feedback to enhance user productivity and effectiveness. By tackling this challenge, users can achieve greater success in VR-bascd tasks, especially in less-than-idcal environments outside the lab.
The findings from this research illustrated that participants demonstrated higher performance and reduced frustration when exposed to moderate and consistent haptic feedback. The study suggested that while haptics can be beneficial, their usage should be moderated to avoid overwhelming or discomforting users.
Keywords
Haptic Feedback, Virtual Reality, Task Performance, Human-Computer-Interaction.
1. Introduction
In the fields of Human Factors Engineering (HFE) and Human-Computer Interaction (HCI), understanding task performance is critical for enhancing individual skills in work settings, enabling organizations to achieve their objectives more effectively [1, 2]. Researchers have extensively explored methods to improve task performance, aiming to increase productivity across various domains [3]. Advanced technologies have been employed to better understand user performance, leading to significant advancements in HFE [4-6]. These include the development of human-centered technologies that enhance work processes, address human needs, and support organizational and societal goals. Such innovations also consider natural human limitations, ensuring that technology aligns with users' capabilities and helps them achieve their objectives [7, 8].
As the world continues to evolve with advanced technologies such as Virtual Reality (VR), researchers must leverage these tools to better understand and enhance human performance in training environments across various domains. This study focuses on improving human performance in VR, particularly emphasizing multimodal interfaces and their role in performance enhancement. Unlike typical applications in gaming and entertainment, this research explores practical settings where focus and engagement are critical, such as training systems. In real-world field settings, unlike controlled environments such as laboratories or entertainment setups, the consistency and availability of haptic feedback can often be unreliable. This unreliability may stem from technical challenges, such as unstable internet bandwidth, which is less predictable than the stable conditions in lab environments. As a result, the inconsistency of haptic feedback in VR applications can reduce their reliability and user comfort, making them less effective.
Immersive VR experiences rely on engaging multiple senses, primarily vision through the eyes, and audio through the ears. Some scientists are exploring additional senses by using tactile haptics feedback through touch [9]. This study investigates the role of different levels of haptic feedback as a form of multimodal sensory engagement through virtual touch. The goal is to identify optimal settings where haptic feedback enhances user engagement and learning without becoming a distraction. By doing so, this research aims to make VR-based training more engaging, effective, and satisfactory for users, ultimately advancing the field of Virtual Reality.
The application of VR in this research, and VR-based research in general, is not intended to replace existing realworld training techniques but rather to serve as an additional tool that complements current methods for learning and skill development across various domains. The primary objective of this study is to enhance the learnability process for users training in VR, with a focus on improving their experience through haptic feedback. By integrating haptic feedback via VR controllers, users can engage with tactile sensations while performing cognitively demanding tasks, thereby enriching their training experience. Additionally, this research aims to identify the most optimal combinations of haptic feedback that enhance user engagement without increasing cognitive workload during VR task performance.
2. Problem Description
While many studies have explored haptic feedback in VR, few have examined how combinations of intensity and availability impact productivity or task performance via VR controllers. This research bridges that gap by investigating how different haptic feedback configurations affect user performance in cognitive-based VR tasks. As VR becomes more integrated into daily life, understanding these effects is crucial for optimizing immersive experiences. Hence the primary goal of this study is to explore how varying intensities and availability of haptic feedback, delivered via VR controllers, affect user performance during tasks in a virtual environment.
3. Related Research
Haptic interaction refers to manipulation or sense of touch. This concept can be applied to touch-based interactions involving humans, machines, or a combination of both, and it can be implemented in real, virtual, or tclcoperatcd environments [10, 11]. In the field of Human-Computer Interaction (HCI), haptic feedback is a design element that incorporates the sensation of touch to enhance user experience [12]. Humans arc often surrounded by large amounts of information, particularly during task performance, making it essential to develop ways to process this information effectively. Multimodal sensory input, including touch, plays a critical role in achieving this.
3.1. Haptics Feedback in VR
The study of haptics in VR research is not new, as it has been a focus of continuous exploration in recent years. For instance, Anatole Lécuyer, a pioneer in the field of haptics, has conducted numerous studies aimed at enhancing the experiences of VR users [13-15]. In one of his notable studies, Lécuyer found that the perception of self-motion in virtual reality could be significantly improved through the use of appropriate haptic feedback, thereby creating a more immersive and engaging experience for users. As revealed by several research studies, haptic feedback is widely recognized as a tool to elevate human experiences in VR, making interactions more realistic and compelling [16-18].
