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Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
Preliminary Study on Virtual Reality Framework for Effect...
Satoshi Fukumori, Kayoko Miura, Ayako Takamori, Sadao Otsuka · 2024-12-06 · via cs.HC updates on arXiv.org

Prospective memory (PM), defining the currently conceived intention of a future action, is crucial for daily functioning, particularly in aging populations. This study develops and validates a virtual reality prospective memory training (VR-PMT) system that integrates visual imagery training (VIT) and virtual reality training (VRT) to enhance the PM abilities of users. The framework is designed to progressively challenge users by simulating real-life PM tasks in a controlled VR environment. The VIT component is designed to improve the generation and utilization of visual imagery by users, while the VRT component provides PM tasks based on time and event cues within a virtual environment.The framework was evaluated on ten healthy adults (university students and elderly participants) over nine weeks. During the initial session, the baseline PM abilities of the participants were assessed using the memory for intentions screening test (MIST). The subsequent sessions alternated between VIT and VRT with increasing task complexity. The MIST scores were significantly positively correlated with task achievement, confirming the efficacy of the system. Imagery abilities were also strongly correlated with task performance, underscoring the importance of visual imagery in PM training.Usability and user experiences, evaluated on the Jikaku-sho Shirabe questionnaire and the user experience questionnaire, indicated an overall positive user experience but higher fatigue levels in elderly participants. This study demonstrates that the VR--PMT system effectively trains and assesses PM abilities by integrating VIT and VRT, supporting its potential for broader applications in clinical settings.