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Andrea Stevenson Won

Publications and source records attributed to Andrea Stevenson Won.

7 recordsLinked to original sources

IMPACT: Modeling Socially Interdependent Movement in a Generative Multi-Agent Simulation of a Pompeian Household

Simulations of archaeological sites can make interpretations of past cultural practices observable and examinable. Generative multi-agent simulations offer a bottom-up approach to modeling how people collectively moved through and used historical spaces. However, current agents designed to simulate everyday life often plan and act independently, limiting their ability to capture how movement depends on others' actions. We introduce IMPACT (Interdependent Movement Planning through Inter-Agent Constraints and Triggers), an architecture that uses culturally specific roles and obligations to define dependencies among agents' activities and guide coordination. IMPACT connects socially gated milestone planning, wait-or-prompt resolution, structured directive issuance, and directive integration. These mechanisms determine whether and when activities can begin or change as social conditions evolve, producing socially constrained and prompted movement as their primary observable outcome. We instantiate IMPACT in a five-hour simulation of a Pompeian dinner involving ten agents across interdependent roles. Analysis of five simulation runs shows how social roles, responsibilities, and status relations shape household activities and spatial practices, as reflected in patterns of co-location, asymmetric waiting, co-movement, and social directives. In a controlled ablation evaluation, thirty-seven participants rated the complete architecture's behavior as more socially coherent and believable than that of two reduced architectures. Interviews with six archaeology experts highlighted historically plausible movement patterns and the simulation's potential to support archaeological interpretation, while identifying areas requiring stronger historical grounding for future work.

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Understanding the Use of a Large Language Model-Powered Guide to Make Virtual Reality Accessible for Blind and Low Vision People

As social virtual reality (VR) grows more popular, addressing accessibility for blind and low vision (BLV) users is increasingly critical. Researchers have proposed an AI "sighted guide" to help users navigate VR and answer their questions, but it has not been studied with users. To address this gap, we developed a large language model (LLM)-powered guide and studied its use with 16 BLV participants in virtual environments with confederates posing as other users. We found that when alone, participants treated the guide as a tool, but treated it companionably around others, giving it nicknames, rationalizing its mistakes with its appearance, and encouraging confederate-guide interaction. Our work furthers understanding of guides as a versatile method for VR accessibility and presents design recommendations for future guides.

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"The Guide Has Your Back": Exploring How Sighted Guides Can Enhance Accessibility in Social Virtual Reality for Blind and Low Vision People

As social VR applications grow in popularity, blind and low vision users encounter continued accessibility barriers. Yet social VR, which enables multiple people to engage in the same virtual space, presents a unique opportunity to allow other people to support a user's access needs. To explore this opportunity, we designed a framework based on physical sighted guidance that enables a guide to support a blind or low vision user with navigation and visual interpretation. A user can virtually hold on to their guide and move with them, while the guide can describe the environment. We studied the use of our framework with 16 blind and low vision participants and found that they had a wide range of preferences. For example, we found that participants wanted to use their guide to support social interactions and establish a human connection with a human-appearing guide. We also highlight opportunities for novel guidance abilities in VR, such as dynamically altering an inaccessible environment. Through this work, we open a novel design space for a versatile approach for making VR fully accessible.

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Accessible Nonverbal Cues to Support Conversations in VR for Blind and Low Vision People

Social VR has increased in popularity due to its affordances for rich, embodied, and nonverbal communication. However, nonverbal communication remains inaccessible for blind and low vision people in social VR. We designed accessible cues with audio and haptics to represent three nonverbal behaviors: eye contact, head shaking, and head nodding. We evaluated these cues in real-time conversation tasks where 16 blind and low vision participants conversed with two other users in VR. We found that the cues were effective in supporting conversations in VR. Participants had statistically significantly higher scores for accuracy and confidence in detecting attention during conversations with the cues than without. We also found that participants had a range of preferences and uses for the cues, such as learning social norms. We present design implications for handling additional cues in the future, such as the challenges of incorporating AI. Through this work, we take a step towards making interpersonal embodied interactions in VR fully accessible for blind and low vision people.

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An AI Guide to Enhance Accessibility of Social Virtual Reality for Blind People

The rapid growth of virtual reality (VR) has led to increased use of social VR platforms for interaction. However, these platforms lack adequate features to support blind and low vision (BLV) users, posing significant challenges in navigation, visual interpretation, and social interaction. One promising approach to these challenges is employing human guides in VR. However, this approach faces limitations with a lack of availability of humans to serve as guides, or the inability to customize the guidance a user receives from the human guide. We introduce an AI-powered guide to address these limitations. The AI guide features six personas, each offering unique behaviors and appearances to meet diverse user needs, along with visual interpretation and navigation assistance. We aim to use this AI guide in the future to help us understand BLV users' preferences for guide forms and functionalities.

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"I Try to Represent Myself as I Am": Self-Presentation Preferences of People with Invisible Disabilities through Embodied Social VR Avatars

With the increasing adoption of social virtual reality (VR), it is critical to design inclusive avatars. While researchers have investigated how and why blind and d/Deaf people wish to disclose their disabilities in VR, little is known about the preferences of many others with invisible disabilities (e.g., ADHD, dyslexia, chronic conditions). We filled this gap by interviewing 15 participants, each with one to three invisible disabilities, who represented 22 different invisible disabilities in total. We found that invisibly disabled people approached avatar-based disclosure through contextualized considerations informed by their prior experiences. For example, some wished to use VR's embodied affordances, such as facial expressions and body language, to dynamically represent their energy level or willingness to engage with others, while others preferred not to disclose their disability identity in any context. We define a binary framework for embodied invisible disability expression (public and private) and discuss three disclosure patterns (Activists, Non-Disclosers, and Situational Disclosers) to inform the design of future inclusive VR experiences.

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Perspectives from Naive Participants and Experienced Social Science Researchers on Addressing Embodiment in a Virtual Cyberball Task

We describe the design of an immersive virtual Cyberball task that included avatar customization, and user feedback on this design. We first created a prototype of an avatar customization template and added it to a Cyberball prototype built in the Unity3D game engine. Then, we conducted in-depth user testing and feedback sessions with 15 Cyberball stakeholders: five naive participants with no prior knowledge of Cyberball and ten experienced researchers with extensive experience using the Cyberball paradigm. We report the divergent perspectives of the two groups on the following design insights; designing for intuitive use, inclusivity, and realistic experiences versus minimalism. Participant responses shed light on how system design problems may contribute to or perpetuate negative experiences when customizing avatars. They also demonstrate the value of considering multiple stakeholders' feedback in the design process for virtual reality, presenting a more comprehensive view in designing future Cyberball prototypes and interactive systems for social science research.

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