Visual Cognition: Human Comprehension of Data Visualizations.
Abstract not provided.
Haass, Michael Joseph↗
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Evaluation of Visualization Heuristics
Heuristics have been used since the early 1990's in the Human Computer Interaction (HCI) domain, for evaluating user interfaces. Most notably, heuristics developed by Nielsen and Molich (1990) have been used by the HCI community. Forsell and Johansson (2010) used the same methodology as Nielsen and Molich and developed a set of 10 heuristics for information visualization. Currently, neither the information visualization or the visual analytics community has adopted these heuristics. However, several studies have been conducted using heuristics. Hearst et al. (2015) found that using the heuristics for visualizations along with asking questions about the data resulted in complementary results as the questions about the data could compensate for heuristics that were difficult to understand and apply. Heli Väätäjä et al. (2016) found that heuristics related to interaction, veracity, and aesthetics needed to be added to the Forsell and Johansson (2010) set. They also noted that a lack of domain knowledge caused the evaluators issues in conducting a heuristic evaluation. We conducted a controlled experiment to determine what factors might impact the expert evaluators' capability to conduct a heuristic evaluation of a static information visualization. In particular, we wanted to understand how their familiarity with the visualizations and their experience in conducting heuristic evaluations impacted their evaluations of visualizations. We recruited 10 participants who were domain experts in the visualization field each having experience with one of more of the following: user experience design or research, user interface development, and human computer interaction research. We used a heuristic set proposed by Forsell and Johansson. Participants were also asked to complete a demographic survey in which they rated their experience with each visualization type generally, their experience conducting heuristic evaluations, their experience using each visualization type to extract information, and their experience developing and creating each visualization type. The participants were shown a series of 5 common visualizations (Scatterplots, Sunbursts, Tree Maps, Parallel Sets, and Area Graphs) all displaying the VAST 2008 Mini Challenge Two- Migrant Boats data . Participants were given a suggested analytical question related to each visualization to keep in mind as they applied the 10 heuristics to each visualization. They were asked if they could find any usability problems in the visualizations and which heuristic(s) helped them to identify these problems. The participants were asked to rate how relevant each heuristic was for evaluating each visualization along with rating how confident they felt in their ability to apply the heuristic to evaluate each visualization. At the conclusion of the study, participants provided qualitative feedback on the usability issues of each visualization. Additionally, we collected eye tracking data on 6 of the 10 participants. We will be using this data to determine how long participants looked at the various visualizations and what parts of the visualizations they focused on. We had two hypotheses for this empirical evaluation: 1.The Forsell and Johansson heuristics of Orientation and help, Data set reduction, and Prompting are less useful for static visualizations because they require some interaction in order to test completely. 2.Participants that do not have experience with heuristic evaluation will provide lower evaluation scores for the heuristics compared to those that have experience. Related to hypothesis 1, as our study used only static visualizations, interaction-related usability challenges should be harder to identify. This should result in lower usefulness ratings for those heuristics. Preliminary results indicate that participants found it more difficult to apply heuristics to unfamiliar visualizations.
