Complexity in Human-Computer Interfaces: Information-Theoretic Approaches and Beyond
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 1681
Special Issue Editor
Interests: human-computer interaction; universal design; usability engineering; visual perception; web user interfaces; user behavior models; accessibility; knowledge engineering; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Evaluation and testing of user interfaces (UIs) is one of the most labor-intensive and subjective stages in software engineering today. Complexity, which is an intensively developed topic in Mathematics, remains quite an elusive attribute in human–computer interaction (HCI).
The straightforward information-theoretic approach and the respective complexity metrics, such as the ones based on Hick–Hyman law, lack the ability to describe the integral nature of human perception. Hence, Gestalt principles describing grouping were proposed as an alternative, and the algorithmic theory of information was then put forward to unite the two approaches. Hence, the state-of-the-art metrics of visual complexity are based on compression algorithms (e.g., JPEG), but these have certain limitations with respect to HCI. Particularly, they do not reflect user-specific aspects, such as familiarity with the stimuli, nor are they relevant for describing tasks and the actual interaction with a computer system. Meanwhile, complexity is known to affect many other qualities of UI, and its automated evaluation is seen as highly desirable.
By initiating this Special Issue, we seek to promote further exploration on this topic. Both theoretic contributions and papers that describe concrete software implementations of algorithms quantifying aspects of UI complexity are welcome.
Prof. Dr. Maxim Bakaev
Guest Editor
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Keywords
- user interfaces
- visual perception
- information processing
- model human processor
- Gestalt principles
- familiarity
- pattern recognition
- Hick–Hyman law
- Solomonoff–Kolmogorov–Chaitin complexity
- algorithmic entropy
