Standalone augmented reality (AR) systems have great potential for interactive three-dimensional (3D) geo-visualization. Emerging head-worn AR technologies can display rich graphical imagery of large-scale environments and permit intuitive interaction through gestural and voice inputs. Yet, how users engage in geo-visualization through these interfaces and what interaction strategies yield the best spatial memory and navigation performance remain open questions. In the present empirical investigation, we related user interactions in a 3D geo-visualization application implemented on the Microsoft HoloLens AR system to virtual navigation outcomes. Informed by spatial cognitive theory, we uncovered interaction strategies during goal-oriented study of a 3D urban environment that predicted different aspects of navigation performance. Users who positioned the 3D city model to gain an overhead (i.e. survey perspective) view early on performed best when later following the route from memory. In contrast, consistent interaction switching (i.e. perspective transformation) during study predicted navigational efficiency when participants were unexpectedly tasked to return to the origin of the route. Individual differences also related to aspects of user interaction. Implications for 3D geo-visualization and navigation-assistive AR application design and suggestions for future directions are discussed.

Augmented Reality (AR) applications can enable geographically distant users to collaborate using shared video feeds or interactive 3D holograms, and may be particularly useful in the socially distant context of the Covid-19 pandemic. However, a good user experience is key for their success and could be negatively impacted by network impairments, which are an inevitable occurrence in today's best-effort Internet. In this paper, we present the findings of an empirical user study, aimed at understanding the effects of network outages, on user experience and behavior, in a collaborative AR task. We highlight how network outages affected users in different ways depending on their role in the collaborative task, and how giving users explicit information about poor network conditions helped them deal with some of these negative effects. Furthermore, we report the strategies that users themselves adopted, to deal with outages, such as batching instructions, or shifting to a different spatial referencing style when communicating with their partners. Lastly, based on our findings, we present some design implications for future remote-collaborative AR applications.

Cross-modality face recognition is an emerging topic due to the wide-spread usage of different sensors in day-to-day life applications. The development of face recognition systems relies greatly on existing databases for evaluation and obtaining training examples for data-hungry machine learning algorithms. However, currently, there is no publicly available face database that includes more than two modalities for the same subject. In this work, we introduce the Tufts Face Database that includes images acquired in various modalities: photograph images, thermal images, near infrared images, a recorded video, a computerized facial sketch, and 3D images of each volunteer's face. An Institutional Research Board protocol was obtained and images were collected from students, staff, faculty, and their family members at Tufts University. The database includes over 10,000 images from 113 individuals from more than 15 different countries, various gender identities, ages, and ethnic backgrounds. The contributions of this work are: 1) Detailed description of the content and acquisition procedure for images in the Tufts Face Database; 2) The Tufts Face Database is publicly available to researchers worldwide, which will allow assessment and creation of more robust, consistent, and adaptable recognition algorithms; 3) A comprehensive, up-to-date review on face recognition systems and face datasets.

The advancement of wearable eye-tracking technology enables cognitive researchers to capture vast amounts of eye gaze information while participants are completing specific tasks without restrictions on their movement. However, while eye trackers can overlay a gaze indicator on the scene video, identifying the specific objects being looked at and analyzing the resulting dataset are accomplished mostly by manual annotation. This method is a cost-prohibitive and time-consuming approach that is prone to human error. Such analytic difficulty limits researchers' ability to data mine the information efficiently, ultimately restricting the number of scenarios that can feasibly be conducted within budget. Here, the first fully automated solution for eye-tracking data analysis is presented, which eliminates the need for manual annotation. The proposed software architecture, gaze to object classification (GoC), processes the gaze-overlaid video from commercially available wearable eye trackers, recognizes and classifies the specific object a user is focusing on and calculates the gaze duration time. GoC utilizes an image cross-correlation method to locate the gaze indicator and an image similarity measurement to support faster processing. The presented system has been successfully adopted by cognitive psychologists. GoC's exceptional performance in analyzing a case study spanning over 50 h of mobile eye-tracking is presented. The accuracy and a cost-analysis comparison between GoC and state-of-the-art manual annotation software are provided. GoC has game-changing potential for increasing the ecological validity of using eye-tracking technology in cognitive research.

Decades of research have repeatedly shown that people perform poorly at estimating and understanding conditional probabilities that are inherent in Bayesian reasoning problems. Yet in the medical domain, both physicians and patients make daily, life-critical judgments based on conditional probability. Although there have been a number of attempts to develop more effective ways to facilitate Bayesian reasoning, reports of these findings tend to be inconsistent and sometimes even contradictory. For instance, the reported accuracies for individuals being able to correctly estimate conditional probability range from 6% to 62%. In this work, we show that problem representation can significantly affect accuracies. By controlling the amount of information presented to the user, we demonstrate how text and visualization designs can increase overall accuracies to as high as 77%. Additionally, we found that for users with high spatial ability, our designs can further improve their accuracies to as high as 100%. By and large, our findings provide explanations for the inconsistent reports on accuracy in Bayesian reasoning tasks and show a significant improvement over existing methods. We believe that these findings can have immediate impact on risk communication in health-related fields.

Spatial language is widely used, both literally to describe space and figuratively to express a broad range of ideas. This article reviews two projects studying the nature of mental representations of space induced entirely by language. The first project investigates perspective in descriptions of large-scale (e.g., convention center, town) space. People typically describe environments using a route or survey perspective, or a mixture of both. Route perspectives take a view from within the environment and describe the locations of landmarks with respect to a moving observer in terms of the observer's left, right, front, and back. Survey perspectives take a view from above the environment and describe locations of landmarks with respect to each other in terms of north, south, east, and west. Features of the environment, such as having a single or multiple path, affect choice of perspective. In comprehension, readers seem to form the same perspective-free mental representation irrespective of description perspective. They respond with equal speed and accuracy to inference questions from either perspective regardless of read perspective. The second project investigates mental representations of the objects located immediately around the body. Readers seem to form mental spatial frameworks, extensions of the three body axes, associating objects to the frameworks. The accessibility of the three axes depends on characteristics of the body, characteristics of the perceptual world, and posture of the observer. For example, for an upright observer, times to access objects along the head/feet axis are fastest because it is an asymmetric axis of the body and is correlated with the only asymmetric axis of the world, that created by gravity. Times to access objects along the front/back axis are next fastest as it is an asymmetric axis of the body, and times to the left/right axis are slowest as it is an axis with few asymmetries. Evidence for the spatial framework hypothesis was obtained in a variety of situations, varying posture, perspective, number of observers, and cause of reorientations. © 1994 John Wiley & Sons, Inc.