Krogmeier, et al. [19] conducted a study to explore the impact of haptic feedback on human interactions with virtual characters. Using a haptic vest, the researchers examined whether delivering haptic feedback to VR participants during these interactions influenced their perception and physiological arousal within the virtual environment. The findings revealed that haptic feedback significantly enhances the realism and immersion of human-virtual character interactions. This study highlights how haptic feedback can improve the quality of VR experiences, refining human perception and interaction in virtual environments. In a related study Basdogan, et al. [20], investigated the role of touch in shared virtual environments, focusing on how haptic feedback combined with visual feedback influences the sense of togetherness and task performance among remotely collaborating participants. Their results demonstrated that haptic feedback not only enhances task performance but also fosters a stronger sense of connection among users, emphasizing its value in collaborative virtual settings.
3.2. Challenges in Multimodal Feedback in VR
A significant challenge in VR system design is the tendency to focus on ideal conditions, overlooking the variability encountered in real-world scenarios. For instance, users in areas with poor Wi-Fi connectivity or those performing tasks in complex field environments may experience inconsistent feedback systems. Understanding how these inconsistencies impact user performance is crucial for designing more robust and inclusive VR systems. This paper addresses this gap by examining the effects of inconsistent haptic feedback on user performance in non-ideal conditions.
3.3. Availability and Intensity of Haptic Feedback in VR
In VR, creating an authentic haptic experience is key to deepening immersion [21]. This requires careful control over the availability and intensity of haptic feedback to ensure a realistic sensory experience. While the availability of haptic feedback is critical, research on its role in VR remains limited. On the other hand, the intensity of haptic feedback which is defined as the strength and magnitude of tactile sensations, has been shown to significantly impact user performance and perception [22,23] Therefore hitting the right balance in intensity and occurrence or availability of haptic feedback is essential to ensure feedback feels realistic without being overwhelming or uncomfortable [24]. This research aims to identify the effects of combinations of haptic feedback intensity and availability to enhance user productivity in VR without causing distraction or discomfort.
4. Methodology
A total of 32 participants were recruited for this study, as an approach implemented in the first author's master's thesis[25]. Each participant was assigned a VR puzzle task designed to engage cognitive abilities within an immersive virtual environment. The VR puzzle application, developed using Unreal Engine, featured 32 unique puzzles divided into four seasonal themes: Spring, Summer, Fall, and Winter. Each puzzle consisted of 36 virtual pieces and incorporated variations in haptic feedback availability and intensity, creating distinct treatment conditions. The study examined ten treatment conditions, with five conditions focusing on haptic feedback availability and the other five on haptic feedback intensity. However, two combinations were identical (0% availability and 0% intensity, and 100% availability and 50% intensity), resulting in eight unique conditions for analysis. Further details on the treatment conditions arc provided in the subsequent section.
4.1. Treatment conditions (Greco-Latin Square Design):
The VR puzzle settings utilized haptic feedback delivered through Oculus controllers paired with a VR head-mounted display (HMD). These controllers provided tactile feedback based on user interactions during puzzle assembly. To mitigate potential learning effects and maintain participant engagement, the puzzles were designed around four seasonal themes: Spring, Summer, Fall, and Winter.
The study utilized a mixed Greco-Latin square design [26], a methodology commonly used in research requiring pairwise multiple comparisons [27-29] rather than traditional main effects analysis in a between-subjects ANO VA framework. Each participant was exposed to four of the eight treatment conditions, with each treatment paired with a distinct seasonal theme for the VR puzzle. Two of the four treatments varied in haptic feedback intensity (ranging from 0% to 100% vibration strength), while the other two varied in haptic feedback availability (ranging from 0% to 100% of puzzle pieces providing feedback). The order of treatments and seasonal themes was balanced across participants using the Greco-Latin square design, ensuring each participant had a unique and distinct experience. For example, as illustrated in Table 1, Participant One was assigned the following treatments: Treatment 1 (0% availability and 0% intensity) as the first condition, Treatment 8 (100% intensity and 100% availability) as the second, Treatment 2 (25% availability and 50% intensity) as the third, and Treatment 7 (75% intensity and 100% availability) as the fourth. The Greco-Latin square design combines within-subject (repeated measures) and between-subject approaches. Participants were intentionally limited to four treatments to avoid overwhelming them with excessive tasks or duration. This within-subject aspect allowed each participant to contribute multiple data points, even though they did not experience all the conditions. During data analysis, the between-subjects aspect became relevant, as it involved comparing data across intensity and availability groups derived from the aggregated within-subject data.
The study examined eight distinct combinations of haptic feedback intensity (vibration strength) and availability (proportion of puzzle pieces providing feedback). Participants were randomly assigned to experience four treatment conditions, each involving two levels of haptic feedback availability (Avail) and two levels of intensity (Int). As a result, each participant completed four puzzles, each corresponding to a unique treatment condition.
