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The perception of distance is a complex process that often involves sensory information beyond that of just vision. In this work, we investigated if depth perception based on auditory information can be calibrated, a process by which perceptual accuracy of depth judgments can be improved by providing feedback and then performing corrective actions. We further investigated if perceptual learning through carryover effects of calibration occurs in different levels of a virtual environment’s visibility based on different levels of virtual lighting. Users performed an auditory depth judgment task over several trials in which they walked where they perceived an aural sound to be, yielding absolute estimates of perceived distance. This task was performed in three sequential phases: pretest, calibration, posttest. Feedback on the perceptual accuracy of distance estimates was only provided in the calibration phase, allowing to study the calibration of auditory depth perception. We employed a 2 (Visibility of virtual environment) ×3 (Phase) ×5 (Target Distance) multi-factorial design, manipulating the phase and target distance as within-subjects factors, and the visibility of the virtual environment as a between-subjects factor. Our results revealed that users generally tend to underestimate aurally perceived distances in VR similar to the distance compression effects that commonly occur in visual distance perception in VR. We found that auditory depth estimates, obtained using an absolute measure, can be calibrated to become more accurate through feedback and corrective action. In terms of environment visibility, we find that environments visible enough to reveal their extent may contain visual information that users attune to in scaling aurally perceived depth. 

Distance compression, which refers to the underestimation of ego-centric distance to objects, is a common problem in immersive virtual environments. Besides visually compensating the compressed distance, several studies have shown that auditory information can be an alternative solution for this problem. In particular, reverberation time (RT) has been proven to be an effective method to compensate distance compression. To further explore the feasibility of applying audio information to improve distance perception, we investigate whether users’ egocentric distance perception can be calibrated, and whether the calibrated effect can be carried over and even sustain for a longer duration. We conducted a study to understand the perceptual learning and carryover effects by using RT as stimuli for users to perceive distance in IVEs. The results show that the carryover effect exists after calibration, which indicates people can learn to perceive distances by attuning reverberation time, and the accuracy even remains a constant level after 6 months. Our findings could potentially be utilized to improve the distance perception in VR systems as the calibration of auditory distance perception in VR could sustain for several months. This could eventually avoid the burden of frequent training regimens. 

Abstract We investigated human understanding of different network visualizations in a large-scale online experiment. Three types of network visualizations were examined: node-link and two different sorting variants of matrix representations on a representative social network of either 20 or 50 nodes. Understanding of the network was quantified using task time and accuracy metrics on questions that were derived from an established task taxonomy. The sample size in our experiment was more than an order of magnitude larger (N = 600) than in previous research, leading to high statistical power and thus more precise estimation of detailed effects. Specifically, high statistical power allowed us to consider modern interaction capabilities as part of the evaluated visualizations, and to evaluate overall learning rates as well as ambient (implicit) learning. Findings indicate that participant understanding was best for the node-link visualization, with higher accuracy and faster task times than the two matrix visualizations. Analysis of participant learning indicated a large initial difference in task time between the node-link and matrix visualizations, with matrix performance steadily approaching that of the node-link visualization over the course of the experiment. This research is reproducible as the web-based module and results have been made available at: https://osf.io/qct84/ .