Williams, Ryan G.↗
User-Centered Evaluation of Visual Analytics
Visual analytics systems are becoming very popular. More domains now use interactive visualizations to analyze the ever-increasing amount and heterogeneity of data. More novel visualizations are being developed for more tasks and users. We need to ensure that these systems can be evaluated to determine that they are both useful and usable. A user-centered evaluation for visual analytics needs to be developed for these systems. While many of the typical human-computer interaction (HCI) evaluation methodologies can be applied as is, others will need modification. Additionally, new functionality in visual analytics systems needs new evaluation methodologies. There is a difference between usability evaluations and user-centered evaluations. Usability looks at the efficiency, effectiveness, and user satisfaction of users carrying out tasks with software applications. User-centered evaluation looks more specifically at the utility provided to the users by the software. This is reflected in the evaluations done and in the metrics used. In the visual analytics domain this is very challenging as users are most likely experts in a particular domain, the tasks they do are often not well defined, the software they use needs to support large amounts of different kinds of data, and often the tasks last for months. These difficulties are discussed more in the section on User-centered Evaluation. Our goal is to provide a discussion of user-centered evaluation practices for visual analytics, including existing practices that can be carried out and new methodologies and metrics that need to be developed and agreed upon by the visual analytics community. The material provided here should be of use for both researchers and practitioners in the field of visual analytics. Researchers and practitioners in HCI and interested in visual analytics will find this information useful as well as a discussion on changes that need to be made to current HCI practices to make them more suitable to visual analytics. A history of analysis and analysis techniques and problems is provided as well as an introduction to user-centered evaluation and various evaluation techniques for readers from different disciplines. The understanding of these techniques is imperative if we wish to support analysis in the visual analytics software we develop. Currently the evaluations that are conducted and published for visual analytics software are very informal and consist mainly of comments from users or potential users. Our goal is to help researchers in visual analytics to conduct more formal user-centered evaluations. While these are time-consuming and expensive to carryout, the outcomes of these studies will have a defining impact on the field of visual analytics and help point the direction for future features and visualizations to incorporate. While many researchers view work in user-centered evaluation as a less-than-exciting area to work, the opposite is true. First of all, the goal is user-centered evaluation is to help visual analytics software developers, researchers, and designers improve their solutions and discover creative ways to better accommodate their users. Working with the users is extremely rewarding as well. While we use the term “users” in almost all situations there are a wide variety of users that all need to be accommodated. Moreover, the domains that use visual analytics are varied and expanding. Just understanding the complexities of a number of these domains is exciting. Researchers are trying out different visualizations and interactions as well. And of course, the size and variety of data are expanding rapidly. User-centered evaluation in this context is rapidly changing. There are no standard processes and metrics and thus those of us working on user-centered evaluation must be creative in our work with both the users and with the researchers and developers.
intelligence analysis↗
Data Visualization Saliency v. 1.0
Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off human subject studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. The visual saliency model for data visualizations described here addresses this need for evaluation tools. Visual saliency models assess the visual features (e.g. color, contrast, edges) of an image to predict which areas of that image will draw a viewer’s attention. Providing predictions of where viewers will look in a visualization based on the saliency of the various regions and elements in that image could help designers to assess whether or not their design will draw the viewer’s attention as intended. The Data Visualization Saliency model, a saliency model for data visualizations, consists of two parts: a text saliency map and a modified version of the Itti, Koch and Niebur model [1]. The final saliency map is a linear combination of the two. The text saliency map is created from an algorithm that computes the likelihood of belonging to a text region for each pixel of an input visualization image. The modified Itti model transforms the original input image to a representation in CIE LAB color space, which provides an accurate representation of perceptual color opponency. For a given input data visualization image, the Data Visualization Saliency model computes and returns the visualization saliency map highlighting areas of the data visualization that will draw a viewer’s attention. It also returns the lower level saliency maps for text and the modified Itti model.
Haass, Michael↗
Visual Acuity Using Head-fixed Displays During Passive Self and Surround Motion
The ability to read head-fixed displays on various motion platforms requires the suppression of vestibulo-ocular reflexes. This study examined dynamic visual acuity while viewing a head-fixed display during different self and surround rotation conditions. Twelve healthy subjects were asked to report the orientation of Landolt C optotypes presented on a micro-display fixed to a rotating chair at 50 cm distance. Acuity thresholds were determined by the lowest size at which the subjects correctly identified 3 of 5 optotype orientations at peak velocity. Visual acuity was compared across four different conditions, each tested at 0.05 and 0.4 Hz (peak amplitude of 57 deg/s). The four conditions included: subject rotated in semi-darkness (i.e., limited to background illumination of the display), subject stationary while visual scene rotated, subject rotated around a stationary visual background, and both subject and visual scene rotated together. Visual acuity performance was greatest when the subject rotated around a stationary visual background; i.e., when both vestibular and visual inputs provided concordant information about the motion. Visual acuity performance was most reduced when the subject and visual scene rotated together; i.e., when the visual scene provided discordant information about the motion. Ranges of 4-5 logMAR step sizes across the conditions indicated the acuity task was sufficient to discriminate visual performance levels. The background visual scene can influence the ability to read head-fixed displays during passive motion disturbances. Dynamic visual acuity using head-fixed displays can provide an operationally relevant screening tool for visual performance during exposure to novel acceleration environments.
