Visual Task Characterization for Exploratory Search

Three experiments examined the attentional components of the popular match-3 casual video game, Bejeweled Blitz (BJB). Attentionally demanding, BJB is highly popular among adults, particularly those in middle and later adulthood. In experiment 1, 54 older adults (Mage = 70.57) and 33 younger adults (Mage = 19.82) played 20 rounds of BJB, and completed online tasks measuring reaction time, simple visual search, and conjunction visual search. Prior experience significantly predicted BJB scores for younger adults, but for older adults, both prior experience and simple visual search task scores predicted BJB performance. Experiment 2 tested whether BJB practice alone would result in a carryover benefit to a visual search task in a sample of 58 young adults (Mage = 19.57) who completed 0, 10, or 30 rounds of BJB followed by a BJB-like visual search task with targets present or absent. Reaction times were significantly faster for participants who completed 30 but not 10 rounds of BJB compared with the search task only. This benefit was evident when targets were both present and absent, suggesting that playing BJB improves not only target detection, but also the ability to quit search effectively. Experiment 3 tested whether the attentional benefit in experiment 2 would apply to non-BJB stimuli. The results revealed a similar numerical but not significant trend. Taken together, the findings suggest there are benefits of casual video game playing to attention and relevant everyday skills, and that these games may have potential value as training tools.

An enduring question in visual attention research is whether unattended objects are subject to perceptual processing. The traditional view suggests that, whereas focal attention is required for the processing of complex features or for individuating objects, it is not required for detecting basic features. However, other models suggest that detecting basic features may be no different from object identification and also require focal attention. In the present study, we approach this problem by measuring the effect of attentional capture in simple and compound visual search tasks. To make sure measurements did not reflect strategic components of the tasks, we measured accuracy with brief displays. Results show that attentional capture influenced only compound but not basic feature searches, suggestive of a distinction between attentional requirements of the 2 tasks. We discuss our findings, together with recent results of top-down word cue effects and dimension-specific intertrial effects, in terms of the dual-route account for visual search, which suggests that the task that is being completed determines whether search is based on attentive or preattentive mechanisms.

Brain polarization of parietal cortex augments training-induced improvement of visual exploratory and attentional skills

As more newsrooms practice data journalism in this age of big data through the use of analytical and visualization tools, much research on exemplary award-winning data stories continue to be Western-centric and associated with data journalism’s democratic role of scrutinizing government and corporations as watchdog. This study examines the news organization in the non-West that has scored the most wins in international data journalism awards, Hong Kong’s South China Morning Post, to discover characteristics of the data journalism it practices, as its media operates within an environment with increasing government monitoring of the press, similar to countries in the Asian region subjected to various forms of authoritarian politics. Through a content analysis of 130 data stories produced from 2016 to 2020, this study investigates the topics that SCMP’s data team chooses to cover, how they are covered and the extent to which data journalism is able to work in the public’s interest amid Hong Kong’s increasingly complex political and social context. Findings show that even when stories are data-driven and evidence-based, with the use of diverse data sources and visualizations, topics related to China or Chinese politics tend to be approached with caution and the nature of stories more explanatory than investigative and less interactive. The roles of watchdog and interventionist are felt weakly in SCMP’s data stories, and the role of loyal-facilitator felt more strongly for stories on China than those on Hong Kong and the world, suggesting that data journalism may face challenges performing its democratic functions.

We apply an approach from cognitive linguistics by mapping Conceptual Metaphor Theory (CMT) to the visualization domain to address patterns of visual conceptual metaphors that are often used in science infographics. Metaphors play an essential part in visual communication and are frequently employed to explain complex concepts. However, their use is often based on intuition, rather than following a formal process. At present, we lack tools and language for understanding and describing metaphor use in visualization to the extent where taxonomy and grammar could guide the creation of visual components, e.g., infographics. Our classification of the visual conceptual mappings within scientific representations is based on the breakdown of visual components in existing scientific infographics. We demonstrate the development of this mapping through a detailed analysis of data collected from four domains (biomedicine, climate, space, and anthropology) that represent a diverse range of visual conceptual metaphors used in the visual communication of science. This work allows us to identify patterns of visual conceptual metaphor use within the domains, resolve ambiguities about why specific conceptual metaphors are used, and develop a better overall understanding of visual metaphor use in scientific infographics. Our analysis shows that ontological and orientational conceptual metaphors are the most widely applied to translate complex scientific concepts. To support our findings we developed a visual exploratory tool based on the collected database that places the individual infographics on a spatio-temporal scale and illustrates the breakdown of visual conceptual metaphors.

