CORTEXA
← Browse
arxivcs.HCphysics.chem-ph2026-07-14

Sensing the properties of virtual objects without physical feedback

Rhoslyn Roebuck Williams, Harry J. Stroud, Luis E. Toledo, Mark D. Wonnacott, David R. Glowacki

People who have interacted with simulated worlds and simulated objects in extended reality (XR) often have a sense that they can 'feel' the objects being simulated despite them not being physical. Our sense of touch is essential for how we 'feel' the physical world, however, there is an open question as to what it means to 'feel' virtual objects when interacting with them in immersive digital environments. In prior research, we have reported that participants often describe a subjective experience of 'feeling' the properties of simulated molecular objects while using interactive molecular dynamics in extended reality (iMD-XR), a field-based interaction paradigm for manipulating real-time simulations of molecular objects without haptic feedback. To better understand these subjective reports of 'feeling', we used a psychophysics approach to quantify the threshold at which participants perceive differences in the rigidity of simulated molecular objects (C$_{60}$ molecules) in iMD-XR. To evaluate this, we carried out experiments to compare the just-noticeable differences (JNDs) in two conditions: (1) via direct interaction with a real-time C$_{60}$ simulation, and (2) via observation-only$\unicode{x2013}$i.e. watching another person interacting with the simulations. Our findings show that direct interaction enabled participants to perceive more subtle rigidity differences of 11.5%, compared to 18.5% for observation-only. Furthermore, participants who undertook interaction first were better able to distinguish rigidity differences in the subsequent observation-only condition, suggesting that interaction trained participants to better perceive differences in molecular properties. These findings demonstrate a novel and flexible approach for sensing the properties of virtual objects in XR, and offer new insights into iMD-XR's potential in molecular research and education.

View free PDFSource page

Related papers

arxivcs.ROcs.HC2026-07-13

Requirement-Driven Design of Whole-Body Social Tactile Sensing via Virtual Human-Robot Interaction

Dakarai Crowder, Ruohan Zhang, Alexis E. Block, Wenzhen Yuan

Tactile sensing for social-physical human-robot interaction (spHRI) is designed in a hardware-driven manner, where predefined sensor configurations constrain coverage, spatial resolution, and the range of recognizable gestures. We propose a requirement-driven framework that deriv…

View free PDFSource page
arxivcs.HC2026-07-18

Retrofitting Existing 3D Objects with Surface-Conforming Capacitive Sensing

Andela Ilic, Junpeng Gao, Zhipeng Li, Yijing Jiang, Rachel Schuchert, Manuel Meier, et al.

Augmenting the surface of 3D objects with capacitive sensing is challenging when their volumes cannot be modified. In this paper, we present a generative computational fabrication pipeline that retrofits surface-only sensor layouts to 3D geometries for multi-touch interaction. Ou…

View free PDFSource page
arxivcs.HC2026-07-11

SyncSpace: Layout-Conditioned 3D Gaussian Splatting for Space Reskinning in Mixed Reality

Qinchuan Zhang, Weibo Xu, Yunge Wen

We present SyncSpace, a system that achieves both spatial alignment and visual consistency between a generated 3DGS world and physical space. We first scan the space via depth sensing to extract 3D bounding boxes, which we render into a layout-only panorama and feed as a geometri…

View free PDFSource page
arxivcs.SEcs.HC2026-07-11

VRExplorer: A Model-based Approach for Semi-Automated Testing of Virtual Reality Scenes

Zhengyang Zhu, Hong-Ning Dai, Hanyang Guo, Zeqin Liao, Zibin Zheng

With the proliferation of Virtual Reality (VR) markets, VR applications are rapidly expanding in scale and complexity, thereby driving an urgent need for assuring VR software quality. Different from traditional mobile applications and computer software, VR testing faces unique ch…

View free PDFSource page
arxivcs.HCcs.AI2026-07-20

Human-in-the-Loop User Feedback Affects Perceived Accuracy and Trust, but Task Subjectivity Matters

Donald R. Honeycutt, Mahsan Nourani, Eric D. Ragan

While ML can produce complex models beyond those that a human could produce manually, incorporating human input can often improve performance beyond purely data-driven models. While this feedback could come from system designers or domain experts, in many cases, the end users who…

View free PDFSource page