Wood, Scott J.↗
Modeling human comprehension of data visualizations
This project was inspired by two needs. The first is a need for tools to help scientists and engineers to design effective data visualizations for communicating information, whether to the user of a system, an analyst who must make decisions based on complex data, or in the context of a technical report or publication. Most scientists and engineers are not trained in visualization design, and they could benefit from simple metrics to assess how well their visualization's design conveys the intended message. In other words, will the most important information draw the viewer's attention? The second is the need for cognition-based metrics for evaluating new types of visualizations created by researchers in the information visualization and visual analytics communities. Evaluating visualizations is difficult even for experts. However, all visualization methods and techniques are intended to exploit the properties of the human visual system to convey information efficiently to a viewer. Thus, developing evaluation methods that are rooted in the scientific knowledge of the human visual system could be a useful approach. In this project, we conducted fundamental research on how humans make sense of abstract data visualizations, and how this process is influenced by their goals and prior experience. We then used that research to develop a new model, the Data Visualization Saliency Model, that can make accurate predictions about which features in an abstract visualization will draw a viewer's attention. The model is an evaluation tool that can address both of the needs described above, supporting both visualization research and Sandia mission needs.
97 MATHEMATICS AND COMPUTING↗
Visualization techniques to aid in the analysis of multispectral astrophysical data sets
The goal of this project was to support the scientific analysis of multi-spectral astrophysical data by means of scientific visualization. Scientific visualization offers its greatest value if it is not used as a method separate or alternative to other data analysis methods but rather in addition to these methods. Together with quantitative analysis of data, such as offered by statistical analysis, image or signal processing, visualization attempts to explore all information inherent in astrophysical data in the most effective way. Data visualization is one aspect of data analysis. Our taxonomy as developed in Section 2 includes identification and access to existing information, preprocessing and quantitative analysis of data, visual representation and the user interface as major components to the software environment of astrophysical data analysis. In pursuing our goal to provide methods and tools for scientific visualization of multi-spectral astrophysical data, we therefore looked at scientific data analysis as one whole process, adding visualization tools to an already existing environment and integrating the various components that define a scientific data analysis environment. As long as the software development process of each component is separate from all other components, users of data analysis software are constantly interrupted in their scientific work in order to convert from one data format to another, or to move from one storage medium to another, or to switch from one user interface to another. We also took an in-depth look at scientific visualization and its underlying concepts, current visualization systems, their contributions and their shortcomings. The role of data visualization is to stimulate mental processes different from quantitative data analysis, such as the perception of spatial relationships or the discovery of patterns or anomalies while browsing through large data sets. Visualization often leads to an intuitive understanding of the meaning of data values and their relationships by sacrificing accuracy in interpreting the data values. In order to be accurate in the interpretation, data values need to be measured, computed on, and compared to theoretical or empirical models (quantitative analysis). If visualization software hampers quantitative analysis (which happens with some commercial visualization products), its use is greatly diminished for astrophysical data analysis. The software system STAR (Scientific Toolkit for Astrophysical Research) was developed as a prototype during the course of the project to better understand the pragmatic concerns raised in the project. STAR led to a better understanding on the importance of collaboration between astrophysicists and computer scientists. Twenty-one examples of the use of visualization for astrophysical data are included with this report. Sixteen publications related to efforts performed during or initiated through work on this project are listed at the end of this report.