The detection of known and unknown attacks usually requires the interpretation and presentation of very large amounts of time-oriented security data. Using regular means for displaying the data, such as text or tables, is often ineffective. Furthermore, displaying only raw data is not sufficient, because the security expert is still required to derive meaningful conclusions from large amounts of data. In addition, in many cases (e.g., for detecting a virus spreading in the network), an aggregated view of multiple network devices is more effective than a view of each individual device. In this paper we propose an intelligent interface used by a distributed architecture that was described in our previous work, specific to the tasks of knowledge-based interpretation, summarization, query, visualization and interactive exploration of large numbers of time-oriented data. In order to support the interpretation and computation process, we provide automated mechanisms that perform derivation of context-specific, interval-based abstract interpretations (also known as Temporal Abstractions) from raw time-stamped security data, by using a domain-specific knowledge-base (e.g., a period of 5 hours, during the night, of a high number of FTP connections within the context of No User Activity, which might indicate the existence of a Trojan in the computer). The proposed visualization tool includes several functionalities for querying, visualization and exploration of both raw and abstracted time-oriented security data regarding single and multiple network devices.

The development of custom interactive visualization tools for specific domains and applications has been made much simpler recently by a surge of visualization tools, libraries and frameworks. Most of these tools are developed for classical data science applications, where a user is supported in analyzing measured or simulated data. But recently, there has also been an increasing interest in visual support for understanding machine learning algorithms and frameworks, especially for deep learning. Many, if not most, of the visualization support for (deep) learning addresses the developer of the learning system and not the end user (data scientist). Here we show on a specific example, namely the development of a matrix calculus algorithm, that supporting visualizations can also greatly benefit the development of algorithms in classical domains like in our case computer algebra. The idea is similar to visually supporting the understanding of learning algorithms, namely provide the developer with an interactive, visual tool that provides insights into the workings and, importantly, also into the failures of the algorithm under development. Developing visualization support for matrix calculus development went similar as the development of more traditional visual support systems for data analysts. First, we had to acquaint ourselves with the problem, its language and challenges by talking to the core developer of the matrix calculus algorithm. Once we understood the challenge, it was fairly easy to develop visual support that streamlined the development of the matrix calculus algorithm significantly.

The subject of the study is the construction and application of visual models using the concept of visualization metaphors in the context of exploratory analysis of heterogeneous data. This study considers improved variants of the previously proposed visualization metaphors that can be used as a basis for building visual models. A technology for exploratory analysis of heterogeneous data based on the joint use of different visualization metaphors is proposed. The process of visual data exploration at the stage of exploratory analysis using the proposed technology is demonstrated to be iterative and multiscenary, contingent upon the analysis goals. The software tool developed to implement the proposed technology is described, along with its additional functionality to calculate and export quantitative characteristics of the visual model. The software tool is then considered in the context of exploratory analysis of a synthetic data set. The future direction of the proposed approach to the construction of visual models, the technology of exploratory data analysis and the software tool for its support are determined.

The growing emphasis on sustainable medical environments has intensified the need for intelligent systems that can monitor, interpret, and respond to environmental data in real-time. This chapter explores the transformative role of data analytics, visualization, and decision support systems (DSS) in building sustainable environment monitoring frameworks for medical care. In modern healthcare settings, vast volumes of environmental data ranging from air quality indices and humidity levels to noise pollution and biohazard alerts are generated continuously. Harnessing this data through advanced analytics and machine learning algorithms enables early detection of risks, efficient resource allocation, and predictive maintenance of medical facilities. This proactive approach not only enhances operational efficiency but also supports eco-conscious medical practices. Visualization tools such as interactive dashboards, heatmaps, and real-time graphs convert complex datasets into actionable insights, allowing healthcare administrators and clinical teams to monitor trends, spot anomalies, and make faster, evidence-based decisions. When integrated with DSS, these technologies provide intelligent recommendations that support infection control, energy management, and patient safety in a seamless, automated manner. This chapter offers an in-depth discussion on innovative models and real-world case studies where sustainable environment monitoring systems powered by data analytics and visualization have significantly improved clinical and environmental outcomes. It also addresses challenges related to data integration, scalability, and user training, offering strategic insights for future-ready smart hospitals. By linking sustainability with technology and healthcare, the chapter highlights how data-driven strategies can empower greener, smarter, and safer medical environments.

