By blurring the boundaries between digital and physical realities, Augmented Reality (AR) is transforming consumers\' perceptions of themselves and their environments. This review demonstrates AR\'s capacity to influence psychology and behavior in profound ways. We begin by providing a concise introduction to AR, considering its technical, practical, and theoretical properties. Next, we showcase a multi-disciplinary set of recent studies that explore AR\'s impact on psychological processes and behavioral outcomes. We conclude by offering a selection of potential future research directions designed to deepen our understanding of the psychological and behavioral implications of AR experiences.

暂无摘要。

Spatial computing (SC) in a surgical context offers reconstructed interactive four-dimensional models of radiological imaging. Preoperative and postoperative assessment with SC can offer more insight into personalized surgical approaches. Spine surgery has benefitted from the use of perioperative SC assessment. Herein, we describe the use of SC to perform a perioperative assessment of a revision spinal deformity surgery. A 79-year-old wheelchair-bound male presented to the neurosurgery clinic with a history of chronic lumbar pain associated with bilateral lower extremity weakness. His surgical history is significant for an L2-L5 lumbar decompression with posterior fixation 1 year prior. On examination, there were signs of thoracic myelopathy. Imaging revealed his previous instrumentation, pseudoarthrosis, and cord compression. We perform a two-staged operation to address the thoracic spinal cord compression and myelopathy, pseudoarthrosis, and malalignment with a lack of global spinal harmony. His imaging is driven by a spatial computing and SC environment and offers support for the diagnosis of his L2-3 and L4-5 pseudoarthrosis on the reconstructed SC-based computed tomography scan. SC enabled the assessment of the configuration of the psoas muscle and course of critical neurovascular structures in addition to graft sizing, trajectory and approach, evaluation of the configuration and durability of the anterior longitudinal ligament, and the overlying abdominal viscera. SC increases the familiarity of the patient\'s specific anatomy and enhances perioperative assessment. As such, SC can be used to preoperatively plan for spinal revision surgery.

Multimodal user interfaces promise natural and intuitive human-machine interactions. However, is the extra effort for the development of a complex multisensor system justified, or can users also be satisfied with only one input modality? This study investigates interactions in an industrial weld inspection workstation. Three unimodal interfaces, including spatial interaction with buttons augmented on a workpiece or a worktable, and speech commands, were tested individually and in a multimodal combination. Within the unimodal conditions, users preferred the augmented worktable, but overall, the interindividual usage of all input technologies in the multimodal condition was ranked best. Our findings indicate that the implementation and the use of multiple input modalities is valuable and that it is difficult to predict the usability of individual input modalities for complex systems.

Virtual humans (VHs)-automated, three-dimensional agents-can serve as realistic embodiments for social interactions with human users. Extant literature suggests that a user\'s cognitive and affective responses toward a VH depend on the extent to which the interaction elicits a sense of copresence, or the subjective \"sense of being together.\" Furthermore, prior research has linked copresence to important social outcomes (e.g., likeability and trust), emphasizing the need to understand which factors contribute to this psychological state. Although there is some understanding of the determinants of copresence in virtual reality (VR) (cf. Oh et al., 2018), it is less known what determines copresence in mixed reality (MR), a modality wherein VHs have unique access to social cues in a \"real-world\" setting. In the current study, we examined the extent to which a VH\'s responsiveness to events occurring in the user\'s physical environment increased a sense of copresence and heightened affective connections to the VH. Participants (N = 65) engaged in two collaborative tasks with a (nonspeaking) VH using an MR headset. In the first task, no event in the participant\'s physical environment would occur, which served as the control condition. In the second task, an event in the participants\' physical environment occurred, to which the VH either responded or ignored depending on the experimental condition. Copresence and interpersonal evaluations of the VHs were measured after each collaborative task via self-reported measures. Results show that when the VH responded to the physical event, participants experienced a significant stronger sense of copresence than when the VH did not respond. However, responsiveness did not elicit more positive evaluations toward the VH (likeability and emotional connectedness). This study is an integral first step in establishing how and when affective and cognitive components of evaluations during social interactions diverge. Importantly, the findings suggest that feeling copresence with VH in MR is partially determined by the VHs\' response to events in the actual physical environment shared by both interactants.

BACKGROUND: Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global).
OBJECTIVE: Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local).
METHODS: We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems.
RESULTS: This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project.
CONCLUSIONS: Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus.
UNASSIGNED: DERR1-10.2196/24432.

Object-oriented combinator chemistry (OOCC) is an artificial chemistry with composition devices borrowed from object-oriented and functional programming languages. Actors in OOCC are embedded in space and subject to diffusion; since they are neither created nor destroyed, their mass is conserved. Actors use programs constructed from combinators to asynchronously update their own states and the states of other actors in their neighborhoods. The fact that programs and combinators are themselves reified as actors makes it possible to build programs that build programs from combinators of a few primitive types using asynchronous spatial processes that resemble chemistry as much as computation. To demonstrate this, OOCC is used to define a parallel, asynchronous, spatially distributed self-replicating system modeled in part on the living cell. Since interactions among its parts result in the construction of more of these same parts, the system is strongly constructive. The system\'s high normalized complexity is contrasted with that of a simple composome.

Computation increasingly takes place not on an individual device, but distributed throughout a material or environment, whether it be a silicon surface, a network of wireless devices, a collection of biological cells or a programmable material. Emerging programming models embrace this reality and provide abstractions inspired by physics, such as computational fields, that allow such systems to be programmed holistically, rather than in terms of individual devices. This paper aims to provide a unified approach for the investigation and engineering of computations programmed with the aid of space-time abstractions, by bringing together a number of recent results, as well as to identify critical open problems.