Brugel, E. W.↗
Scientific Visualization and Computational Science: Natural Partners
Scientific visualization is developing rapidly, stimulated by computational science, which is gaining acceptance as a third alternative to theory and experiment. Computational science is based on numerical simulations of mathematical models derived from theory. But each individual simulation is like a hypothetical experiment; initial conditions are specified, and the result is a record of the observed conditions. Experiments can be simulated for situations that can not really be created or controlled. Results impossible to measure can be computed.. Even for observable values, computed samples are typically much denser. Numerical simulations also extend scientific exploration where the mathematics is analytically intractable. Numerical simulations are used to study phenomena from subatomic to intergalactic scales and from abstract mathematical structures to pragmatic engineering of everyday objects. But computational science methods would be almost useless without visualization. The obvious reason is that the huge amounts of data produced require the high bandwidth of the human visual system, and interactivity adds to the power. Visualization systems also provide a single context for all the activities involved from debugging the simulations, to exploring the data, to communicating the results. Most of the presentations today have their roots in image processing, where the fundamental task is: Given an image, extract information about the scene. Visualization has developed from computer graphics, and the inverse task: Given a scene description, make an image. Visualization extends the graphics paradigm by expanding the possible input. The goal is still to produce images; the difficulty is that the input is not a scene description displayable by standard graphics methods. Visualization techniques must either transform the data into a scene description or extend graphics techniques to display this odd input. Computational science is a fertile field for visualization research because the results vary so widely and include things that have no known appearance. The amount of data creates additional challenges for both hardware and software systems. Evaluations of visualization should ultimately reflect the insight gained into the scientific phenomena. So making good visualizations requires consideration of characteristics of the user and the purpose of the visualization. Knowledge about human perception and graphic design is also relevant. It is this breadth of knowledge that stimulates proposals for multidisciplinary visualization teams and intelligent visualization assistant software. Visualization is an immature field, but computational science is stimulating research on a broad front.
Uselton, Samuel P.↗
Comparative measurement of visual stability in Earth and cosmic space (L-4)
The frequency and the intensity of space motion sickness was reviewed and investigated. Unusual induced-gravity situations, such as rotation, linear acceleration, parallel swinging etc. were investigated as well. Visually-induced motion sensation or distorted perception were evaluated with respect to visual stability by many investigators. These studies are all concerned with the effect of gravity and the induction of motion sickness through human visual perception. Direct investigation under the microgravity situation, such as parabolic flight, Skylab, and Spacelab, was carried out. These studies focused on how human visual stability is established through various sensory afferents in specific gravity conditions. Results from these investigations indicate that sensory mismatch probably plays an important role in space motion sickness. The interaction of visual, vestibular, and somatosensory perception is smoothly coordinated under normal gravitational conditions on the Earth in our daily life. When the cooperation is destroyed or a mismatch occurs among them, motion sickness may develop not only on the Earth but also in microgravity. The latter case may be the cause of space motion sickness or space adaption syndrome. How human beings obtain visual stability even with posture changes on the ground was investigated. Visual stability can be categorized as static or dynamic. Static visual stability is concerned with orientation and dynamic stability is concerned with object motion perception. The perception of visual stability is modified by many other sensations, such as somatosensory, vestibular, and muscle tension. We will mainly focus on modifications by vestibular inputs to visual perception produced by eye movements in microgravity. The Vestibular-Oscular Reflex (VOR) is a well-known characteristic which results from the relationship between eye mobility and vestibular afferent inputs. Eye movements also modify dynamic visual perception, such as perceived object motion velocity. The VOR is constantly simulated under 1-g conditions on Earth. In fact, human beings have been habituated and 'programmed' for orientation (visual stability) in their everyday, 1-g environment. When humans are exposed to a different gravity situation, this programmed behavior must change; that is, it is reprogrammed. This is called habituation or familiarization. We hope to examine how object motion perception is perturbed and subsequently adapted in the microgravity environment. This experiment is focused on the cooperation of visual, vestibular, and somatosensory perception coordination and how it is changed or reduced in space compared to 1-g environment. We will obtain information on the coordination between eye movement and neck muscle activity by using EOG and EMG. We will also collect data from Payload Specialists using a self-diagnostic questionnaire concerned with perceptual abnormality. When each sensory input function and its integration in the higher nervous system are well-characterized, then more effective techniques to control SAS may be developed.
