Human factors in visualization research

By Melanie Tory, Torsten Möller. IEEE Transactions on Visualization and Computer Graphics, 2004

475 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/df2bbe74a7d66e4ac488c444a42428dc288ac569

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In Civil and Structural Engineering

Citations in the corpus

A Visual Analytics Approach Using the Exploration of Multidimensional Feature Spaces for Content-Based Medical Image Retrieval (2015, background): “Guidelines for visualization [30] and search interfaces [31] suggest that effective human interpretation of visualized information can be enabled through multiple different views of complex data, abstractions to summarize complex information, the presentation of supplementary information, and the ab”
A Systematic Review on the Practice of Evaluating Visualization (2013, methodology): “Tory and Möller [65] specifically discuss the role of human factors in visualization and advocate various methods applied in user-centered design processes.”
The Role of Uncertainty, Awareness, and Trust in Visual Analytics (2016, background): “Knowledge Generation: Tory and M¨oller [76] give an introduction to human factors and highlight that visualisations serve as cognitive support and address human computer cooperation.”
Design Study Methodology: Reflections from the Trenches and the Stacks (2012, methodology): “The phenomenon that what a target user says they do in retrospect is only an incomplete match with their actual activities is well-known in psychology [23] and HCI [60]; most users do not accurately introspect about their own data analysis and visualization needs, making these needs invisible to the”
Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey (2013, unknown): “A continuous data model is very often assumed with scientific data, which means that the data can be interpolated between discretely sampled values [39].”
Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data (2013, background): “In addition, Tory and Moller [99] presented a number of human factors that also need to be considered to enable the interpretation of visualized data by users.”
Analysis of biomedical data with multilevel glyphs (2014, methodology): “In the evaluation phase expert interviews at the work environment level based on methods described by Tory & Möller (2004) [32] and Plaisant (2004) [33] were done.”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “ issues. Experts can tell us how well the current design can help them in understanding complex machine learning models, and these reviews are also valuable parts of the visual analytics design cycle [87,121]. T3: Machine Learning Experts Machine learning experts are scientists and engineers who des”
The value of visualization (2005, background): “Tory and Möller [20] give a good overview of the current status of the use of human factors research in visualization, and identify areas for future research.”
Visualizing Big Data with augmented and virtual reality: challenges and research agenda (2015, methodology): “After observation and discussion about existing visualization methods and tools for Big Data, we can clarify and outline its important disadvantages that are sufficiently discussed by specialists from different fields [147–149].”
A Nested Model for Visualization Design and Validation (2009, methodology): “As others have pointed out [42], asking users to simply introspect about their actions and needs is notoriously insufficient.”