With the growing need for visualization to aid users in understanding large, complex datasets, the ability for users to interact and explore these datasets is critical. As visual analytic systems have advanced to leverage powerful computational models and data analytics capabilities, the modes by which users engage and interact with the information are limited. Often, users are taxed with directly manipulating parameters of these models through traditional GUIs (e.g., using sliders to directly manipulate the value of a parameter). However, the purpose of user interaction in visual analytic systems is to enable visual data exploration -- where users can focus on their task, as opposed to the tool or system. As a result, users can engage freely in data exploration and decision-making, for the purpose of gaining insight. In this position paper, we discuss how evaluating visual analytic systems can be approached through user interaction analysis, where the goal is to minimize the cognitive translation between the visual metaphor and the mode of interaction (i.e., reducing the "interaction junk"). We motivate this concept through a discussion of traditional GUIs used in visual analytics for direct manipulation of model parameters, and the importance of designing interactions the support visual data exploration.

The Python programming language provides a development environment suitable to both computational and visualization tasks. One of Python's key advantages is that it lets developers use packages that extend the language to provide advanced capabilities, such as array and matrix manipulation, image processing, digital signal processing, and visualization. Several popular data exploration and visualization tools have been built in Python, including Visit (www.llnl. gov/visit), Paraview (www.paraview. org), climate data analysis tools (CDAT; www2-pcmdi.llnl.gov/cdat), and VisTrails (www.vistrails .org). In our work, we use VisTrails; however, nearly any Python-enabled application can produce similar results. The neuroscience field often uses bothmultimodal data and computationally complex algorithms to analyze data collected from study participants. Here, we investigate a study in which magnetic resonance imaging (MRI) is combined with electroencephalography (EEG) data to examine working memory.

Public transportation schedules are designed by agencies to optimize service quality under multiple constraints. However, real service usually deviates from the plan. Therefore, transportation analysts need to identify, compare and explain both eventual and systemic performance issues that must be addressed so that better timetables can be created. The purely statistical tools commonly used by analysts pose many difficulties due to the large number of attributes at trip- and station-level for planned and real service. Also challenging is the need for models at multiple scales to search for patterns at different times and stations, since analysts do not know exactly where or when relevant patterns might emerge and need to compute statistical summaries for multiple attributes at different granularities. To aid in this analysis, we worked in close collaboration with a transportation expert to design TR-EX, a visual exploration tool developed to identify, inspect and compare spatio-temporal patterns for planned and real transportation service. TR-EX combines two new visual encodings inspired by Marey's Train Schedule: Trips Explorer for trip-level analysis of frequency, deviation and speed; and Stops Explorer for station-level study of delay, wait time, reliability and performance deficiencies such as bunching. To tackle overplotting and to provide a robust representation for a large numbers of trips and stops at multiple scales, the system supports variable kernel bandwidths to achieve the level of detail required by users for different tasks. We justify our design decisions based on specific analysis needs of transportation analysts. We provide anecdotal evidence of the efficacy of TR-EX through a series of case studies that explore NYC subway service, which illustrate how TR-EX can be used to confirm hypotheses and derive new insights through visual exploration.

Genome browsers facilitate integrated analysis of multiple genomics datasets yet visualize only a few regions at a time and lack statistical functions for extracting meaningful information. We present HiCognition, a visual exploration and machine-learning tool based on a new genomic region set concept, enabling detection of patterns and associations between 3D chromosome conformation and collections of 1D genomics profiles of any type. By revealing how transcription and cohesion subunit isoforms contribute to chromosome conformation, we showcase how the flexible user interface and machine learning tools of HiCognition help to understand the relationship between the structure and function of the genome.

Software visualization techniques and tools play an important role in system comprehension efforts of software developers in the era of increasing code size and complexity. They enable the developer to have a global perception on various software attributes with the aid of different visualization metaphors and tools. One such tool is CodeMetropolis which is built on top of the game engine Minecraft and which uses the city metaphor to show the structure of the source code as a virtual city. In it, different physical properties of the city and the buildings are related to various code metrics. Up to now, it was limited to represent only code related artifacts. In this work, we extend the metaphor to include properties of the tests related to the program code using a novel concept. The test suite and the test cases are also associated with a set of metrics that characterize their quality (such as coverage and specialization), but also reveal new properties of the system itself. In a new version of CodeMetropolis, gardens representing code elements will give rise to outposts that characterize properties of the tests and show how they contribute to the quality of the code.

We developed a visualization tool and a methodology to support strategic planning of hospital service portfolios. Hospitals in Switzerland are reimbursed with a fixed fee per case. The fixed-fee model makes medical services comparable from a financial point of view. In order to take advantage of this model, the data that characterizes the medical services must be operationalized. The method that we developed, centers around a visual metaphor that provides the basis for strategic thinking. It is complemented by a visualization tool that allows visualization, analysis, and modification of service portfolios. Special features enable the tool to be used during live planning sessions. We describe the method, the tool, and its application in strategy workshops for infrastructure planning, reorganization, and resource optimization decisions.