Memory Recall for Data Visualizations in Mixed Reality, Virtual Reality, 3D and 2D.

This article explores how the ability to recall information in data visualizations depends on the presentation technology. Participants viewed 10 Isotype visualizations on a 2D screen, in 3D, in Virtual Reality (VR) and in Mixed Reality (MR). To provide a fair comparison between the three 3D conditions, we used LIDAR to capture the details of the physical rooms, and used this information to create our textured 3D models. For all environments, we measured the number of visualizations recalled and their order (2D) or spatial location (3D, VR, MR). We also measured the number of syntactic and semantic features recalled. Results of our study show increased recall and greater richness of data understanding in the MR condition. Not only did participants recall more visualizations and ordinal/spatial positions in MR, but they also remembered more details about graph axes and data mappings, and more information about the shape of the data. We discuss how differences in the spatial and kinesthetic cues provided in these different environments could contribute to these results, and reasons why we did not observe comparable performance in the 3D and VR conditions.

Why and when do you look away when trying to remember? Gaze aversion as a marker of the attentional switch to the internal world during memory retrieval.

It is common to look away while trying to remember specific information, for example during autobiographical memory retrieval, a behavior referred to as gaze aversion. Given the competition between internal and external attention, gaze aversion is assumed to play a role in visual decoupling, i.e., suppressing environmental distractors during internal tasks. This suggests a link between gaze aversion and the attentional switch from the outside world to a temporary internal mental space that takes place during the initial stage of memory retrieval, but this assumption has never been verified so far. We designed a protocol where 33 participants answered 48 autobiographical questions while their eye movements were recorded with an eye-tracker and a camcorder. Results indicated that gaze aversion occurred early (median 1.09 s) and predominantly during the access phase of memory retrieval-i.e., the moment when the attentional switch is assumed to take place. In addition, gaze aversion lasted a relatively long time (on average 6 s), and was notably decoupled from concurrent head movements. These results support a role of gaze aversion in perceptual decoupling. Gaze aversion was also related to higher retrieval effort and was rare during memories which came spontaneously to mind. This suggests that gaze aversion might be required only when cognitive effort is required to switch the attention toward the internal world to help retrieving hard-to-access memories. Compared to eye vergence, another visual decoupling strategy, the association with the attentional switch seemed specific to gaze aversion. Our results provide for the first time several arguments supporting the hypothesis that gaze aversion is related to the attentional switch from the outside world to memory.

Attentional switch to memory: An early and critical phase of the cognitive cascade allowing autobiographical memory retrieval.

Remembering and mentally reliving yesterday's lunch is a typical example of episodic autobiographical memory retrieval. In the present review, we reappraised the complex cascade of cognitive processes involved in memory retrieval, by highlighting one particular phase that has received little interest so far: attentional switch to memory (ASM). As attention cannot be simultaneously directed toward external stimuli and internal memories, there has to be an attentional switch from the external to the internal world in order to initiate memory retrieval. We formulated hypotheses and developed hypothetical models of both the cognitive and brain processes that accompany ASM. We suggest that gaze aversion could serve as an objective temporal marker of the point at which people switch their attention to memory, and highlight several fields (neuropsychology, neuroscience, social cognition, comparative psychology) in which ASM markers could be essential. Our review thus provides a new framework for understanding the early stages of autobiographical memory retrieval.

Target Netgrams: An Annulus-Constrained Stress Model for Radial Graph Visualization.

We present Target Netgrams as a visualization technique for radial layouts of graphs. Inspired by manually created target sociograms, we propose an annulus-constrained stress model that aims to position nodes onto the annuli between adjacent circles for indicating their radial hierarchy, while maintaining the network structure (clusters and neighborhoods) and improving readability as much as possible. This is achieved by having more space on the annuli than traditional layout techniques. By adapting stress majorization to this model, the layout is computed as a constrained least square optimization problem. Additional constraints (e.g., parent-child preservation, attribute-based clusters and structure-aware radii) are provided for exploring nodes, edges, and levels of interest. We demonstrate the effectiveness of our method through a comprehensive evaluation, a user study, and a case study.

EOG-Based Human-Computer Interface: 2000-2020 Review.

