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Virtual reality applications employing avatar embodiment typically use virtual mirrors to allow users to perceive their digital selves not only from a first-person but also from a holistic third-person perspective. However, due to distance-related biases such as the distance compression effect or a reduced relative rendering resolution, the self-observation distance (SOD) between the user and the virtual mirror might influence how users perceive their embodied avatar. Our article systematically investigates the effects of a short (1 m), middle (2.5 m), and far (4 m) SOD between users and mirror on the perception of their personalized and self-embodied avatars. The avatars were photorealistic reconstructed using state-of-the-art photogrammetric methods. Thirty participants repeatedly faced their real-time animated self-embodied avatars in each of the three SOD conditions, where they were repeatedly altered in their body weight, and participants rated the 1) sense of embodiment, 2) body weight perception, and 3) affective appraisal towards their avatar. We found that the different SODs are unlikely to influence any of our measures except for the perceived body weight estimation difficulty. Here, the participants perceived the difficulty significantly higher for the farthest SOD. We further found that the participants’ self-esteem significantly impacted their ability to modify their avatar’s body weight to their current body weight and that it positively correlated with the perceived attractiveness of the avatar. Additionally, the participants’ concerns about their body shape affected how eerie they perceived their avatars. The participants’ self-esteem and concerns about their body shape influenced the perceived body weight estimation difficulty. We conclude that the virtual mirror in embodiment scenarios can be freely placed and varied at a distance of one to four meters from the user without expecting major effects on the perception of the avatar.
As an emerging market for voice assistants (VA), the healthcare sector imposes increasing requirements on the users’ trust in the technological system. To encourage patients to reveal sensitive data requires patients to trust in the technological counterpart. In an experimental laboratory study, participants were presented a VA, which was introduced as either a “specialist” or a “generalist” tool for sexual health. In both conditions, the VA asked the exact same health-related questions. Afterwards, participants assessed the trustworthiness of the tool and further source layers (provider, platform provider, automatic speech recognition in general, data receiver) and reported individual characteristics (disposition to trust and disclose sexual information). Results revealed that perceiving the VA as a specialist resulted in higher trustworthiness of the VA and of the provider, the platform provider and automatic speech recognition in general. Furthermore, the provider’s trustworthiness affected the perceived trustworthiness of the VA. Presenting both a theoretical line of reasoning and empirical data, the study points out the importance of the users’ perspective on the assistant. In sum, this paper argues for further analyses of trustworthiness in voice-based systems and its effects on the usage behavior as well as the impact on responsible design of future technology.
Artificial Intelligence (AI) covers a broad spectrum of computational problems and use cases. Many of those implicate profound and sometimes intricate questions of how humans interact or should interact with AIs. Moreover, many users or future users do have abstract ideas of what AI is, significantly depending on the specific embodiment of AI applications. Human-centered-design approaches would suggest evaluating the impact of different embodiments on human perception of and interaction with AI. An approach that is difficult to realize due to the sheer complexity of application fields and embodiments in reality. However, here XR opens new possibilities to research human-AI interactions. The article’s contribution is twofold: First, it provides a theoretical treatment and model of human-AI interaction based on an XR-AI continuum as a framework for and a perspective of different approaches of XR-AI combinations. It motivates XR-AI combinations as a method to learn about the effects of prospective human-AI interfaces and shows why the combination of XR and AI fruitfully contributes to a valid and systematic investigation of human-AI interactions and interfaces. Second, the article provides two exemplary experiments investigating the aforementioned approach for two distinct AI-systems. The first experiment reveals an interesting gender effect in human-robot interaction, while the second experiment reveals an Eliza effect of a recommender system. Here the article introduces two paradigmatic implementations of the proposed XR testbed for human-AI interactions and interfaces and shows how a valid and systematic investigation can be conducted. In sum, the article opens new perspectives on how XR benefits human-centered AI design and development.
