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Near-eye display systems for augmented reality (AR) aim to seamlessly merge virtual content with the user’s view of the real-world. A substantial limitation of current systems is that they only present virtual content over a limited portion of the user’s natural field of view (FOV). This limitation reduces the immersion and utility of these systems. Thus, it is essential to quantify FOV coverage in AR systems and understand how to maximize it. It is straightforward to determine the FOV coverage for monocular AR systems based on the system architecture. However, stereoscopic AR systems that present 3D virtual content create a more complicated scenario because the two eyes’ views do not always completely overlap. The introduction of partial binocular overlap in stereoscopic systems can potentially expand the perceived horizontal FOV coverage, but it can also introduce perceptual nonuniformity artifacts. In this arrticle, we first review the principles of binocular FOV overlap for natural vision and for stereoscopic display systems. We report the results of a set of perceptual studies that examine how different amounts and types of horizontal binocular overlap in stereoscopic AR systems influence the perception of nonuniformity across the FOV. We then describe how to quantify the horizontal FOV in stereoscopic AR when taking 3D content into account. We show that all stereoscopic AR systems result in a variable horizontal FOV coverage and variable amounts of binocular overlap depending on fixation distance. Taken together, these results provide a framework for optimizing perceived FOV coverage and minimizing perceptual artifacts in stereoscopic AR systems for different use cases. 

Detecting and avoiding obstacles while navigating can pose a challenge for people with low vision, but augmented reality (AR) has the potential to assist by enhancing obstacle visibility. Perceptual and user experience research is needed to understand how to craft effective AR visuals for this purpose. We developed a prototype AR application capable of displaying multiple kinds of visual cues for obstacles on an optical see-through head-mounted display. We assessed the usability of these cues via a study in which participants with low vision navigated an obstacle course. The results suggest that 3D world-locked AR cues were superior to directional heads-up cues for most participants during this activity.

 

A central question in neuroscience is how sensory inputs are transformed into percepts. At this point, it is clear that this process is strongly influenced by prior knowledge of the sensory environment. Bayesian ideal observer models provide a useful link between data and theory that can help researchers evaluate how prior knowledge is represented and integrated with incoming sensory information. However, the statistical prior employed by a Bayesian observer cannot be measured directly, and must instead be inferred from behavioral measurements. Here, we review the general problem of inferring priors from psychophysical data, and the simple solution that follows from assuming a prior that is a Gaussian probability distribution. As our understanding of sensory processing advances, however, there is an increasing need for methods to flexibly recover the shape of Bayesian priors that are not well approximated by elementary functions. To address this issue, we describe a novel approach that applies to arbitrary prior shapes, which we parameterize using mixtures of Gaussian distributions. After incorporating a simple approximation, this method produces an analytical solution for psychophysical quantities that can be numerically optimized to recover the shapes of Bayesian priors. This approach offers advantages in flexibility, while still providing an analytical framework for many scenarios. We provide a MATLAB toolbox implementing key computations described herein.

 

Neuroplasticity, defined as the brain’spotentialto change in response to its environment, has been extensively studied at the cellular and molecular levels. Work in animal models suggests that stimulation to the ventral tegmental area (VTA) enhances plasticity, and that myelination constrains plasticity. Little is known, however, about whether proxy measures of these properties in the human brain are associated with learning. Here, we investigated the plasticity of the frontoparietal system by asking whether VTA resting-state functional connectivity and myelin map values (T1w/T2w ratios) predicted learning after short-term training on the adaptiven-back (n = 46, ages 18–25). We found that stronger baseline connectivity between VTA and lateral prefrontal cortex predicted greater improvements in accuracy. Lower myelin map values predicted improvements in response times, but not accuracy. Our findings suggest that proxy markers of neural plasticity can predict learning in humans.