Electro-oculography (EOG)-based brain-computer interface (BCI) is a relevant technology influencing physical medicine, daily life, gaming and even the aeronautics field. EOG-based BCI systems record activity related to users' intention, perception and motor decisions. It converts the bio-physiological signals into commands for external hardware, and it executes the operation expected by the user through the output device. EOG signal is used for identifying and classifying eye movements through active or passive interaction. Both types of interaction have the potential for controlling the output device by performing the user's communication with the environment. In the aeronautical field, investigations of EOG-BCI systems are being explored as a relevant tool to replace the manual command and as a communicative tool dedicated to accelerating the user's intention. This paper reviews the last two decades of EOG-based BCI studies and provides a structured design space with a large set of representative papers. Our purpose is to introduce the existing BCI systems based on EOG signals and to inspire the design of new ones. First, we highlight the basic components of EOG-based BCI studies, including EOG signal acquisition, EOG device particularity, extracted features, translation algorithms, and interaction commands. Second, we provide an overview of EOG-based BCI applications in the real and virtual environment along with the aeronautical application. We conclude with a discussion of the actual limits of EOG devices regarding existing systems. Finally, we provide suggestions to gain insight for future design inquiries.

Attentional Orienting in Front and Rear Spaces in a Virtual Reality Discrimination Task.

Recent studies on covert attention suggested that the visual processing of information in front of us is different, depending on whether the information is present in front of us or if it is a reflection of information behind us (mirror information). This difference in processing suggests that we have different processes for directing our attention to objects in front of us (front space) or behind us (rear space). In this study, we investigated the effects of attentional orienting in front and rear space consecutive of visual or auditory endogenous cues. Twenty-one participants performed a modified version of the Posner paradigm in virtual reality during a spaceship discrimination task. An eye tracker integrated into the virtual reality headset was used to make sure that the participants did not move their eyes and used their covert attention. The results show that informative cues produced faster response times than non-informative cues but no impact on target identification was observed. In addition, we observed faster response times when the target occurred in front space rather than in rear space. These results are consistent with an orienting cognitive process differentiation in the front and rear spaces. Several explanations are discussed. No effect was found on subjects' eye movements, suggesting that participants did not use their overt attention to improve task performance.

Gaze direction as a facial cue of memory retrieval state.

Gaze direction is a powerful social cue that indicates the direction of attention and can be used to decode others' mental states. When an individual looks at an external object, inferring where their attention is focused from their gaze direction is easy. But when people are immersed in memories, their attention is oriented towards their inner world. Is there any specific gaze direction in this situation, and if so, which one? While trying to remember, a common behavior is gaze aversion, which has mostly been reported as an upward-directed gaze. Our primary aim was to evaluate whether gaze direction plays a role in the inference of the orientation of attention-i.e., external vs. internal-in particular, whether an upward direction is considered as an indicator of attention towards the internal world. Our secondary objective was to explore whether different gaze directions are consistently attributed to different types of internal mental states and, more specifically, memory states (autobiographical or semantic memory retrieval, or working memory). Gaze aversion is assumed to play a role in perceptual decoupling, which is supposed to support internal attention. We therefore also tested whether internal attention was associated with high gaze eccentricity because the mismatch between head and eye direction alters visual acuity. We conducted two large-sample (160-163 participants) online experiments. Participants were asked to choose which mental state-among different internal and external attentional states-they would attribute to faces with gazes oriented in different directions. Participants significantly associated internal attention with an upward-averted gaze across experiments, while external attention was mostly associated with a gaze remaining on the horizontal axis. This shows that gaze direction is robustly used by observers to infer others' mental states. Unexpectedly, internal attentional states were not more associated with gaze eccentricity at high (30°) than low (10°) eccentricity and we found that autobiographical memory retrieval, but not the other memory states, was highly associated with 10° downward gaze. This reveals the possible existence of different types of gaze aversion for different types of memories and opens new perspectives.

Data Visceralization: Enabling Deeper Understanding of Data Using Virtual Reality.

A fundamental part of data visualization is transforming data to map abstract information onto visual attributes. While this abstraction is a powerful basis for data visualization, the connection between the representation and the original underlying data (i.e., what the quantities and measurements actually correspond with in reality) can be lost. On the other hand, virtual reality (VR) is being increasingly used to represent real and abstract models as natural experiences to users. In this work, we explore the potential of using VR to help restore the basic understanding of units and measures that are often abstracted away in data visualization in an approach we call data visceralization. By building VR prototypes as design probes, we identify key themes and factors for data visceralization. We do this first through a critical reflection by the authors, then by involving external participants. We find that data visceralization is an engaging way of understanding the qualitative aspects of physical measures and their real-life form, which complements analytical and quantitative understanding commonly gained from data visualization. However, data visceralization is most effective when there is a one-to-one mapping between data and representation, with transformations such as scaling affecting this understanding. We conclude with a discussion of future directions for data visceralization.