Koga, Kazugo↗
Visualization techniques to aid in the analysis of multi-spectral astrophysical data sets
The goal of this project was to support the scientific analysis of multi-spectral astrophysical data by means of scientific visualization. Scientific visualization offers its greatest value if it is not used as a method separate or alternative to other data analysis methods but rather in addition to these methods. Together with quantitative analysis of data, such as offered by statistical analysis, image or signal processing, visualization attempts to explore all information inherent in astrophysical data in the most effective way. Data visualization is one aspect of data analysis. Our taxonomy as developed in Section 2 includes identification and access to existing information, preprocessing and quantitative analysis of data, visual representation and the user interface as major components to the software environment of astrophysical data analysis. In pursuing our goal to provide methods and tools for scientific visualization of multi-spectral astrophysical data, we therefore looked at scientific data analysis as one whole process, adding visualization tools to an already existing environment and integrating the various components that define a scientific data analysis environment. As long as the software development process of each component is separate from all other components, users of data analysis software are constantly interrupted in their scientific work in order to convert from one data format to another, or to move from one storage medium to another, or to switch from one user interface to another. We also took an in-depth look at scientific visualization and its underlying concepts, current visualization systems, their contributions, and their shortcomings. The role of data visualization is to stimulate mental processes different from quantitative data analysis, such as the perception of spatial relationships or the discovery of patterns or anomalies while browsing through large data sets. Visualization often leads to an intuitive understanding of the meaning of data values and their relationships by sacrificing accuracy in interpreting the data values. In order to be accurate in the interpretation, data values need to be measured, computed on, and compared to theoretical or empirical models (quantitative analysis). If visualization software hampers quantitative analysis (which happens with some commercial visualization products), its use is greatly diminished for astrophysical data analysis. The software system STAR (Scientific Toolkit for Astrophysical Research) was developed as a prototype during the course of the project to better understand the pragmatic concerns raised in the project. STAR led to a better understanding on the importance of collaboration between astrophysicists and computer scientists.
Brugel, Edward W.↗
Conflict Alerts for Aircraft Conducting Visual Approaches
It is common for aircraft to conduct visual and instrument final approaches to a single runway or multiple parallel runways. Useful, nonexcessive safety alerts on aircraft conducting visual approaches are helpful to air traffic controllers, though pilots are responsible for separation with the preceding aircraft. A variety of visual approaches to various runway configurations are studied, and a set of safety alert thresholds is proposed. Fast-time simulations with recorded real-world air traffic data of mostly visual approach flights are performed on a prototype tactical separation assurance system for terminal airspace. Alerts are generated -- with both the standard separation thresholds and the proposed safety alert thresholds -- and compared with those from the Conflict Alert (CA) functionality in the Standard Terminal Automation Replacement System (STARS). The results show that the number of Mode-C Intruder alerts generated was reduced 76% as compared to STARS CA. The nuisance alerts generated by assuming visual to be instrument approaches was reduced by 92% when the proposed safety alert thresholds were used and visual approaches were assumed.A set of safety alert thresholds, which allow safety alerts to be provided to the controllers for aircraft conductingvisual approaches to a single runway or multiple parallel runways, has been proposed based on input from SubjectMatter Experts as well as visual approach procedures and common practices. The goal is to maximize the thresholdswithin the guidance of the procedures and common practices and to minimize the number of nuisance alerts and totalnumber of alerts with the support of flight intent information. Tests