Plenty of theories, models, measures, and investigations target the understanding of virtual presence, i.e., the sense of presence in immersive Virtual Reality (VR). Other varieties of the so-called eXtended Realities (XR), e.g., Augmented and Mixed Reality (AR and MR) incorporate immersive features to a lesser degree and continuously combine spatial cues from the real physical space and the simulated virtual space. This blurred separation questions the applicability of the accumulated knowledge about the similarities of virtual presence and presence occurring in other varieties of XR, and corresponding outcomes. The present work bridges this gap by analyzing the construct of presence in mixed realities (MR). To achieve this, the following presents (1) a short review of definitions, dimensions, and measurements of presence in VR, and (2) the state of the art views on MR. Additionally, we (3) derived a working definition of MR, extending the Milgram continuum. This definition is based on entities reaching from real to virtual manifestations at one time point. Entities possess different degrees of referential power, determining the selection of the frame of reference. Furthermore, we (4) identified three research desiderata, including research questions about the frame of reference, the corresponding dimension of transportation, and the dimension of realism in MR. Mainly the relationship between the main aspects of virtual presence of immersive VR, i.e., the place-illusion, and the plausibility-illusion, and of the referential power of MR entities are discussed regarding the concept, measures, and design of presence in MR. Finally, (5) we suggested an experimental setup to reveal the research heuristic behind experiments investigating presence in MR. The present work contributes to the theories and the meaning of and approaches to simulate and measure presence in MR. We hypothesize that research about essential underlying factors determining user experience (UX) in MR simulations and experiences is still in its infancy and hopes this article provides an encouraging starting point to tackle related questions.
The design and evaluation of assisting technologies to support behavior change processes have become an essential topic within the field of human-computer interaction research in general and the field of immersive intervention technologies in particular. The mechanisms and success of behavior change techniques and interventions are broadly investigated in the field of psychology. However, it is not always easy to adapt these psychological findings to the context of immersive technologies. The lack of theoretical foundation also leads to a lack of explanation as to why and how immersive interventions support behavior change processes. The Behavioral Framework for immersive Technologies (BehaveFIT) addresses this lack by 1) presenting an intelligible categorization and condensation of psychological barriers and immersive features, by 2) suggesting a mapping that shows why and how immersive technologies can help to overcome barriers and finally by 3) proposing a generic prediction path that enables a structured, theory-based approach to the development and evaluation of immersive interventions. These three steps explain how BehaveFIT can be used, and include guiding questions for each step. Further, two use cases illustrate the usage of BehaveFIT. Thus, the present paper contributes to guidance for immersive intervention design and evaluation, showing that immersive interventions support behavior change processes and explain and predict 'why' and 'how' immersive interventions can bridge the intention-behavior-gap.
With the increasing adaptability and complexity of advisory artificial intelligence (AI)-based agents, the topics of explainable AI and human-centered AI are moving close together. Variations in the explanation itself have been widely studied, with some contradictory results. These could be due to users’ individual differences, which have rarely been systematically studied regarding their inhibiting or enabling effect on the fulfillment of explanation objectives (such as trust, understanding, or workload). This paper aims to shed light on the significance of human dimensions (gender, age, trust disposition, need for cognition, affinity for technology, self-efficacy, attitudes, and mind attribution) as well as their interplay with different explanation modes (no, simple, or complex explanation). Participants played the game Deal or No Deal while interacting with an AI-based agent. The agent gave advice to the participants on whether they should accept or reject the deals offered to them. As expected, giving an explanation had a positive influence on the explanation objectives. However, the users’ individual characteristics particularly reinforced the fulfillment of the objectives. The strongest predictor of objective fulfillment was the degree of attribution of human characteristics. The more human characteristics were attributed, the more trust was placed in the agent, advice was more likely to be accepted and understood, and important needs were satisfied during the interaction. Thus, the current work contributes to a better understanding of the design of explanations of an AI-based agent system that takes into account individual characteristics and meets the demand for both explainable and human-centered agent systems.
