Science of Interaction: Difference between revisions
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'''Create a new science of interaction to support visual analytics.''' | '''Create a new science of interaction to support visual analytics.''' | ||
The grand challenge of interaction is to develop a taxonomy to describe the design space of interaction techniques that supports the science of analytical reason- ing. We must characterize this design space and identify under-explored areas that are relevant to visual analytics. Then, R&D should be focused on expanding the repertoire of interaction techniques that can fill those gaps in the design space.|[Thomas and Cook, 2005]}} | The grand challenge of interaction is to develop a taxonomy to describe the design space of interaction techniques that supports the science of analytical reason- ing. We must characterize this design space and identify under-explored areas that are relevant to visual analytics. Then, R&D should be focused on expanding the repertoire of interaction techniques that can fill those gaps in the design space. | ||
|[Thomas and Cook, 2005]}} | |||
====Interaction techniques for human-information discourse==== | |||
{{Quotation| | |||
Existing work on interaction techniques for human-computer interaction and information visualization has focused on cognitive time bands, interaction for data manipulation, visual mapping manipulation, and navigation. The discussion on analytic discourse and sense-making in Chapter 2 makes it clear the higher-level dialogue between analyst and information, or human-information discourse, is of vital importance. This discourse involves the rational time band and higher-level uses of interaction, but neither has been sufficiently explored. | |||
''Recommendation 3.4'' | |||
'''Expand the science of interaction to support the human-information discourse needed for analytical reasoning. In particular, identify and develop interaction techniques that support higher-level reasoning and that address the rational human timeframe.''' | |||
Human beings are very skilled at analyzing complex situations using a combination of their available information and their combined knowledge and experience. However, there are inherent human tendencies that analysts must recognize and overcome. Interaction techniques must be developed that support an analytic discourse and help compensate for human limitations, including: | |||
* Information overload in complex situations. Techniques are needed to help analysts simplify their cognitive load without compromising the analyst’s effectiveness and to help compensate for faulty memory. | |||
* Overcoming biases. Biases affect the way data are interpreted. Biases about the reliability of different sources may lead people to discount information from sources that aren’t considered reliable. People often see what they expect to see and tend to ignore evidence that is contradictory to a preferred theory. If they form a preliminary judgment too early in the analytical process, they may hold firm to it long after the evidence invalidates it [Heuer, 1999]. | |||
* Satisficing. People settle for a “good enough” answer, sometimes stopping their analytical process before they identify critical information that would lead them to a different conclusion [Heuer, 1999]. | |||
New interaction techniques are needed to support the user in evaluating evidence, challenging assumptions, and finding alternatives. Analytical environments should support the user in identifying and understanding all relevant information to reach a solid conclusion rapidly. The tools we create need to establish a correct balance between structure and intuition. | |||
|[Thomas and Cook, 2005]}} | |||
== References == | == References == | ||
Revision as of 18:58, 6 April 2011
Starting point:
A Science of Interaction
p.73-77 (Chapter 3) in [Thomas and Cook, 2005]
Technology Needs
Basic interaction techniques
To achieve successful adoption, visual analytics software must support both basic interactions and highly sophisticated interactions that support the analytic reasoning process. Before these more sophisticated interactions can be addressed systematically, work is needed to create a scientific understanding about the basic interactions that are used to support simpler operations. This understanding will form the foundation for research into more sophisticated interactions.
Recommendation 3.3
Create a new science of interaction to support visual analytics.
The grand challenge of interaction is to develop a taxonomy to describe the design space of interaction techniques that supports the science of analytical reason- ing. We must characterize this design space and identify under-explored areas that are relevant to visual analytics. Then, R&D should be focused on expanding the repertoire of interaction techniques that can fill those gaps in the design space.
Interaction techniques for human-information discourse
Existing work on interaction techniques for human-computer interaction and information visualization has focused on cognitive time bands, interaction for data manipulation, visual mapping manipulation, and navigation. The discussion on analytic discourse and sense-making in Chapter 2 makes it clear the higher-level dialogue between analyst and information, or human-information discourse, is of vital importance. This discourse involves the rational time band and higher-level uses of interaction, but neither has been sufficiently explored.
Recommendation 3.4
Expand the science of interaction to support the human-information discourse needed for analytical reasoning. In particular, identify and develop interaction techniques that support higher-level reasoning and that address the rational human timeframe.
Human beings are very skilled at analyzing complex situations using a combination of their available information and their combined knowledge and experience. However, there are inherent human tendencies that analysts must recognize and overcome. Interaction techniques must be developed that support an analytic discourse and help compensate for human limitations, including:
- Information overload in complex situations. Techniques are needed to help analysts simplify their cognitive load without compromising the analyst’s effectiveness and to help compensate for faulty memory.
- Overcoming biases. Biases affect the way data are interpreted. Biases about the reliability of different sources may lead people to discount information from sources that aren’t considered reliable. People often see what they expect to see and tend to ignore evidence that is contradictory to a preferred theory. If they form a preliminary judgment too early in the analytical process, they may hold firm to it long after the evidence invalidates it [Heuer, 1999].
- Satisficing. People settle for a “good enough” answer, sometimes stopping their analytical process before they identify critical information that would lead them to a different conclusion [Heuer, 1999].
New interaction techniques are needed to support the user in evaluating evidence, challenging assumptions, and finding alternatives. Analytical environments should support the user in identifying and understanding all relevant information to reach a solid conclusion rapidly. The tools we create need to establish a correct balance between structure and intuition.
References
- [Thomas and Cook, 2005] J.J. Thomas and K.A. Cook, eds., Illuminating the Path: The Research and Development Agenda for Visual Analytics, IEEE CS Press, 2005.