ShapeWordle: Tailoring Wordles using Shape-aware Archimedean Spirals.

We present a new technique to enable the creation of shape-bounded Wordles, we call ShapeWordle, in which we fit words to form a given shape. To guide word placement within a shape, we extend the traditional Archimedean spirals to be shape-aware by formulating the spirals in a differential form using the distance field of the shape. To handle non-convex shapes, we introduce a multi-centric Wordle layout method that segments the shape into parts for our shape-aware spirals to adaptively fill the space and generate word placements. In addition, we offer a set of editing interactions to facilitate the creation of semantically-meaningful Wordles. Lastly, we present three evaluations: a comprehensive comparison of our results against the state-of-the-art technique (WordArt), case studies with 14 users, and a gallery to showcase the coverage of our technique.

Interactive Structure-aware Blending of Diverse Edge Bundling Visualizations.

Many edge bundling techniques (i.e., data simplification as a support for data visualization and decision making) exist but they are not directly applicable to any kind of dataset and their parameters are often too abstract and difficult to set up. As a result, this hinders the user ability to create efficient aggregated visualizations. To address these issues, we investigated a novel way of handling visual aggregation with a task-driven and user-centered approach. Given a graph, our approach produces a decluttered view as follows: first, the user investigates different edge bundling results and specifies areas, where certain edge bundling techniques would provide user-desired results. Second, our system then computes a smooth and structural preserving transition between these specified areas. Lastly, the user can further fine-tune the global visualization with a direct manipulation technique to remove the local ambiguity and to apply different visual deformations. In this paper, we provide details for our design rationale and implementation. Also, we show how our algorithm gives more suitable results compared to current edge bundling techniques, and in the end, we provide concrete instances of usages, where the algorithm combines various edge bundling results to support diverse data exploration and visualizations.

How Neurophysiological Measures Can be Used to Enhance the Evaluation of Remote Tower Solutions.

New solutions in operational environments are often, among objective measurements, evaluated by using subjective assessment and judgment from experts. Anyhow, it has been demonstrated that subjective measures suffer from poor resolution due to a high intra and inter-operator variability. Also, performance measures, if available, could provide just partial information, since an operator could achieve the same performance but experiencing a different workload. In this study, we aimed to demonstrate: (i) the higher resolution of neurophysiological measures in comparison to subjective ones; and (ii) how the simultaneous employment of neurophysiological measures and behavioral ones could allow a holistic assessment of operational tools. In this regard, we tested the effectiveness of an electroencephalography (EEG)-based neurophysiological index (WEEG index) in comparing two different solutions (i.e., Normal and Augmented) in terms of experienced workload. In this regard, 16 professional air traffic controllers (ATCOs) have been asked to perform two operational scenarios. Galvanic Skin Response (GSR) has also been recorded to evaluate the level of arousal (i.e., operator involvement) during the two scenarios execution. NASA-TLX questionnaire has been used to evaluate the perceived workload, and an expert was asked to assess performance achieved by the ATCOs. Finally, reaction times on specific operational events relevant for the assessment of the two solutions, have also been collected. Results highlighted that the Augmented solution induced a local increase in subjects performance (Reaction times). At the same time, this solution induced an increase in the workload experienced by the participants (WEEG). Anyhow, this increase is still acceptable, since it did not negatively impact the performance and has to be intended only as a consequence of the higher engagement of the ATCOs. This behavioral effect is totally in line with physiological results obtained in terms of arousal (GSR), that increased during the scenario with augmentation. Subjective measures (NASA-TLX) did not highlight any significant variation in perceived workload. These results suggest that neurophysiological measure provide additional information than behavioral and subjective ones, even at a level of few seconds, and its employment during the pre-operational activities (e.g., design process) could allow a more holistic and accurate evaluation of new solutions.

Improving eye-tracking calibration accuracy using symbolic regression.