have been performed using a recently developedprototype tactical separation assurance system for terminal airspace, called Terminal Tactical Separation-AssuredFlight Environment (T-TSAFE). The input was a full day of air traffic data from Dallas/Fort Worth (DFW) TRACONwith most arriving flights conducting visual approaches. The results compare favorably with those of the ConflictAlert (CA) functionality of the Standard Terminal Automation Replacement System (STARS).When fast-time simulation experiment was performed using T-TSAFE with all arriving flights assumed to conductinstrument approaches, the expected large number of separation alerts were observed. When compared with STARSCA, the conflict pairs common to both T-TSAFE and STARS CA was only 21% of the total STARS CA alerts. As aresult, the nuisance-alert rate for STARS CA was estimated to be about 70%, which is comparable to a similar previously estimation of 80% nuisance-alert rate for CARTS (Common Automated Radar Terminal System) CA. The Mode-CIntruder (MCI) alerts were also reduced by 76% as compared to STARS CA.Examination of the common conflict pairs between T-TSAFE and STARS CA shows that they are valid separationconflicts with good alert lead times for T-TSAFE. However, many of them would still be considered nuisance alertsif the aircraft were conducting visual approaches. This was confirmed by another visual approach fast-time simulationT-TSAFE experiment, in which all DFW arriving flights were assumed to be on visual approaches and our proposedsafety alert thresholds were used and tested. The result of the experiment showed that the number of non-MCI alertswas less by 92% as compared to the number when all DFW arrivals were assumed to be conducting instrumentapproaches. The common conflict pairs involving aircraft conducting visual approaches to parallel runways werereduced by 93% as well. Thus, the flight intent information and the safety alert thresholds are effective in reducingnuisance alerts.T-TSAFE can thus provide separation and safety conflict alerts seamlessly in the real-world environment of mixedterminal operations with arriving flights of both visual and instrument approaches. Compared with STARS CA, TTSAFEhas fewer false alerts, larger alert lead time, and larger alert thresholds. While the nuisance alerts are reducedsignificantly with the safety alert thresholds, further work is needed to study if the alert lead time for safety alerts issufficient.
Tang, Huabin↗
Prenatal and postnatal polybrominated diphenyl ether exposure and visual spatial abilities in children
Polybrominated diphenyl ethers (PBDEs) are associated with impaired visual spatial abilities in toxicological studies, but no epidemiologic study has investigated PBDEs and visual spatial abilities in children. The Health Outcomes and Measures of the Environment Study, a prospective birth cohort (2003–2006, Cincinnati, OH), was used to examine prenatal and childhood PBDEs and visual spatial abilities in 199 children. PBDEs were measured at 16±3 weeks gestation and at 1, 2, 3, 5, and 8 years using gas chromatography/isotope dilution high-resolution mass spectrometry. We used the Virtual Morris Water Maze to measure visual spatial abilities at 8 years. In covariate-adjusted models, 10-fold increases in BDE-47, −99, and −100 at 5 years were associated with shorter completion times by 5.2 s (95% Confidence Interval [CI] −9.3, −1.1), 4.5 s (95% CI −8.1, −0.9), and 4.7 s (95% CI −9.0, −0.3), respectively. However, children with higher BDE-153 at 3 years had longer completion times (β=5.4 s, 95% CI −0.3, 11.1). Prenatal PBDEs were associated with improved visual spatial memory retention, with children spending a higher percentage of their search path in the correct quadrant. Child sex modified some associations between PBDEs and visual spatial learning. Longer path lengths were observed among males with increased BDE-47 at 2 and 3 years, while females had shorter paths. In conclusion, prenatal and postnatal BDE-28, −47, −99, and −100 at 5 and 8 years were associated with improved visual spatial abilities, whereas a pattern of impairments in visual spatial learning was noted with early childhood BDE-153 concentrations. - Highlights: • The VMWM test was used to assess visual spatial abilities in children at 8 years. • BDE-153 at 3 years was adversely associated with visual spatial learning. • BDE-47, −99, and −100 at 5 years was associated with better visual spatial learning. • Prenatal PBDEs were associated with improved visual spatial memory retention. • Male children were observed to perform more poorly on the VMWM than females.
