GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Information
Open accessAbstract
We investigate how multiple sliders with and without feedforward visualizations influence users’ control of generative models. In an online study (N=138), we collected a dataset of people interacting with a generative adversarial network (StyleGAN2) in an image reconstruction task. We found that more control dimensions (sliders) significantly increase task difficulty and user actions. Visual feedforward partly mitigates this by enabling more goal-directed interaction. However, we found no evidence of faster or more accurate task performance. This indicates a tradeoff between feedforward detail and implied cognitive costs, such as attention. Moreover, we found that visualizations alone are not always sufficient for users to understand individual control dimensions. Our study quantifies fundamental UI design factors and resulting interaction behavior in this context, revealing opportunities for improvement in the UI design for interactive applications of generative models. We close by discussing design directions and further aspects.
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System customizability can moderate the metacognitive demands of GenAI.
“The question of how much control to give users-system customizability, or how many 'knobs' a user can adjust-is another design choice that can moderate the metacognitive demands of GenAI [42,119].”
From The Metacognitive Demands and Opportunities of Generative AI (2024) · cites this paper as [42]
Section 5.10 (pp. 8–9) reports that more sliders increased perceived workload and made decisions about which slider to adjust next harder, supporting the point that the number of controls affects user demands. The study tested an image-generation task, however, and did not directly measure metacognitive demands across GenAI systems. (LLM, conf=0.83)
Experimental — accuracy not yet measuredFeedforward information can increase cognitive load.
“However, feedforward information can also increase cognitive load [42], so the right balance, particularly for complex systems, remains to be explored.”
From The Metacognitive Demands and Opportunities of Generative AI (2024) · cites this paper as [42]
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- Authors
- Dang, H., Mecke, L., & Buschek, D.
- Affiliations
- University of Bayreuth (DE)
- Universität der Bundeswehr München (DE)
- Venue
- CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2022-04-29
- Pages
- 1-15
- OpenAlex
- W4221086478
- Cited by
- 37
- References
- 43
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 11 · 2023: 4 · 2022: 1
- FWCI
- 5.80
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- ERef Bayreuth (University of Bayreuth) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Bavarian State Ministry of Science and the Arts
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Interactive and Immersive Displays
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Keywords
- generative adversarial networks, StyleGAN2, slider control, image reconstruction, control dimension, user interface design, cognitive load, attention, human-computer interaction
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