Special Issue "Coupling Computation and Human Cognition through Interaction Design"

A special issue of Multimodal Technologies and Interaction (ISSN 2414-4088).

Deadline for manuscript submissions: closed (20 December 2017)

Special Issue Editors

Guest Editor
Dr. Kamran Sedig

Department of Computer Science and The Faculty of Information & Media Studies, Western University, London, Ontario, N6A 5B7, Canada
Website | E-Mail
Guest Editor
Dr. Paul Parsons

Department of Computer Graphics Technology, Purdue University, 401 N Grant St, West Lafayette, IN 47907, USA
Website | E-Mail
Interests: human-computer interaction; human-centered design; information visualization; visual interface design; interaction design; educational and learning technologies; applied cognition and perception

Special Issue Information

Dear Colleagues,

Many fields are encountering complex activities that involve intrinsically hard data analysis tasks—e.g., discovery-based research; emergency management; cybersecurity; and uncertain, risk-based decision-making. These activities are often open-ended, ill-specified, non-linear, and data-driven; they comprise a network of interdependent tasks; and they are domain-knowledge intensive and cognitively demanding. It is impossible for humans to perform such complex activities without support from powerful computational tools. There is a need for a coupling between human cognition and powerful computational tools, where the strengths of both are leveraged and tasks are distributed between human cognition and computation. Complex activities demand constant human-data interaction and decision-making—e.g., navigating and making sense of datasets; interpreting, selecting, constructing, and/or validating machine learning models; transforming input and output components (e.g., visualizations); deploying available resources; and managing tasks. These activities require a strong “human-in-the-loop” presence, where human perception, knowledge, and insight play a crucial role in accomplishing goal-oriented tasks.

The focus of this issue is on human cognition and computation teaming together to achieve goals of complex activities. We are interested in cases where human cognition and computation form a partnership and jointly carry out tasks. In such contexts, coupling is achieved through interaction between humans and computational artifacts. Thus the focus of the special issue is on coupling computation and cognition through interaction design. Submissions should address how computation and cognition work together through the deliberate design of interaction techniques and strategies.

We encourage authors to submit original research articles, works in progress, surveys, reviews, and viewpoint articles. This special issue welcomes general theories, models, and frameworks as well as applications in specific domains such as healthcare, education, neuroscience, bioinformatics, intelligence analysis, cybersecurity, and others. Topics of interest for the special issue include (but are not limited to):

  • Coupling human cognition and machine learning
  • Interactive visual data analysis
  • Interactive visualization and visual analytics
  • Human-in-the-loop analytics
  • Joint cognitive systems
  • Interactive model steering
  • Interactive data-driven learning
  • Human-computer joint reasoning
  • Human-computer knowledge discovery
  • Mixed-initiative interaction
  • Cognitive tools
  • Cognitive systems engineering

Dr. Kamran Sedig
Dr. Paul Parsons
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Multimodal Technologies and Interaction is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

Open AccessArticle Discourse with Visual Health Data: Design of Human-Data Interaction
Multimodal Technologies Interact. 2018, 2(1), 10; https://doi.org/10.3390/mti2010010
Received: 19 December 2017 / Revised: 9 March 2018 / Accepted: 10 March 2018 / Published: 20 March 2018
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Abstract
Previous work has suggested that large repositories of data can revolutionize healthcare activities; however, there remains a disconnection between data collection and its effective usage. The way in which users interact with data strongly impacts their ability to not only complete tasks but
[...] Read more.
Previous work has suggested that large repositories of data can revolutionize healthcare activities; however, there remains a disconnection between data collection and its effective usage. The way in which users interact with data strongly impacts their ability to not only complete tasks but also capitalize on the purported benefits of such data. Interactive visualizations can provide a means by which many data-driven tasks can be performed. Recent surveys, however, suggest that many visualizations mostly enable users to perform simple manipulations, thus limiting their ability to complete tasks. Researchers have called for tools that allow for richer discourse with data. Nonetheless, systematic design of human-data interaction for visualization tools is a non-trivial task. It requires taking into consideration a myriad of issues. Creation of visualization tools that incorporate rich human-data discourse would benefit from the use of design frameworks. In this paper, we examine and present a design process that is based on a conceptual human-data interaction framework. We discuss and describe the design of interaction for a visualization tool intended for sensemaking of public health data. We demonstrate the utility of systematic interaction design in two ways. First, we use scenarios to highlight how our design approach supports a rich and meaningful discourse with data. Second, we present results from a study that details how users were able to perform various tasks with health data and learn about global health trends. Full article
(This article belongs to the Special Issue Coupling Computation and Human Cognition through Interaction Design)
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Graphical abstract

Open AccessArticle Sense-making Strategies for the Interpretation of Visualizations—Bridging the Gap between Theory and Empirical Research
Multimodal Technologies Interact. 2017, 1(3), 16; https://doi.org/10.3390/mti1030016
Received: 31 May 2017 / Revised: 5 July 2017 / Accepted: 19 July 2017 / Published: 26 July 2017
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Abstract
Making sense of visualizations is often an open and explorative process. This process is still not very well understood. On the one hand, it is an open question which theoretical models are appropriate for the explanation of these activities. Heuristics and theories of
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Making sense of visualizations is often an open and explorative process. This process is still not very well understood. On the one hand, it is an open question which theoretical models are appropriate for the explanation of these activities. Heuristics and theories of everyday thinking probably describe this process better than more formal models. On the other hand, there are only few detailed investigations of interaction processes with information visualizations. We will try to relate approaches describing the usage of heuristics and everyday thinking with existing empirical studies describing sense-making of visualizations. Full article
(This article belongs to the Special Issue Coupling Computation and Human Cognition through Interaction Design)
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Open AccessArticle Evaluating Interactive Visualization of Multidimensional Data Projection with Feature Transformation
Multimodal Technologies Interact. 2017, 1(3), 13; https://doi.org/10.3390/mti1030013
Received: 31 May 2017 / Revised: 3 July 2017 / Accepted: 4 July 2017 / Published: 8 July 2017
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Abstract
There has been extensive research on dimensionality reduction techniques. While these make it possible to present visually the high-dimensional data in 2D or 3D, it remains a challenge for users to make sense of such projected data. Recently, interactive techniques, such as Feature
[...] Read more.
There has been extensive research on dimensionality reduction techniques. While these make it possible to present visually the high-dimensional data in 2D or 3D, it remains a challenge for users to make sense of such projected data. Recently, interactive techniques, such as Feature Transformation, have been introduced to address this. This paper describes a user study that was designed to understand how the feature transformation techniques affect user’s understanding of multi-dimensional data visualisation. It was compared with the traditional dimension reduction techniques, both unsupervised (PCA) and supervised (MCML). Thirty-one participants were recruited to detect visual clusters and outliers using visualisations produced by these techniques. Six different datasets with a range of dimensionality and data size were used in the experiment. Five of these are benchmark datasets, which makes it possible to compare with other studies using the same datasets. Both task accuracy and completion time were recorded for comparison. The results show that there is a strong case for the feature transformation technique. Participants performed best with the visualisations produced with high-level feature transformation, in terms of both accuracy and completion time. The improvements over other techniques are substantial, particularly in the case of the accuracy of the clustering task. However, visualising data with very high dimensionality (i.e., greater than 100 dimensions) remains a challenge. Full article
(This article belongs to the Special Issue Coupling Computation and Human Cognition through Interaction Design)
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