Visual Analytics as a Translational Cognitive Science

Fisher, Brian; Green, Tera Marie; Arias-Hernandez, Richard

doi:10.1111/j.1756-8765.2011.01148.x

Cited by 30 publications

(12 citation statements)

References 40 publications

(40 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Visual analytics, the science of analytical reasoning via visual interactive interfaces, can be used to build knowledge from data, make sense of the data, and make decisions from data for future research. [17][18][19] In this study, we used visual analytics to focus the analysis of the datasets.…”

Section: Overviewmentioning

confidence: 99%

Secondary Data Analytics of Aquaporin Expression Levels in Glioblastoma Stem-Like Cells

et al. 2015

View full text Add to dashboard Cite

Glioblastoma is the most common brain tumor in adults in which recurrence has been attributed to the presence of cancer stem cells in a hypoxic microenvironment. On the basis of tumor formation in vivo and growth type in vitro, two published microarray gene expression profiling studies grouped nine glioblastoma stem-like (GS) cell lines into one of two groups: full (GSf) or restricted (GSr) stem-like phenotypes. Aquaporin-1 (AQP1) and aquaporin-4 (AQP4) are water transport proteins that are highly expressed in primary glial-derived tumors. However, the expression levels of AQP1 and AQP4 have not been previously described in a panel of 92 glioma samples. Therefore, we designed secondary data analytics methods to determine the expression levels of AQP1 and AQP4 in GS cell lines and glioblastoma neurospheres. Our investigation also included a total of 2,566 expression levels from 28 Affymetrix microarray probe sets encoding 13 human aquaporins (AQP0–AQP12); CXCR4 (the receptor for stromal cell derived factor-1 [SDF-1], a potential glioma stem cell therapeutic target]); and PROM1 (gene encoding CD133, the widely used glioma stem cell marker). Interactive visual representation designs for integrating phenotypic features and expression levels revealed that inverse expression levels of AQP1 and AQP4 correlate with distinct phenotypes in a set of cell lines grouped into full and restricted stem-like phenotypes. Discriminant function analysis further revealed that AQP1 and AQP4 expression are better predictors for tumor formation and growth types in glioblastoma stem-like cells than are CXCR4 and PROM1. Future investigations are needed to characterize the molecular mechanisms for inverse expression levels of AQP1 and AQP4 in the glioblastoma stem-like neurospheres.

show abstract

Section: Overviewmentioning

confidence: 99%

Secondary Data Analytics of Aquaporin Expression Levels in Glioblastoma Stem-Like Cells

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Thus, a codevelopment framework can help to achieve userfriendliness in the development of visual analytic tools. Fisher and Green [28] provided such a framework for the co-development of visual analytics in translational cognitive science and highlighted the applicability of the approach across projects and disciplines. There is some preliminary evidence of the benefits of this approach in practice, e.g., Freebairn and Atkinson [27] developed a dynamic simulation model for physical health planning and noted the co-development process led to greater expert uptake of the tool.…”

Section: Discussionmentioning

confidence: 99%

Experts’ perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study

Walsh

Chung

Salvador‐Carulla

2020

BMC Med Res Methodol

View full text Add to dashboard Cite

Background: Health experts including planners and policy-makers face complex decisions in diverse and constantly changing healthcare systems. Visual analytics may play a critical role in supporting analysis of complex healthcare data and decision-making. The purpose of this study was to examine the real-world experience that experts in mental healthcare planning have with visual analytics tools, investigate how well current visualisation techniques meet their needs, and suggest priorities for the future development of visual analytics tools of practical benefit to mental healthcare policy and decision-making. Methods: Health expert experience was assessed by an online exploratory survey consisting of a mix of multiple choice and open-ended questions. Health experts were sampled from an international pool of policy-makers, health agency directors, and researchers with extensive and direct experience of using visual analytics tools for complex mental healthcare systems planning. We invited them to the survey, and the experts' responses were analysed using statistical and text mining approaches. Results: The forty respondents who took part in the study recognised the complexity of healthcare systems data, but had most experience with and preference for relatively simple and familiar visualisations such as bar charts, scatter plots, and geographical maps. Sixty-five percent rated visual analytics as important to their field for evidence-informed decision-making processes. Fifty-five percent indicated that more advanced visual analytics tools were needed for their data analysis, and 67.5% stated their willingness to learn new tools. This was reflected in text mining and qualitative synthesis of open-ended responses. Conclusions: This exploratory research provides readers with the first self-report insight into expert experience with visual analytics in mental healthcare systems research and policy. In spite of the awareness of their importance for complex healthcare planning, the majority of experts use simple, readily available visualisation tools. We conclude that co-creation and co-development strategies will be required to support advanced visual analytics tools and skills, which will become essential in the future of healthcare.

show abstract

“…Methods such as eye movement monitoring can provide valuable insight into real-time cognitive processing in visual analysis, but can best be interpreted within the context of scientific theory of human cognition and performance. We have argued here and elsewhere 90 that a translational cognitive science of human interaction with visualization environments is the best, and perhaps the only, pathway by which we can achieve this objective.…”

Section: Future Directionsmentioning

confidence: 94%

Eye tracking evaluation of visual analytics

Kurzhals

Fisher

Burch

et al. 2016

Information Visualization

Self Cite

View full text Add to dashboard Cite

The application of eye tracking for the evaluation of humans’ viewing behavior is a common approach in psychological research. So far, the use of this technique for the evaluation of visual analytics and visualization is less prominent. We investigate recent scientific publications from the main visualization and visual analytics conferences and journals, as well as related research fields that include an evaluation by eye tracking. Furthermore, we provide an overview of evaluation goals that can be achieved by eye tracking and state-of-the-art analysis techniques for eye tracking data. Ideally, visual analytics leads to a mixed-initiative cognitive system where the mechanism of distribution is the interaction of the user with the visualization environment. Therefore, we also include a discussion of cognitive approaches and models to include the user in the evaluation process. Based on our review of the current use of eye tracking evaluation in our field and the cognitive theory, we propose directions for future research on evaluation methodology, leading to the grand challenge of developing an evaluation approach to the mixed-initiative cognitive system of visual analytics.

show abstract

Visual Analytics as a Translational Cognitive Science

Cited by 30 publications

References 40 publications

Secondary Data Analytics of Aquaporin Expression Levels in Glioblastoma Stem-Like Cells

Secondary Data Analytics of Aquaporin Expression Levels in Glioblastoma Stem-Like Cells

Experts’ perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study

Eye tracking evaluation of visual analytics

Contact Info

Product

Resources

About