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Soft machines: A philosophy of user-computer interface design

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Abstract

Machines and computer systems differ in many characteristics that have important consequences for the user. Machines are special-purpose, have forms suggestive of their functions, are operated with controls in obvious one-to-one correspondence with their actions, and the consequences of the actions on visible objects are immediately and readily apparent. By contrast, computer systems are general-purpose, have inscrutable form, are operated symbolically via a keyboard with no obvious correspondence between keys and actions, and typically operate on invisible objects with consequences that are not immediately or readily apparent. The characteristics possessed by machines, but typically absent in computer systems, aid learning, use and transfer among machines. But “hard,” physical machines have limitations: they are inflexible, and their complexity can overwhelm us. We have built in our laboratory “soft machine” interfaces for computer systems to capitalize on the good characteristics of machines and overcome their limitations. A soft machine is implemented using the synergistic combination of real-time computer graphics to display “soft controls,” and a touch screen to make soft controls operable like conventional hard controls.
... Fog of War is a form of progressive disclosure (Nakatani & Rohrlich, 1983;Springer & Whittaker, 2019). Games with an exploration component often enforce Fog of War to limit players' record of the world to the areas they have explored. ...
... Otherwise, the user can't easily adapt to the machine. Nakatani [16] constructed a laboratory-based soft machine interface using a synergistic combination of realtime computer graphics and touch screen with some specific soft controls. Schmandt [17] developed a soft machine using color graphics and touch screen to communicate with the user. ...
Conference Paper
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... Progressive disclosure has a long history in UI research, dating back to the Xerox Star and early word processing systems [14,61,74]. The original concept involved hiding advanced interface controls; allowing users to make fewer initial errors and learn the system more effectively [14]. ...
Conference Paper
As we increasingly delegate important decisions to intelligent systems, it is essential that users understand how algorithmic decisions are made. Prior work has often taken a technocentric approach to transparency. In contrast, we explore empirical user-centric methods to better understand user reactions to transparent systems. We assess user reactions to transparency in two studies. In Study 1, users anticipated that a more transparent system would perform better, but retracted this evaluation after experience with the system. Qualitative data suggest this arose because transparency is distracting and undermines simple heuristics users form about system operation. Study 2 explored these effects in depth, suggesting that users may benefit from initially simplified feedback that hides potential system errors and assists users in building working heuristics about system operation. We use these findings to motivate new progressive disclosure principles for transparency in intelligent systems.
... Progressive disclosure has a long history in UI research, dating back to the Xerox Star and early word processing systems [14,59,72]. The original concept involved hiding advanced interface controls; allowing users to make fewer initial errors and learn the system more effectively [14]. ...
Preprint
As we increasingly delegate important decisions to intelligent systems, it is essential that users understand how algorithmic decisions are made. Prior work has often taken a technocentric approach to transparency. In contrast, we explore empirical user-centric methods to better understand user reactions to transparent systems. We assess user reactions to global and incremental feedback in two studies. In Study 1, users anticipated that the more transparent incremental system would perform better, but retracted this evaluation after experience with the system. Qualitative data suggest this may arise because incremental feedback is distracting and undermines simple heuristics users form about system operation. Study 2 explored these effects in depth, suggesting that users may benefit from initially simplified feedback that hides potential system errors and assists users in building working heuristics about system operation. We use these findings to motivate new progressive disclosure principles for transparency in intelligent systems.
Chapter
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Conference Paper
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Presents and discusses data, principles, and design practices for human engineering. Topics covered include the visual and auditory presentation of information and the design of controls, work areas, and training systems and devices. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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