19 research outputs found
Turning the (virtual) world around: Patterns in saccade direction vary with picture orientation and shape in virtual reality
Research investigating gaze in natural scenes has identified a number of spatial biases in where people look, but it is unclear whether these are partly due to constrained testing environments (e.g., a participant with their head restrained and looking at a landscape image framed within a computer monitor). We examined the extent to which image shape (square vs. circle), image rotation, and image content (landscapes vs. fractal images) influence eye and head movements in virtual reality (VR). Both the eyes and head were tracked while observers looked at natural scenes in a virtual environment. In line with previous work, we found a bias for saccade directions parallel to the image horizon, regardless of image shape or content. We found that, when allowed to do so, observers move both their eyes and head to explore images. Head rotation, however, was idiosyncratic; some observers rotated a lot, whereas others did not. Interestingly, the head rotated in line with the rotation of landscape but not fractal images. That head rotation and gaze direction respond differently to image content suggests that they may be under different control systems. We discuss our findings in relation to current theories on head and eye movement control and how insights from VR might inform more traditional eye-tracking studies
Recognizing Affiliation: Using Behavioural Traces to Predict the Quality of Social Interactions in Online Games
Online social interactions in multiplayer games can be supportive and
positive or toxic and harmful; however, few methods can easily assess
interpersonal interaction quality in games. We use behavioural traces to
predict affiliation between dyadic strangers, facilitated through their social
interactions in an online gaming setting. We collected audio, video, in-game,
and self-report data from 23 dyads, extracted 75 features, trained Random
Forest and Support Vector Machine models, and evaluated their performance
predicting binary (high/low) as well as continuous affiliation toward a
partner. The models can predict both binary and continuous affiliation with up
to 79.1% accuracy (F1) and 20.1% explained variance (R2) on unseen data, with
features based on verbal communication demonstrating the highest potential. Our
findings can inform the design of multiplayer games and game communities, and
guide the development of systems for matchmaking and mitigating toxic behaviour
in online games.Comment: CHI '2
Clusters, Trends, and Outliers : How Immersive Technologies Can Facilitate the Collaborative Analysis of Multidimensional Data
Immersive technologies such as augmented reality devices are opening up a new design space for the visual analysis of data. This paper studies the potential of an augmented reality environment for the purpose of collaborative analysis of multidimensional, abstract data. We present ART, a collaborative analysis tool to visualize multidimensional data in augmented reality using an interactive, 3D parallel coordinates visualization. The visualization is anchored to a touch-sensitive tabletop, benefiting from well-established interaction techniques. The results of group-based, expert walkthroughs show that ART can facilitate immersion in the data, a fluid analysis process, and collaboration. Based on the results, we provide a set of guidelines and discuss future research areas to foster the development of immersive technologies as tools for the collaborative analysis of multidimensional data.publishe
USE: An integrative suite for temporally-precise psychophysical experiments in virtual environments for human, nonhuman, and artificially intelligent agents
Way-finding lighting systems for rail tunnel evacuation: A virtual reality experiment with Oculus Rift®
Game-Based Augmented Visual Feedback for Enlarging Speech Movements in Parkinson's Disease
User guided movement analysis in games using semantic trajectories
Understanding how players navigate through virtual worlds can offer useful guidance for map and level design of video games. One way to handle large-scale movement data obtained within games is by modelling movement as a sequence of visited locations instead of focusing on raw trajectory data. In this paper, we introduce a visualization approach for movement analysis based on semantic trajectories derived from a user-guided segmentation of the game environment. Based on this concept, the visualization offers an aggregated view of movement patterns together with the possibility to view individual paths for detailed inspection. We report on a user study with six experts from the game industry and compare the insights they have gleaned from the visualization with feedback from players. Our results indicate that the approach is successful in localizing problematic areas and that semantic trajectories can be a valuable addition to existing approaches for player movement analysis