Social patterns and roles can develop when users talk to intelligent voice assistants (IVAs) daily. The current study investigates whether users assign different roles to devices and how this affects their usage behavior, user experience, and social perceptions. Since social roles take time to establish, we equipped 106 participants with Alexa or Google assistants and some smart home devices and observed their interactions for nine months. We analyzed diverse subjective (questionnaire) and objective data (interaction data). By combining social science and data science analyses, we identified two distinct clusters—users who assigned a friendship role to IVAs over time and users who did not. Interestingly, these clusters exhibited significant differences in their usage behavior, user experience, and social perceptions of the devices. For example, participants who assigned a role to IVAs attributed more friendship to them used them more frequently, reported more enjoyment during interactions, and perceived more empathy for IVAs. In addition, these users had distinct personal requirements, for example, they reported more loneliness. This study provides valuable insights into the role-specific effects and consequences of voice assistants. Recent developments in conversational language models such as ChatGPT suggest that the findings of this study could make an important contribution to the design of dialogic human–AI interactions.
The concept of digital literacy has been introduced as a new cultural technique, which is regarded as essential for successful participation in a (future) digitized world. Regarding the increasing importance of AI, literacy concepts need to be extended to account for AI-related specifics. The easy handling of the systems results in increased usage, contrasting limited conceptualizations (e.g., imagination of future importance) and competencies (e.g., knowledge about functional principles). In reference to voice-based conversational agents as a concrete application of AI, the present paper aims for the development of a measurement to assess the conceptualizations and competencies about conversational agents. In a first step, a theoretical framework of “AI literacy” is transferred to the context of conversational agent literacy. Second, the “conversational agent literacy scale” (short CALS) is developed, constituting the first attempt to measure interindividual differences in the “(il) literate” usage of conversational agents. 29 items were derived, of which 170 participants answered. An explanatory factor analysis identified five factors leading to five subscales to assess CAL: storage and transfer of the smart speaker’s data input; smart speaker’s functional principles; smart speaker’s intelligent functions, learning abilities; smart speaker’s reach and potential; smart speaker’s technological (surrounding) infrastructure. Preliminary insights into construct validity and reliability of CALS showed satisfying results. Third, using the newly developed instrument, a student sample’s CAL was assessed, revealing intermediated values. Remarkably, owning a smart speaker did not lead to higher CAL scores, confirming our basic assumption that usage of systems does not guarantee enlightened conceptualizations and competencies. In sum, the paper contributes to the first insights into the operationalization and understanding of CAL as a specific subdomain of AI-related competencies.
Mobile health interventions (i.e., “apps”) are used to address mental health and are an increasingly popular method available to both individuals and organizations to manage workplace stress. However, at present, there is a lack of research on the effectiveness of mobile health interventions in counteracting or improving stress-related health problems, particularly in naturalistic, non-clinical settings. This project aimed at validating a mobile health intervention (which is theoretically grounded in the Job Demands-Resources Model) in preventing and managing stress at work. Within the mobile health intervention, employees make an evidence-based, personalized, psycho-educational journey to build further resources, and thus, reduce stress. A large-scale longitudinal randomized control trial, conducted with six European companies over 6 weeks using four measurement points, examined indicators of mental health via measures of stress, wellbeing, resilience, and sleep. The data were analyzed by means of hierarchical multilevel models for repeated measures, including both self-report measures and user behavior metrics from the app. The results (n = 532) suggest that using the mobile health intervention (vs. waitlist control group) significantly improved stress and wellbeing over time. Higher engagement in the intervention increased the beneficial effects. Additionally, use of the sleep tracking function led to an improvement in sleeping troubles. The intervention had no effects on measures of physical health or social community at work. Theoretical and practical implications of these findings are discussed, focusing on benefits and challenges of using technological solutions for organizations to support individuals’ mental health in the workplace.
