Perceptual Guidelines Research Articles

Perceptual Localization Performance of the Whole Hand Vibrotactile Funneling Illusion.

Funneling illusion refers to a midway perceived illusory tactile sensation between multiple distant stimulations. Since haptic illusion provides guidelines to simplify tactile interfaces, funneling illusion has been explored on different parts of human skin. This study aimed to evaluate the perceptual localization performance of vibrotactile funneling illusion on palmar side of the hand. It is based on the idea of leveraging the vibrotactile funneling illusion to produce distributed vibrotactile stimuli across the entire hand using only a few vibration actuators. By designing a glove with actuators on five fingertips and the palm heel, we measured the localization density of funneling illusion on the whole hand in two experimental conditions [one-dimensional (1D) illusion along each finger and 2D illusion on palmar plane]. The results showed that the average correct rate of location discrimination was 97%, 82%, and 71% in 3-, 4-, and 5-location densities for 1D illusion and 85%, 70%, and 58% in 11-, 16-, and 21-location densities for 2D illusion, respectively. These findings confirmed the feasibility of simulating multi-location stimuli using only a few actuators. Also, perceptual guidelines were provided for the designing of vibrotactile gloves by leveraging the entire hand funneling illusion.

Perceptual Guidelines for Optimizing Field of View in Stereoscopic Augmented Reality Displays

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.

Perceptual Guidelines for Creating Rectangular Treemaps

Treemaps are space-filling visualizations that make efficient use of limited display space to depict large amounts of hierarchical data. Creating perceptually effective treemaps requires carefully managing a number of design parameters including the aspect ratio and luminance of rectangles. Moreover, treemaps encode values using area, which has been found to be less accurate than judgments of other visual encodings, such as length. We conduct a series of controlled experiments aimed at producing a set of design guidelines for creating effective rectangular treemaps. We find no evidence that luminance affects area judgments, but observe that aspect ratio does have an effect. Specifically, we find that the accuracy of area comparisons suffers when the compared rectangles have extreme aspect ratios or when both are squares. Contrary to common assumptions, the optimal distribution of rectangle aspect ratios within a treemap should include non-squares, but should avoid extremes. We then compare treemaps with hierarchical bar chart displays to identify the data densities at which length-encoded bar charts become less effective than area-encoded treemaps. We report the transition points at which treemaps exhibit judgment accuracy on par with bar charts for both leaf and non-leaf tree nodes. We also find that even at relatively low data densities treemaps result in faster comparisons than bar charts. Based on these results, we present a set of guidelines for the effective use of treemaps and suggest alternate approaches for treemap layout.

Visual Perception and Mixed-Initiative Interaction for Assisted Visualization Design

This paper describes the integration of perceptual guidelines from human vision with an AI-based mixed-initiative search strategy. The result is a visualization assistant called ViA, a system that collaborates with its users to identify perceptually salient visualizations for large, multidimensional datasets. ViA applies knowledge of low-level human vision to: (1) evaluate the effectiveness of a particular visualization for a given dataset and analysis tasks; and (2) rapidly direct its search towards new visualizations that are most likely to offer improvements over those seen to date. Context, domain expertise, and a high-level understanding of a dataset are critical to identifying effective visualizations. We apply a mixed-initiative strategy that allows ViA and its users to share their different strengths and continually improve ViA's understanding of a user's preferences. We visualize historical weather conditions to compare ViA's search strategy to exhaustive analysis, simulated annealing, and reactive tabu search, and to measure the improvement provided by mixed-initiative interaction. We also visualize intelligent agents competing in a simulated online auction to evaluate ViA's perceptual guidelines. Results from each study are positive, suggesting that ViA can construct high-quality visualizations for a range of real-world datasets.

Need for perceptual display hierarchies in visualization

The advent of computers with high processing power has led to the generation of large, multidimensional collections of data. Visualization lends itself well to the challenge of exploring and analyzing these information spaces by harnessing the strengths of the human visual system. Most visualization techniques are based on the assumption that the display device has sufficient resolution, and that our visual acuity is adequate for completing the analysis tasks. However, this may not be true, particularly for specialized display devices (e.g., PDAs or large-format projection walls). In this article, we propose to: (1) determine the amount of information a particular display environment can encode; (2) design visualizations that maximize the information they represent relative to this upper-limit; and (3) dynamically update a visualization when the display environment changes to continue to maintain high levels of information content. To our knowledge, there are no visualization systems that do this type of information addition/removal based on perceptual guidelines. However, there are systems that attempt to increase or decrease the amount of information based on some level-of-detail or zooming rules. For example, semantic zooming tags objects with "details" and adds or removes them as the user zooms in and out. Furnas's original fisheye lens system [9] used semantic details to determine how much zoom was necessary to include certain details. Thus, while zooming for detail, you see not only a more detailed graphic representation, but also more text details (e.g., more street names on the zoomed-in portion of a map). Level-of-detail hierarchies have also been used in computer graphics to reduce geometric complexity where full resolution models are unnecessary and can be replaced with low-detail models where the resulting error cannot be easily recognized. Our approach is motivated by all these ideas, but our key contribution is that we use human perception constraints to define when to add or remove information.

Perceptually based brush strokes for nonphotorealistic visualization

An important problem in the area of computer graphics is the visualization of large, complex information spaces. Datasets of this type have grown rapidly in recent years, both in number and in size. Images of the data stored in these collections must support rapid and accurate exploration and analysis. This article presents a method for constructing visualizations that are both effective and aesthetic. Our approach uses techniques from master paintings and human perception to visualize a multidimensional dataset. Individual data elements are drawn with one or more brush strokes that vary their appearance to represent the element's attribute values. The result is a nonphotorealistic visualization of information stored in the dataset. Our research extends existing glyph-based and nonphotorealistic techniques by applying perceptual guidelines to build an effective representation of the underlying data. The nonphotorealistic properties the strokes employ are selected from studies of the history and theory of Impressionist art. We show that these properties are similar to visual features that are detected by the low-level human visual system. This correspondence allows us to manage the strokes to produce perceptually salient visualizations. Psychophysical experiments confirm a strong relationship between the expressive power of our nonphotorealistic properties and previous findings on the use of perceptual color and texture patterns for data display. Results from these studies are used to produce effective nonphotorealistic visualizations. We conclude by applying our techniques to a large, multidimensional weather dataset to demonstrate their viability in a practical, real-world setting.