Eye tracking systems have recently experienced a diversity of novel calibration procedures, including smooth pursuit and vestibulo-ocular reflex based calibrations. These approaches allowed collecting more data compared to the standard 9-point calibration. However, the computation of the mapping function which provides planar gaze positions from pupil features given as input is mostly based on polynomial regressions, and little work has investigated alternative approaches. This paper fills this gap by providing a new calibration computation method based on symbolic regression. Instead of making prior assumptions on the polynomial transfer function between input and output records, symbolic regression seeks an optimal model among different types of functions and their combinations. This approach offers an interesting perspective in terms of flexibility and accuracy. Therefore, we designed two experiments in which we collected ground truth data to compare vestibulo-ocular and smooth pursuit calibrations based on symbolic regression, both using a marker or a finger as a target, resulting in four different calibrations. As a result, we improved calibration accuracy by more than 30%, with reasonable extra computation time.

Persuasive Data Videos: Investigating Persuasive Self-Tracking Feedback with Augmented Data Videos.

Self-tracking feedback with engaging and persuasive visualizations not only helps convey data but can also affect people's attitudes and behaviors. We investigate persuasive self-tracking feedback by augmenting data videos (DVs)-novel, engaging storytelling media. We introduce a new class of DVs, called Persuasive Data Videos (PDVs), by incorporating four persuasive elements-primary task, dialogue, system credibility, and social supports-drawn from the Persuasive System Design Model. We describe the iterative design of PDVs and a within-subjects preliminary validation to check their persuasive potential. We then assess PDVs' feasibility using the Persuasive Potential Questionnaire in a between-subjects study comparing a PDV against a conventional DV on Amazon Mechanical Turk (N = 252). Our results indicate the feasibility of using PDVs in providing individuals' self-tracking feedback to convey persuasive health messages, based on which we discuss opportunities for designing persuasive behavioral feedback in an engaging way.

Human-Machine Interaction Assessment by Neurophysiological Measures: A Study on Professional Air Traffic Controllers.

This study aims at investigating the possibility to employ neurophysiological measures to assess the humanmachine interaction effectiveness. Such a measure can be used to compare new technologies or solutions, with the final purpose to enhance operator's experience and increase safety. In the present work, two different interaction modalities (Normal and Augmented) related to Air Traffic Management field have been compared, by involving 10 professional air traffic controllers in a control tower simulated environment. Experimental task consisted in locating aircrafts in different airspace positions by using the sense of hearing. In one modality (i.e. "Normal"), all the sound sources (aircrafts) had the same amplification factor. In the "Augmented" modality, the amplification factor of the sound sources located along the participant head sagittal axis was increased, while the intensity of sound sources located outside this axis decreased. In other words, when the user oriented his head toward the aircraft position, the related sound was amplified. Performance data, subjective questionnaires (i.e. NASA-TLX) and neurophysiological measures (i.e. EEG-based) related to the experienced workload have been collected. Results showed higher significant performance achieved by the users during the "Augmented" modality with respect to the "Normal" one, supported by a significant decreasing in experienced workload, evaluated by using EEG-based index. In addition, Performance and EEG-based workload index showed a significant negative correlation. On the contrary, subjective workload analysis did not show any significant trend. This result is a demonstration of the higher effectiveness of neurophysiological measures with respect to subjective ones for Human-Computer Interaction assessment.

InPhysible: Camouflage Against Video-Based Physiological Measurement.

Imaging photoplethysmography (iPPG) is a powerful set of methods for measuring physiological signals from video. Recent advances have shown that a low-cost webcam can be used to measure heart rate, blood flow, respiration, blood oxygen levels and stress. While these methods have many beneficial applications, the unobtrusive and ubiquitous nature of the sensors risk exposing people to unwanted measurement. We present InPhysible the first camouflage system against video- based physiological measurement. The infra-red system can be embedded into any pair of glasses, or other headwear, and disrupts the measurement of the iPPG signal while being imperceptible by the human eye. Our system is flexible and can simulate realistic pulse signals to hinder heart rate measurement. In this paper we present the design of our prototype and a user study validating its efficacy. Finally, we discuss the limitations and implications for data privacy and security.

Interactive obstruction-free lensing for volumetric data visualization.

