Sabarish V. Babu

Effective collaboration in Virtual Reality depends on users' ability to accurately perceive the affordances of others within a shared space. This paper presents the first empirical investigation of how people judge others' reaching capabilities in Immersive Virtual Environments (IVEs). We examined whether observers can reliably estimate the maximum horizontal reach distance of virtual actors, how different actor representations influence these perceptual judgments, and whether calibration feedback can improve estimation accuracy. Participants (N=35) judged the reachability of objects for virtual actors across three visual fidelity conditions: high-fidelity, low-fidelity, and end-effector-only representations. Results demonstrate that users can successfully estimate others' horizontal reachability in IVEs, though with overestimation. Calibration through action feedback significantly improved estimation accuracy, with overestimation decreasing across trials. Visual fidelity showed no main effect on initial reachability judgments but significantly affected post-calibration performance, with low-fidelity representations demonstrating significantly better calibration outcomes compared to high-fidelity avatars, while end-effector-only representations showed no significant difference from either condition. The findings contribute to the social perception literature in Virtual Reality (VR) and to provide design guidelines for avatar systems in collaborative virtual environments, where spatial coordination and shared action planning are critical.

Immersive Virtual Environments (IVEs) incorporating tangible graspable objects are becoming more accessible for training and other applications. Visual and proprioceptive information has been shown to be important for accurate distance and size perception. However, previous research has shown that users overestimate the size of tangible objects in IVEs. To address this issue, researchers have demonstrated the use of calibration to alter users' perceptions. In this study, we examined the carryover effects of calibration on the perceived size of graspable dials in IVEs. Participants wore head-mounted displays and experienced two calibration perturbations: Divergent Plus (physical dials 10% larger than virtual) and Divergent Minus (physical dials smaller than virtual). These were compared to the baseline Convergent condition (equal diameters). The results showed significant effects of the calibration on the accuracy of diameter estimates. Overall, a higher overestimation of dial sizes for the haptics-only condition was observed as compared to the vision-only condition. Additionally, participants' estimations worsened when haptic information was added to the vision during calibration but improved when vision information was added to the haptics-only condition during calibration. These findings highlight the importance of calibration in improving the accuracy of size perception in IVEs and contribute to our understanding of how tangible graspable objects are perceived in immersive virtual environments.

Large Language Model (LLM) -based conversational agents have become increasingly popular in recent years due to their novel capacity for natural, human-like dialogue interactions. However, mistrust in LLMs persists due to concerns about privacy, the potential for incorrect responses (often referred to as “hallucinations”), and issues related to social bias. Previous AI research shows that anthropomorphic form positively influences users’ perceptions. However, this aspect remains under-explored in LLM-based conversational agent research. Our research features two anthropomorphic forms: embodied and behavioral. Embodied Anthropomorphic Form (EA) encompasses chatbot, chatbot with Text-to-Speech (TTS) , and Embodied Conversational Agent (ECA) interface designs. Behavioral Anthropomorphic (BA) Form involves LLMs instructed with and without Theory of Mind (ToM) principles. In an empirical evaluation, we explored how interplay between BA form and EA form, and vice-versa, affects users’ perceptions of LLM-based conversational agents on trust, anthropomorphism, presence, usability, and user experience. Our findings provide evidence of such effects, offering novel insight into the influence of both anthropomorphic forms on perceived anthropomorphism, presence, usability, user experience, and their positive impact on user trust in LLM-based conversational agents. However, the combined highest (i.e., ECA with ToM behaviors ) and lowest (i.e., Chatbot without ToM behaviors ) levels of both forms result in lower user trust, suggesting a complex relationship between embodiment and ToM behaviors that warrants further investigation.

In immersive virtual environments (IVEs), accurate size perception is critical, especially in training simulations designed to mimic real-world tasks, such as, nuclear power plant control room or medical procedures. These simulations have dials or instruments of varying sizes. Visual information of the objects alone, often fails to capture subtle size differences in virtual reality (VR). However, integrating haptic and hand-avatars may potentially improve accuracy and performance. This improvement could be especially beneficial for real-world scenarios where hand(s) are intermittently visible or obscured. To investigate how this intermittent presence or absence of body-scaled hand-avatars affects size perception when integrated with haptic information, we conducted 2×2 mixed-factorial experiment design using a near-field, size-estimation task in VR. The experiment conditions compared size estimations with or without virtual hand visibility in the perception and reporting phases. The task involved 16 graspable objects of varying sizes and randomly repeated 3 times across 48 trials per participant (total 80 participants). We employed Linear Mixed Models (LMMs) analysis to objective measures: perceived size, residual error and proportional errors. Results revealed that as the tangible-graspable size increases, overestimation reduces if the hand-avatars are visible in the reporting phase. Also, overestimation reduces as the number of trials increases, if the hand-avatars are visible in the reporting phase. Thus, the presence of hand-avatars facilitated perceptual calibration. This novel study, with different combinations of hand-avatar visibility, taking perception and reporting of size as two separate phases, could open future research directions in more complex scenarios for refined integration of sensory modalities and consequently enhance real-world application performance.

Background: Numerous mental health apps purport to alleviate depressive symptoms. Strong evidence suggests that brief cognitive behavioral therapy (bCBT)-based mental health apps can decrease depressive symptoms, yet there is limited research elucidating the specific features that may augment its therapeutic benefits. One potential design feature that may influence effectiveness and user experience is the inclusion of virtual agents that can mimic realistic, human face-to-face interactions. Objective: The goal of the current experiment was to determine the effect of conversational and animation features of a virtual agent within a bCBT-based mental health app on depressive symptoms and user experience in college students with and without depressive symptoms. Methods: College students (N=209) completed a 2-week intervention in which they engaged with a bCBT-based mental health app with a customizable therapeutic virtual agent that varied in conversational and animation features. A 2 (time: baseline vs 2-week follow-up) × 2 (conversational vs non-conversational agent) × 2 (animated vs non-animated agent) randomized controlled trial was used to assess mental health symptoms (Patient Health Questionnaire-8, Perceived Stress Scale-10, and Response Rumination Scale questionnaires) and user experience (mHealth App Usability Questionnaire, MAUQ) in college students with and without current depressive symptoms. The mental health app usability and qualitative questions regarding users’ perceptions of their therapeutic virtual agent interactions and customization process were assessed at follow-up. Results: Mixed ANOVA (analysis of variance) results demonstrated a significant decrease in symptoms of depression (P=.002; mean [SD]=5.5 [4.86] at follow-up vs mean [SD]=6.35 [4.71] at baseline), stress (P=.005; mean [SD]=15.91 [7.67] at follow-up vs mean [SD]=17.02 [6.81] at baseline), and rumination (P=.03; mean [SD]=40.42 [12.96] at follow-up vs mean [SD]=41.92 [13.61] at baseline); however, no significant effect of conversation or animation was observed. Findings also indicate a significant increase in user experience in animated conditions. This significant increase in animated conditions is also reflected in the user’s ease of use and satisfaction (F(1, 201)=102.60, P<.001), system information arrangement (F(1, 201)=123.12, P<.001), and usefulness of the application (F(1, 201)=3667.62, P<.001). Conclusions: The current experiment provides support for bCBT-based mental health apps featuring customizable, humanlike therapeutic virtual agents and their ability to significantly reduce negative symptomology over a brief timeframe. The app intervention reduced mental health symptoms, regardless of whether the agent included conversational or animation features, but animation features enhanced the user experience. These effects were observed in both users with and without depressive symptoms.