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Wai-Tat Fu
Researcher at University of Illinois at Urbana–Champaign
Publications - 162
Citations - 4146
Wai-Tat Fu is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Cognition & Exploratory search. The author has an hindex of 30, co-authored 162 publications receiving 3804 citations. Previous affiliations of Wai-Tat Fu include Carnegie Mellon University & BBN Technologies.
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Journal ArticleDOI
The soft constraints hypothesis: a rational analysis approach to resource allocation for interactive behavior.
TL;DR: Model and data support the SCH view of resource allocation; at the under 1000-ms level of analysis, mixtures of cognitive and perceptual-motor resources are adjusted based on their cost-benefit tradeoffs for interactive behavior.
SNIF-ACT: A Cognitive Model of User Navigation on the World Wide Web
Wai-Tat Fu,Peter Pirolli +1 more
TL;DR: In this paper, the authors presented a cognitive model that simulates how people seek information on the Web, called SNIF-ACT, which stands for Scent-based Navigation and Information Foraging in the ACT architecture.
Journal ArticleDOI
SNIF-ACT: a cognitive model of user navigation on the world wide web
Wai-Tat Fu,Peter Pirolli +1 more
TL;DR: The model was tested against a detailed set of protocol data collected from 8 participants as they engaged in two information-seeking tasks using the World Wide Web and concluded that the combination of the IFT and the BSM provides a good description of user-Web interaction.
Journal ArticleDOI
Soft constraints in interactive behavior: the case of ignoring perfect knowledge in-the-world for imperfect knowledge in-the-head*,**
Wayne D. Gray,Wai-Tat Fu +1 more
TL;DR: Soft constraints lead to a reliance on knowledge in-the-head even when the absolute difference in perceptual-motor versus memory retrieval effort is small, and even when relying on memory leads to a higher error rate and lower performance.
Book ChapterDOI
SNIF-ACT: a model of information foraging on the world wide web
Peter Pirolli,Wai-Tat Fu +1 more
TL;DR: The results suggest that the current content-based spreading activation SNIF-ACT model is able to generate useful predictions about complex user-WWW interactions.