Publications by authors named "Stephen B Gilbert"

This study explored the effects of scene complexity factor on cybersickness. In this between-subjects experiment, 44 participants played the Pendulum Chair VR game, half with a simple scene and half with a complex scene. The complex scene featured higher optic flow (lower-level perceptual factor) and higher familiarity (higher level factor).

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Cybersickness, or sickness induced by virtual reality (VR), negatively impacts the enjoyment and adoption of the technology. One method that has been used to reduce sickness is repeated exposure to VR, herein Cybersickness Abatement from Repeated Exposure (CARE). However, high sickness levels during repeated exposure may discourage some users from returning.

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Multiple tools are available to reduce cybersickness (sickness caused by virtual reality), but past research has not investigated the combined effects of multiple mitigation tools. Field of view (FOV) restriction limits peripheral vision during self-motion, and ample evidence supports its effectiveness for reducing cybersickness. Snap turning involves discrete rotations of the user's perspective without presenting intermediate views, although reports on its effectiveness at reducing cybersickness are limited and equivocal.

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The Reading the Mind in the Eyes Test (RMET) has received attention due to its correlation with collective intelligence. If the RMET is a marker of collective intelligence, training to improve RMET could result in better teamwork, whether for human-human or human-AI (artificial intelligence) in composition. While training on related skills has proven effective in the literature, RMET training has not been studied.

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The wide availability of consumer-oriented virtual reality (VR) equipment has enabled researchers to recruit existing VR owners to participate remotely using their own equipment. Yet, there are many differences between lab environments and home environments, as well as differences between participant samples recruited for lab studies and remote studies. This paper replicates a lab-based experiment on VR locomotion interfaces using a remote sample.

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Virtual reality (VR) allows users to walk to explore the virtual environment (VE), but this capability is constrained by real obstacles. Teleporting interfaces overcome this constraint by allowing users to select a position, and sometimes orientation, in the VE before being instantly transported without self-motion cues. This study investigated whether individual differences in navigation performance when teleporting correspond to characteristics of the individual, including spatial ability.

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This research assessed how the performance and team skills of three-person teams working with an Intelligent Team Tutoring System (ITTS) on a virtual military surveillance task were affected by feedback privacy, participant role, task experience, prior team experience, and teammate familiarity. Previous work in Intelligent Tutoring Systems (ITSs) has focused on outcomes for task skill training for individual learners. As research extends into intelligent tutoring for teams, both task skills and team skills are necessary for good team performance.

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Virtual reality systems typically allow users to physically walk and turn, but virtual environments (VEs) often exceed the available walking space. Teleporting has become a common user interface, whereby the user aims a laser pointer to indicate the desired location, and sometimes orientation, in the VE before being transported without self-motion cues. This study evaluated the influence of rotational self-motion cues on spatial updating performance when teleporting, and whether the importance of rotational cues varies across movement scale and environment scale.

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Teleporting is a popular interface to allow virtual reality users to explore environments that are larger than the available walking space. When teleporting, the user positions a marker in the virtual environment and is instantly transported without any self-motion cues. Five experiments were designed to evaluate the spatial cognitive consequences of teleporting and to identify environmental cues that could mitigate those costs.

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Background: Although many aspects of systematic reviews use computational tools, systematic reviewers have been reluctant to adopt machine learning tools.

Discussion: We discuss that the potential reason for the slow adoption of machine learning tools into systematic reviews is multifactorial. We focus on the current absence of trust in automation and set-up challenges as major barriers to adoption.

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The third meeting of the International Collaboration for Automation of Systematic Reviews (ICASR) was held 17-18 October 2017 in London, England. ICASR is an interdisciplinary group whose goal is to maximize the use of technology for conducting rapid, accurate, and efficient systematic reviews of scientific evidence. The group seeks to facilitate the development and widespread acceptance of automated techniques for systematic reviews.

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Article Synopsis
  • * The study examined 100 preclinical experiments related to brain trauma and toxicology, focusing on design elements like comparison groups, factor arrangements, and allocation methods to identify potential biases and errors.
  • * Findings revealed common issues such as unit-of-analysis errors and complex designs, like factorial arrangements, highlighting the need for more expertise to understand biases in preclinical research than what is required for simpler study designs.
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The second meeting of the International Collaboration for Automation of Systematic Reviews (ICASR) was held 3-4 October 2016 in Philadelphia, Pennsylvania, USA. ICASR is an interdisciplinary group whose aim is to maximize the use of technology for conducting rapid, accurate, and efficient systematic reviews of scientific evidence. Having automated tools for systematic review should enable more transparent and timely review, maximizing the potential for identifying and translating research findings to practical application.

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Objective: Factors influencing long-term viability of an intermediated regional food supply network (food hub) were modeled using agent-based modeling techniques informed by interview data gathered from food hub participants.

Background: Previous analyses of food hub dynamics focused primarily on financial drivers rather than social factors and have not used mathematical models.

Method: Based on qualitative and quantitative data gathered from 22 customers and 11 vendors at a midwestern food hub, an agent-based model (ABM) was created with distinct consumer personas characterizing the range of consumer priorities.

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In training assembly workers in a factory, there are often barriers such as cost and lost productivity due to shutdown. The use of virtual reality (VR) training has the potential to reduce these costs. This research compares virtual bimanual haptic training versus traditional physical training and the effectiveness for learning transfer.

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