54 ENVIRONMENTAL SCIENCES↗
Metrics and Benchmarks for Visualization
What is a "good" visualization? How can the quality of a visualization be measured? How can one tell whether one visualization is "better" than another? I claim that the true quality of a visualization can only be measured in the context of a particular purpose. The same image generated from the same data may be excellent for one purpose and abysmal for another. A good measure of visualization quality will correspond to the performance of users in accomplishing the intended purpose, so the "gold standard" is user testing. As a user of visualization software (or at least a consultant to such users) I don't expect visualization software to have been tested in this way for every possible use. In fact, scientific visualization (as distinct from more "production oriented" uses of visualization) will continually encounter new data, new questions and new purposes; user testing can never keep up. User need software they can trust, and advice on appropriate visualizations of particular purposes. Considering the following four processes, and their impact on visualization trustworthiness, reveals important work needed to create worthwhile metrics and benchmarks for visualization. These four processes are (1) complete system testing (user-in-loop), (2) software testing, (3) software design and (4) information dissemination. Additional information is contained in the original extended abstract.
Uselton, Samuel P.↗
Automated objective characterization of visual field defects in 3D
A method and apparatus for electronically performing a visual field test for a patient. A visual field test pattern is displayed to the patient on an electronic display device and the patient's responses to the visual field test pattern are recorded. A visual field representation is generated from the patient's responses. The visual field representation is then used as an input into a variety of automated diagnostic processes. In one process, the visual field representation is used to generate a statistical description of the rapidity of change of a patient's visual field at the boundary of a visual field defect. In another process, the area of a visual field defect is calculated using the visual field representation. In another process, the visual field representation is used to generate a statistical description of the volume of a patient's visual field defect.
Fink, Wolfgang↗
Portable interactive visualization of large-scale simulations in geotechnical engineering using Unity3D
Development in large-scale geotechnical engineering simulation places tremendous demand for efficient visualization of such simulation data. This study presents a lightweight software tool, i.e. Geotechnical Interactive Visualization (GIV), as a solution to this challenge, which achieves efficient interactive visualization of large-scale simulations data in geotechnical engineering. Visualization data flow and algorithms specifically optimized for common geotechnical engineering applications are implemented in GIV. GIV can visualize geotechnical structure models with time-varying attributes attached to mesh with fixed topology, and also models with time-varying mesh topologies but no attributes attached, the two most common visualization tasks in geotechnical engineering. Furthermore, challenges for large-scale simulation data visualization, including parallel simulation data redundancy, massive data size, dynamic user interaction, and portability are overcome via specifically designed algorithms for simulation data preprocessing and optimized visualization modules using the powerful 3D rendering and interactive game engine Unity3D. Comparison of GIV with several widely used visualization tools for the visualization of large-scale idealized datasets and realistic geotechnical simulations highlights the visualization efficiency, smooth interactivity, and lightweight features of GIV.
42 ENGINEERING↗
Role of somatosensory and vestibular cues in attenuating visually induced human postural sway
The purpose of this study was to determine the contribution of visual, vestibular, and somatosensory cues to the maintenance of stance in humans. Postural sway was induced by full-field, sinusoidal visual surround rotations about an axis at the level of the ankle joints. The influences of vestibular and somatosensory cues were characterized by comparing postural sway in normal and bilateral vestibular absent subjects in conditions that provided either accurate or inaccurate somatosensory orientation information. In normal subjects, the amplitude of visually induced sway reached a saturation level as stimulus amplitude increased. The saturation amplitude decreased with increasing stimulus frequency. No saturation phenomena were observed in subjects with vestibular loss, implying that vestibular cues were responsible for the saturation phenomenon. For visually induced sways below the saturation level, the stimulus-response curves for both normal subjects and subjects experiencing vestibular loss were nearly identical, implying (1) that normal subjects were not using vestibular information to attenuate their visually induced sway, possibly because sway was below a vestibular-related threshold level, and (2) that subjects with vestibular loss did not utilize visual cues to a greater extent than normal subjects; that is, a fundamental change in visual system "gain" was not used to compensate for a vestibular deficit. An unexpected finding was that the amplitude of body sway induced by visual surround motion could be almost 3 times greater than the amplitude of the visual stimulus in normal subjects and subjects with vestibular loss. This occurred in conditions where somatosensory cues were inaccurate and at low stimulus amplitudes. A control system model of visually induced postural sway was developed to explain this finding. For both subject groups, the amplitude of visually induced sway was smaller by a factor of about 4 in tests where somatosensory cues provided accurate versus inaccurate orientation information. This implied (1) that the subjects experiencing vestibular loss did not utilize somatosensory cues to a greater extent than normal subjects; that is, changes in somatosensory system "gain" were not used to compensate for a vestibular deficit, and (2) that the threshold for the use of vestibular cues in normal subjects was apparently lower in test conditions where somatosensory cues were providing accurate orientation information.