In the haptic feedback availability condition, only a subset of puzzle pieces provided vibration when interacted with, while others did not. For example, in a Spring puzzle with 25% availability, nine out of 36 pieces (25%) were randomly selected to vibrate at a fixed intensity of 50%. This intensity was chosen to ensure the feedback was neither too strong nor too weak. In contrast, the intensity condition maintained 100% availability but varied the strength of the vibration across pieces. The feedback treatment options were as follows:
* Treatment 1 : Avail (0%), Int (0%)
* Treatment 2: Avail (25%), Int (50%)
* Treatment 3: Avail (50%), Int (50%)
* Treatment 4: Avail (75%), Int (50%)
* Treatment 5: Avail (100%), Int (25%)
* Treatment 6: Avail (100%), Int (50%)
* Treatment 7: Avail (100%), Int (75%)
* Treatment 8 : Avail ( 100%), Int ( 100%)
4.2. Procedure
After completing a demographics questionnaire, participants were guided to put on the VR headset and controllers while remaining seated. A brief tutorial was provided to familiarize them with manipulating puzzle pieces in the VR environment using the controllers. Following the tutorial, participants began the study by assembling four VR puzzles, each corresponding to a different assigned treatment condition. The start and end times for each puzzle assembly were recorded to measure completion times.
After completing each puzzle, participants removed the headset and filled out a NASA TLX survey to provide subjective feedback on their experiences with the varying conditions of haptic feedback intensity and availability. This process was repeated after every puzzle. Once all four puzzles were completed, participants removed the headset for the final time.
4.3. NASA Task Load Index (NASA TLX)
Cognitive and mental workload were measured using the NASA-TLX subjective rating questionnaire [30]. Participants provided their subjective assessments through a Likert scale format, which evaluated various dimensions of mental and cognitive workload. Because time to complete the puzzle was not an emphasis of the task, the NASA TLX subjective measures were a primary dependent measure of task performance for this task.
5. Results
Data collection was conducted on the campus of a large university in the Midwestern United States from March to July 2023. Out of 40 initially recruited participants, 8 withdrew due to scheduling conflicts. Among the remaining participants, the gender distribution included seven males (21.88% of the sample), 24 females (75%), and one individual who identified as non-binary.
Participants were asked to complete the NASA TLX survey after each VR puzzle assembly task. They provided feedback using a 7-point Likert scale, evaluating their experience across the six NASA TLX subscales: Mental Demand, Physical Demand, Temporal Demand, Performance, Effort, and Frustration. Figure 2 was generated based on the raw scores from participants' responses, showing that Performance and Frustration received the highest average ratings, while Mental Demand had the lowest average score.
The results indicated that only some combinations of feedback had some perceived impact on workload as seen in Table 2. Participants perceived the puzzle task as relatively easy. This perception may stem from the researcher's intentional design of an engaging and straightforward VR puzzle assembly task. The study prioritized enhancing user experience and ensuring fiili immersion in the VR system while collecting data on cognitive responses to haptic feedback combinations. Hence Table 2 below shows that the NASA TLX subscales for Mental, Physical, Temporal, and Effort did not show significant differences, likely because participants found the task easy to complete and not particularly demanding in terms of mental, physical, or temporal effort.
An inverse relationship was observed between the Performance and Frustration subscales: participants perceived better performance and lower frustration levels when haptic feedback was consistent and of moderate intensity during the puzzle assembly task.
5.1. Implications of Results
Creating positive VR experiences is crucial, especially in high-stakes applications like training for expert performance in simulated dangerous scenarios. These experiences should enable participants to focus on the cognitive aspects of task performance without feeling overwhelmed. While haptic feedback can significantly impact these experiences, this study demonstrates that inconsistent haptic feedback may lead to frustration and undermine the intended benefits of engagement with VR tasks.
6. Conclusions
This study aimed to address a gap in VR research by exploring how different combinations of haptic feedback intensity and availability affect human cognitive performance. A total of 32 participants used a Meta Quest headset and VR controllers to assemble four puzzles under varying haptic feedback conditions-four treatments altered availability, while four others adjusted intensity. After completing the tasks, participants filled out a NASA TLX survey to evaluate their experiences. The results showed that participants performed better and reported less frustration when haptic feedback was moderate and consistent (100% availability, 50% intensity). The findings suggest that while haptic feedback can enhance VR experiences, it should be carefully balanced to avoid overwhelming users.
The study also revealed an inverse relationship between frustration and performance under moderate and consistent haptic feedback. To advance VR immersion, further research like this is essential. Such studies provide valuable insights that contribute to the growing body of knowledge in VR technology, ultimately improving user experiences in this field.
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