In this article, we present approaches to interactive simulations of biohybrid systems. These simulations are comprised of two major computational components: (1) agent-based developmental models that retrace organismal growth and unfolding of technical scaffoldings and (2) interfaces to explore these models interactively. Simulations of biohybrid systems allow us to fast forward and experience their evolution over time based on our design decisions involving the choice, configuration and initial states of the deployed biological and robotic actors as well as their interplay with the environment. We briefly introduce the concept of swarm grammars, an agent-based extension of L-systems for retracing growth processes and structural artifacts. Next, we review an early augmented reality prototype for designing and projecting biohybrid system simulations into real space. In addition to models that retrace plant behaviors, we specify swarm grammar agents to braid structures in a self-organizing manner. Based on this model, both robotic and plant-driven braiding processes can be experienced and explored in virtual worlds. We present an according user interface for use in virtual reality. As we present interactive models concerning rather diverse description levels, we only ensured their principal capacity for interaction but did not consider efficiency analyzes beyond prototypic operation. We conclude this article with an outlook on future works on melding reality and virtuality to drive the design and deployment of biohybrid systems.
Meal-concurrent media use has been linked to several problematic outcomes, including higher caloric intake and an increased risk for obesity. Nevertheless, the sociocultural and dispositional predictors of using media while eating are not yet well-understood, including potential cross-cultural differences. Inspired by the recent emergence of a new food-related media phenomenon called “mukbang”—digital eating broadcasts that have become immensely popular throughout East and Southeast Asia—we inquire 296 participants from two cultures (Germany and South Korea) about their meal-concurrent media use. Our results suggest that South Koreans tend to use media more frequently during meals than Germans, especially for social purposes. Meanwhile, younger age only predicts meal-concurrent media use in the German sample. Apart from that, however, many other examined predictors (e.g., gender, living situation, body-esteem, the Big Five) remain statistically insignificant, indicating notable universality for the behavior in question. In the second part of our study, we then put special focus on the emerging mukbang trend and conduct a theory-driven exploration of its gratifications. Doing so, we find that participants' parasocial and social experiences during eating broadcasts significantly predict their enjoyment of the genre.
When interacting with sophisticated digital technologies, people often fall back on the same interaction scripts they apply to the communication with other humans—especially if the technology in question provides strong anthropomorphic cues (e.g., a human-like embodiment). Accordingly, research indicates that observers tend to interpret the body language of social robots in the same way as they would with another human being. Backed by initial evidence, we assumed that a humanoid robot will be considered as more dominant and competent, but also as more eerie and threatening once it strikes a so-called power pose. Moreover, we pursued the research question whether these effects might be accentuated by the robot’s body size. To this end, the current study presented 204 participants with pictures of the robot NAO in different poses (expansive vs. constrictive), while also manipulating its height (child-sized vs. adult-sized). Our results show that NAO’s posture indeed exerted strong effects on perceptions of dominance and competence. Conversely, participants’ threat and eeriness ratings remained statistically independent of the robot’s depicted body language. Further, we found that the machine’s size did not affect any of the measured interpersonal perceptions in a notable way. The study findings are discussed considering limitations and future research directions.
According to research examining self‐regulated learning (SRL), we regard individual regulation as a specific sequence of regulatory activities. Ideally, students perform various learning activities, such as analyzing, monitoring, and evaluating cognitive and motivational aspects during learning. Metacognitive prompts can foster SRL by inducing regulatory activities, which, in turn, improve the learning outcome. However, the specific effects of metacognitive support on the dynamic characteristics of SRL are not understood. Therefore, the aim of our study was to analyze the effects of metacognitive prompts on learning processes and outcomes during a computer‐based learning task. Participants of the experimental group (EG, n=35) were supported by metacognitive prompts, whereas participants of the control group (CG, n=35) received no support. Data regarding learning processes were obtained by concurrent think‐aloud protocols. The EG exhibited significantly more metacognitive learning events than did the CG. Furthermore, these regulatory activities correspond positively with learning outcomes. Process mining techniques were used to analyze sequential patterns. Our findings indicate differences in the process models of the EG and CG and demonstrate the added value of taking the order of learning activities into account by discovering regulatory patterns.
