Fidelity describes how closely a replication resembles the original. It can be helpful to analyze how faithful interactions in virtual reality (VR) are to a reference interaction. In prior research, fidelity has been restricted to the simulation of reality—also called realism. Our definition includes other reference interactions, such as superpowers or fiction. Interaction fidelity is a multilayered concept. Unfortunately, different aspects of fidelity have either not been distinguished in scientific discourse or referred to with inconsistent terminology. Therefore, we present the Interaction Fidelity Model (IntFi Model). Based on the human-computer interaction loop, it systematically covers all stages of VR interactions. The conceptual model establishes a clear structure and precise definitions of eight distinct components. As a communication tool, it helps teams to understand and discuss fidelity in VR. It was reviewed through workshops with fourteen VR experts. We provide guidelines, diverse examples, and educational material to apply the IntFi Model universally to any VR experience and propose foundational research opportunities.

Recent advances in language processing and speech recognition open up a large opportunity for video game companies to embrace voice interaction as an intuitive feature and appealing game mechanics. However, speech-based systems still remain liable to recognition errors. These add a layer of challenge on top of the game’s existing obstacles, preventing players from reaching their goals and thus often resulting in player frustration. This work investigates a novel method called context-aware speech recognition, where the game environment and actions are used as supplementary information to enhance recognition in a speech-based game. In a between-subject user study (N = 40), we compared our proposed method with a standard method in which recognition is based only on the voice input without taking context into account. Our results indicate that our proposed method could improve the player experience and the usability of the speech system.

With video games and Esports becoming ever-present, playing as long as possible to improve proficiency has become a common strategy among video game enthusiasts. This behavior does not only bear increased health and social risks, but is also connected to performance decline, which itself inhibits skill acquisition. To promote health and advance self-regulated learning, we investigate the effects of distributed practice on skill acquisition in an ecologically valid Esports task. As research about distributed practice in video games is restricted to simple games or short practice sessions, we explore the impact of distributed versus massed practice with an extended training period of 45 minutes. Using (n = 16) participants and a complex contemporary fighting game, we show that massed practice schedules do not provide any advantage over distributed ones - suggesting that practice time can be evenly distributed without improvement losses, which would alleviate health risks connected to longer sessions.

With the rise of popularity and societal importance of video games, trends of grinding play for excessive amounts of time to improve proficiency becomes more and more common. This bears not only physical and mental health risks, but has already shown to be detrimental for skill acquisition, defeating its purpose in the first place. For the sake of health promotion, well-being, and the advancement of self-regulated learning, we specifically investigate the impact of detailed feedback on player performance. As research about comparable activities in related domains suggest inconclusive implications for when feedback in video game learning should be presented, we particularly explore the impact of delivering immediate versus delayed feedback, during or after play. Using (n = 30) participants and the well-investigated Super Mario Bros., we communicate insights in favor for deploying feedback immediately when polishing one's video game related skills such as in speedrunning.

Providing haptic feedback in virtual reality to make the experience more realistic has become a strong focus of research in recent years. The resulting haptic feedback systems differ greatly in their technologies, feedback possibilities, and overall realism making it challenging to compare different systems. We propose the Haptic Fidelity Framework providing the means to describe, understand and compare haptic feedback systems. The framework locates a system in the spectrum of providing realistic or abstract haptic feedback using the Haptic Fidelity dimension. It comprises 14 criteria that either describe foundational or limiting factors. A second Versatility dimension captures the current trade-off between highly realistic but application-specific and more abstract but widely applicable feedback. To validate the framework, we compared the Haptic Fidelity score to the perceived feedback realism of evaluations from 38 papers and found a strong correlation suggesting the framework accurately describes the realism of haptic feedback.

Professionals in domains like film, theater, or architecture often rely on physical models to visualize spaces. With virtual reality (VR) new tools are available providing immersive experiences with correct perceptions of depth and scale. However, these lack the tangibility of physical models. Using tangible objects in VR can close this gap but creates the challenges of producing suitable objects and interacting with them with only the virtual objects visible. This work addresses these challenges by evaluating tangibles with three haptic fidelities: equal disc-shaped tangibles for all virtual objects, Lego-built tangibles, and 3D-printed tangibles resembling the virtual shapes. We present results from a comparative study on immersion, performance, and intuitive interaction and interviews with domain experts. The results show that 3D-printed objects perform best, but Lego offers a good trade-off between fast creation of tangibles and sufficient fidelity. The experts rate our approach as useful and would use all three versions.

Crowdsourcing experiments online allows for low-cost data gathering with large participant pools; however, collecting data online does not give researchers access to certain metrics. For example, physiological measures such as heart rate (HR) can provide high-resolution data about the physical, emotional, and mental state of the participant. We investigate and characterize the feasibility of gathering HR from videos of online participants engaged in single user and social tasks. We show that room lighting, head motion, and network bandwidth influence measurement quality, but that instructing participants in good practices substantially improves measurement quality. Our work takes a step towards online physiological data collection.