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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.
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Information Theoretic Model to Simulate Agent-Signage Interaction for Wayfinding
Abstract Signage systems are critical for communicating spatial information during wayfinding among a plethora of noise in the environment. A proper signage system can improve wayfinding performance and user experience by reducing the perceived complexity of the environment. However, previous models of sign-based wayfinding do not incorporate realistic noise or quantify the reduction in perceived complexity from the use of signage. Drawing upon concepts from information theory, we propose and validate a new agent-signage interaction model that quantifies available wayfinding information from signs for wayfinding. We conducted two online crowd-sourcing experiments to compute the distribution of a sign’s visibility and an agent’s decision-making confidence as a function of observation angle and viewing distance. We then validated this model using a virtual reality (VR) experiment with trajectories from human participants. The crowd-sourcing experiments provided a distribution of decision-making entropy (conditioned on visibility) that can be applied to any sign/environment. From the VR experiment, a training dataset of 30 trajectories was used to refine our model, and the remaining test dataset of 10 trajectories was compared with agent behavior using dynamic time warping (DTW) distance. The results revealed a reduction of 38.76% in DTW distance between the average trajectories before and after refinement. Our refined agent-signage interaction model provides realistic predictions of human wayfinding behavior using signs. These findings represent a first step towards modeling human wayfinding behavior in complex real environments in a manner that can incorporate several additional random variables (e.g., environment layout).
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- PAR ID:
- 10223000
- Date Published:
- Journal Name:
- Cognitive Computation
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 1866-9956
- Page Range / eLocation ID:
- 189 to 206
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s12559-019-09689-1