NASA Discipline Neuroscience↗
SNL ATDM Visualization (ECP ST Capability Assessment Report)
The SNL ATDM Data and Visualization work consolidates existing ATDM activities in scalable data management and visualization. Part of the responsibilities of the SNL ATDM Data and Visualization Project is the maintenance and development of visualization resources for ATDM applications on Exascale platforms. The ATDM Scalable Visualization project provides visualization and analysis required to satisfy the needs of the ASC/ATDM applications on next-generation, many-core platforms. This involves many activities including the re-engineering of visualization algorithms, services, and tools that enable ASC customers to carry out data analysis on ASC systems and ACES platforms. Current tools include scalable data analysis software released open source through ParaView, VTK, and Catalyst. We are also both leveraging and contributing to VTK-m, a many-core visualization library, to satisfy our visualization needs on advanced architectures. The scope of the Scalable Visualization under ATDM at SNL is R&D for the programming model and implementation of visualization code for ASC/ATDM projects and ASC/ATDM application support.
97 MATHEMATICS AND COMPUTING↗
Role of somatosensory and vestibular cues in attenuating visually induced human postural sway
The purpose was to determine the contribution of visual, vestibular, and somatosensory cues to the maintenance of stance in humans. Postural sway was induced by full field, sinusoidal visual surround rotations about an axis at the level of the ankle joints. The influences of vestibular and somatosensory cues were characterized by comparing postural sway in normal and bilateral vestibular absent subjects in conditions that provided either accurate or inaccurate somatosensory orientation information. In normal subjects, the amplitude of visually induced sway reached a saturation level as stimulus amplitude increased. The saturation amplitude decreased with increasing stimulus frequency. No saturation phenomena was observed in subjects with vestibular loss, implying that vestibular cues were responsible for the saturation phenomenon. For visually induced sways below the saturation level, the stimulus-response curves for both normal and vestibular loss subjects were nearly identical implying that (1) normal subjects were not using vestibular information to attenuate their visually induced sway, possibly because sway was below a vestibular-related threshold level, and (2) vestibular loss subjects did not utilize visual cues to a greater extent than normal subjects; that is, a fundamental change in visual system 'gain' was not used to compensate for a vestibular deficit. An unexpected finding was that the amplitude of body sway induced by visual surround motion could be almost three times greater than the amplitude of the visual stimulus in normals and vestibular loss subjects. This occurred in conditions where somatosensory cues were inaccurate and at low stimulus amplitudes. A control system model of visually induced postural sway was developed to explain this finding. For both subject groups, the amplitude of visually induced sway was smaller by a factor of about four in tests where somatosensory cues provided accurate versus inaccurate orientation information. This implied that (1) the vestibular loss subjects did not utilize somatosensory cues to a greater extent than normal subjects; that is, changes in somatosensory system 'gain' were not used to compensate for a vestibular deficit, and (2) the threshold for the use of vestibular cues in normals was apparently lower in test conditions where somatosensory cues were providing accurate orientation information.
Peterka, Robert J.↗