Occlusion is an issue in volumetric visualization as it prevents direct visualization of the region of interest. While many techniques such as transfer functions, volume segmentation or view distortion have been developed to address this, there is still room for improvement to better support the understanding of objects' vicinity. However, most existing Focus+Context fail to solve partial occlusion in datasets where the target and the occluder are very similar density-wise. For these reasons, we investigate a new technique which maintains the general structure of the investigated volumetric dataset while addressing occlusion issues. With our technique, the user interactively defines an area of interest where an occluded region or object is partially visible. Then our lens starts pushing at its border occluding objects, thus revealing hidden volumetric data. Next, the lens is modified with an extended field of view (fish-eye deformation) to better see the vicinity of the selected region. Finally, the user can freely explore the surroundings of the area under investigation within the lens. To provide real-time exploration, we implemented our lens using a GPU accelerated ray-casting framework to handle ray deformations, local lighting, and local viewpoint manipulation. We illustrate our technique with five application scenarios in baggage inspection, 3D fluid flow visualization, chest radiology, air traffic planning, and DTI fiber exploration.

FiberClay: Sculpting Three Dimensional Trajectories to Reveal Structural Insights.

Visualizing 3D trajectories to extract insights about their similarities and spatial configuration is a critical task in several domains. Air traffic controllers for example deal with large quantities of aircrafts routes to optimize safety in airspace and neuroscientists attempt to understand neuronal pathways in the human brain by visualizing bundles of fibers from DTI images. Extracting insights from masses of 3D trajectories is challenging as the multiple three dimensional lines have complex geometries, may overlap, cross or even merge with each other, making it impossible to follow individual ones in dense areas. As trajectories are inherently spatial and three dimensional, we propose FiberClay: a system to display and interact with 3D trajectories in immersive environments. FiberClay renders a large quantity of trajectories in real time using GP-GPU techniques. FiberClay also introduces a new set of interactive techniques for composing complex queries in 3D space leveraging immersive environment controllers and user position. These techniques enable an analyst to select and compare sets of trajectories with specific geometries and data properties. We conclude by discussing insights found using FiberClay with domain experts in air traffic control and neurology.

Scanpath visualization and comparison using visual aggregation techniques.

We demonstrate the use of different visual aggregation techniques to obtain non-cluttered visual representations of scanpaths. First, fixation points are clustered using the mean-shift algorithm. Second, saccades are aggregated using the Attribute-Driven Edge Bundling (ADEB) algorithm that handles a saccades direction, onset timestamp, magnitude or their combination for the edge compatibility criterion. Flow direction maps, computed during bundling, can be visualized separately (vertical or horizontal components) or as a single image using the Oriented Line Integral Convolution (OLIC) algorithm. Furthermore, cosine similarity between two flow direction maps provides a similarity map to compare two scanpaths. Last, we provide examples of basic patterns, visual search task, and art perception. Used together, these techniques provide valuable insights about scanpath exploration and informative illustrations of the eye movement data.

Uncertainty visualization of gaze estimation to support operator-controlled calibration.

In this paper, we investigate how visualization assets can support the qualitative evaluation of gaze estimation uncertainty. Although eye tracking data are commonly available, little has been done to visually investigate the uncertainty of recorded gaze information. This paper tries to fill this gap by using innovative uncertainty computation and visualization. Given a gaze processing pipeline, we estimate the location of this gaze position in the world camera. To do so we developed our own gaze data processing which give us access to every stage of the data transformation and thus the uncertainty computation. To validate our gaze estimation pipeline, we designed an experiment with 12 participants and showed that the correction methods we proposed reduced the Mean Angular Error by about 1.32 cm, aggregating all 12 participants' results. The Mean Angular Error is 0.25° (SD=0.15°) after correction of the estimated gaze. Next, to support the qualitative assessment of this data, we provide a map which codes the actual uncertainty in the user point of view.

Functional Decomposition for Bundled Simplification of Trail Sets.

Bundling visually aggregates curves to reduce clutter and help finding important patterns in trail-sets or graph drawings. We propose a new approach to bundling based on functional decomposition of the underling dataset. We recover the functional nature of the curves by representing them as linear combinations of piecewise-polynomial basis functions with associated expansion coefficients. Next, we express all curves in a given cluster in terms of a centroid curve and a complementary term, via a set of so-called principal component functions. Based on the above, we propose a two-fold contribution: First, we use cluster centroids to design a new bundling method for 2D and 3D curve-sets. Secondly, we deform the cluster centroids and generate new curves along them, which enables us to modify the underlying data in a statistically-controlled way via its simplified (bundled) view. We demonstrate our method by applications on real-world 2D and 3D datasets for graph bundling, trajectory analysis, and vector field and tensor field visualization.