In computer-supported learning environments, the deployment of self-regulatory skills represents an essential prerequisite for successful learning. Metacognitive prompts are a promising type of instructional support to activate students’ strategic learning activities. However, despite positive effects in previous studies, there are still a large number of students who do not benefit from provided support. Therefore, it may be necessary to consider explicitly the conditions under which a prompt is beneficial for a student, i.e., so-called adaptive scaffolding. The current study aims to (i) classify the effectiveness of prompts on regulatory behavior, (ii) investigate the correspondence of the classification with learning outcome, and (iii) discover the conditions under which prompts induce regulatory activities (i.e., the proper temporal positioning of prompts). The think-aloud data of an experiment in which metacognitive prompts supported the experimental group (n = 35) was used to distinguish between effective and non-effective prompts. Students’ activities preceding the prompt presentation were analyzed using data mining and process mining techniques. The results indicate that approximately half of the presented prompts induced metacognitive learning activities as expected. Moreover, the number of induced monitoring activities correlates positively with transfer performance. Finally, the occurrence of orientation and monitoring activities, which are not well-embedded in the course of learning, increases the effectiveness of a presented prompt. In general, our findings demonstrate the benefits of investigating metacognitive support using process data, which can provide implications for the design of effective instructional support.
We provide a literature overview of 30 years of research on the amount of invested mental effort (AIME, Salomon, 1984), illuminating relevant literature in this field. Since the introduction of AIME, this concept appears to have vanished. To obtain a clearer picture of where the theory of AIME has diffused, we conducted a literature search focusing on the period 1985–2015. We examined scientific articles (N = 244) that cite Salomon (1984) and content-analyzed their keywords. Based on these keywords, we identified seven content clusters: affect and motivation, application fields, cognition and learning, education and teaching, media technology, learning with media technology, and methods. We present selected works of each content cluster and describe in which research field the articles had been published. Results indicate that AIME was most commonly (but not exclusively) referred to in the area of educational psychology indicating its importance regarding learning and education, thereby investigating print and TV, as well as new media. From a methodological perspective, research applied various research methods (e.g., longitudinal studies, experimental designs, theoretical analysis) and samples (e.g., children, college students, low income families). From these findings, the importance of AIME for further research is discussed.
Objective
Global challenges such as climate change or the COVID‐19 pandemic have drawn public attention to conspiracy theories and citizens' non‐compliance to science‐based behavioral guidelines. We focus on individuals' worldviews about how one can and should construct reality (epistemic beliefs) to explain the endorsement of conspiracy theories and behavior during the COVID‐19 pandemic and propose the Dark Factor of Personality (D) as an antecedent of post‐truth epistemic beliefs.
Method and Results
This model is tested in four pre‐registered studies. In Study 1 (N = 321), we found first evidence for a positive association between D and post‐truth epistemic beliefs (Faith in Intuition for Facts, Need for Evidence, Truth is Political). In Study 2 (N = 453), we tested the model proper by further showing that post‐truth epistemic beliefs predict the endorsement of COVID‐19 conspiracies and disregarding COVID‐19 behavioral guidelines. Study 3 (N = 923) largely replicated these results at a later stage of the pandemic. Finally, in Study 4 (N = 513), we replicated the results in a German sample, corroborating their cross‐cultural validity. Interactions with political orientation were observed.
Conclusion
Our research highlights that epistemic beliefs need to be taken into account when addressing major challenges to humankind.
A substantial number of people refused to get vaccinated against COVID-19, which prompts the question as to why. We focus on the role of individual worldviews about the nature and generation of knowledge (epistemic beliefs). We propose a model that includes epistemic beliefs, their relationship to the Dark Factor of Personality (D), and their mutual effect on the probability of having been vaccinated against COVID-19. Based on a US nationally representative sample (N = 1268), we show that stronger endorsement of post-truth epistemic beliefs was associated with a lower probability of having been vaccinated against COVID-19. D was also linked to a lower probability of having been vaccinated against COVID-19, which can be explained by post-truth epistemic beliefs. Our results indicate that the more individuals deliberately refrain from adhering to the better argument, the less likely they are vaccinated. More generally, post-truth epistemic beliefs pose a challenge for rational communication.
