We are happy to share that we published our latest work on a new tool for Mixed Reality research at the ACM CHI Conference: the Mixed Reality Concerns Questionnaire! The questionnaire evaluates people’s apprehensions and concerns about MR, helping researchers and practitioners assess whether their MR prototypes are raising more worry than utility.

Neha Balamurugan starts a position as a research intern. Neha researches how mixed reality interfaces can be adapted during safety-critical situations.

Our research about AI placebos obtains media coverage. In our research, we show that the narrative of an AI-based assistive or augmentation system leads users to misjudge their performance and risk-behavior.

Current research in Mixed Reality (MR) presents a wide range of novel use cases for blending virtual elements with the real world. This yet-to-be-ubiquitous technology challenges how users currently work and interact with digital content. While offering many potential advantages, MR technologies introduce new security, safety, and privacy challenges. Thus, it is relevant to understand users’ apprehensions towards MR technologies, ranging from security concerns to social acceptance. To address this challenge, we present the Mixed Reality Concerns (MRC) Questionnaire, designed to assess users’ concerns towards MR artifacts and applications systematically. The development followed a structured process considering previous work, expert interviews, iterative refinements, and confirmatory tests to analytically validate the questionnaire. The MRC Questionnaire offers a new method of assessing users’ critical opinions to compare and assess novel MR artifacts and applications regarding security, privacy, social implications, and trust.

In the medical field, patients often experience tangible benefits from treatments they expect will improve their condition, even if the treatment has no mechanism of effect. This phenomenon often obscuring scientific evaluation of human treatment is termed the “placebo effect.” Latest research in human-computer interaction has shown that using cutting-edge technologies similarly raises expectations of improvement, culminating in placebo effects that undermine evaluation efforts for user studies. This workshop delves into the role of placebo effects in human-computer interaction for cutting-edge technologies such as artificial intelligence, its influence as a confounding factor in user studies, and identifies methods that researchers can adopt to reduce its impact on study findings. By the end of this workshop, attendees will be equipped to incorporate placebo control measures in their experimental designs.

Electromyography (EMG) enables hands-free interactions by detecting muscle activity at different human body locations. Previous studies have demonstrated that input performance based on isometric contractions is muscle-dependent and can benefit from synchronous biofeedback. However, it remains unknown whether stimulation before interaction can help to localize and tense a mus- cle faster. In a response-based VR experiment (N=21), we investigated whether prior stimulation using visual or tactile cues at four different target muscles (biceps, triceps, upper leg, calf) can help reduce the time to perform isometric muscle contractions. The results show that prior stimulation decreases EMG reaction times with visual, vibrotactile, and electrotactile cues. Our experiment also revealed important findings regarding learning and fatigue at the different body locations. We provide qualitative insights into the participants’ perceptions and discuss potential reasons for the improved interaction. We contribute with implications and use cases for prior stimulated muscle activation.