Over the past years, scholars have explored eudaimonic video game experiences—profound entertainment responses that include meaningfulness, reflection, and others. In a comparatively short time, a plethora of explanations for the formation of such eudaimonic gaming experiences has been developed across multiple disciplines, making it difficult to keep track of the state of theory development. Hence, we present a theoretical overview of these explanations. We first provide a working definition of eudaimonic gaming experiences (i.e., experiences that reflect human virtues and encourage players to develop their potential as human beings fully) and outline four layers of video games—agency, narrative, sociality, and aesthetics—that form the basis for theorizing. Subsequently, we provide an overview of the theoretical approaches, categorizing them based on which of the four game layers their explanation mainly rests upon. Finally, we suggest the contingency of the different theoretical approaches for explaining eudaimonic experiences by describing how their usefulness varies as a function of interactivity. As different types of games offer players various levels of interactivity, our overview suggests which theories and which game layers should be considered when examining eudaimonic experiences for specific game types.
Disfluency as a Desirable Difficulty — The Effects of Letter Deletion on Monitoring and Performance
(2018)
Desirable difficulties initiate learning processes that foster performance. Such a desirable difficulty is generation, e.g., filling in deleted letters in a deleted letter text. Likewise, letter deletion is a manipulation of processing fluency: A deleted letter text is more difficult to process than an intact text. Disfluency theory also supposes that disfluency initiates analytic processes and thus, improves performance. However, performance is often not affected but, rather, monitoring is affected. The aim of this study is to propose a specification of the effects of disfluency as a desirable difficulty: We suppose that mentally filling in deleted letters activates analytic monitoring but not necessarily analytic cognitive processing and improved performance. Moreover, once activated, analytic monitoring should remain for succeeding fluent text. To test our assumptions, half of the students (n = 32) first learned with a disfluent (deleted letter) text and then with a fluent (intact) text. Results show no differences in monitoring between the disfluent and the fluent text. This supports our assumption that disfluency activates analytic monitoring that remains for succeeding fluent text. When the other half of the students (n = 33) first learned with a fluent and then with a disfluent text, differences in monitoring between the disfluent and the fluent text were found. Performance was significantly affected by fluency but in favor of the fluent texts, and hence, disfluency did not activate analytic cognitive processing. Thus, difficulties can foster analytic monitoring that remains for succeeding fluent text, but they do not necessarily improve performance. Further research is required to investigate how analytic monitoring can lead to improved cognitive processing and performance.
Objective: Gait adaptation to environmental challenges is fundamental for independent and safe community ambulation. The possibility of precisely studying gait modulation using standardized protocols of gait analysis closely resembling everyday life scenarios is still an unmet need.
Methods: We have developed a fully-immersive virtual reality (VR) environment where subjects have to adjust their walking pattern to avoid collision with a virtual agent (VA) crossing their gait trajectory. We collected kinematic data of 12 healthy young subjects walking in real world (RW) and in the VR environment, both with (VR/A+) and without (VR/A-) the VA perturbation. The VR environment closely resembled the RW scenario of the gait laboratory. To ensure standardization of the obstacle presentation the starting time speed and trajectory of the VA were defined using the kinematics of the participant as detected online during each walking trial.
Results: We did not observe kinematic differences between walking in RW and VR/A-, suggesting that our VR environment per se might not induce significant changes in the locomotor pattern. When facing the VA all subjects consistently reduced stride length and velocity while increasing stride duration. Trunk inclination and mediolateral trajectory deviation also facilitated avoidance of the obstacle.
Conclusions: This proof-of-concept study shows that our VR/A+ paradigm effectively induced a timely gait modulation in a standardized immersive and realistic scenario. This protocol could be a powerful research tool to study gait modulation and its derangements in relation to aging and clinical conditions.