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Book Chapters

Fu, W.-T. & Pirolli, P. (2013). Establishing the micro-to-macro link: Multilevel models of human-information interaction. The Oxford Handbook of Cognitive Engineering. [PDF] [Abstract]

We argue that the time is ripe to develop multilevel models that provide explanations at both the individual and social levels to extend research in human-computer interaction to the emerging area called socio-computer interaction. We provide three examples of such multilevel models. We first describe the SNIF-ACT model, which was developed to explain individuals’ information foraging behavior as they navigate through web pages to find target information, and to explain the general tendency for people to stay on any particular website. We then describe the social information Foraging theory, which characterizes how search efficiencies may be improved by utilizing social signals generated by other information foragers. Finally, we describe the semantic imitation model, which characterizes how individual users may generate social tags to index their own documents and could lead to aggregate indices that act as semantic structures that help other users to find relevant information. The models demonstrate the significance in capturing the social dynamics involved in systems that afford socio-computer interaction.

Fu, W.-T. (2012). From Plato to the WWW: Exploratory Information Foraging. In P. M. Todd & T. Robbins (Eds.), Cognitive Search. MIT Press. [PDF] [Abstract]

Generally speaking, two conditions make cognitive search possible: (a) symbolic structures must be present in the environment and (b) these structures must be detectable by a searcher, whose behavior changes based on the structures detected. In this chapter, information search on the Internet is used to illustrate how a theoretical framework of these two conditions can assist our understanding of cognitive search. Discussion begins with information foraging theory (IFT), which predicts how general symbolic structures may exist in an information environment and how the searcher may use these structures to search for information. A computational model called SNIF-ACT (developed based on IFT) is then presented and provides a good match to online information search for specifi c target information. Because a further component important to cognitive search is the ability to detect and learn useful structures in the environment, discussion follows on how IFT can be extended to explain search behavior that involves incremental learning of the search environment. Illustration is provided on how different forms of semantic structures may exist in the World Wide Web, and how human searchers can learn from these structures to improve their search. Finally, the SNIFACT model is extended to characterize directed and exploratory information foraging behavior in information environments.

Fu, W.-T., Chin, J., Dong, W., & liao, Q. V. (2011). Interactive Skills. In N. M. Seel (Ed.), Encyclopedia of the Sciences of Learning, Springer-Verlag. [PDF]

Fu, W.-T. & Kannampallil, T. (2009). Harnessing Web 2.0 for context-aware learning: The impact of social tagging system on knowledge adaption. In N. Lambropoulos and R. Margarida (Eds.), Educational Social Software for Context-Aware Learning: Collaborative Methods and Human Interaction. IGI Global.

Fu, W.-T. (2007), Adaptive Tradeoffs Between Exploration and Exploitation: A Rational-Ecological Approach. In W. D. Gray (Ed.), Integrated Models of Cognitive Systems. Oxford: Oxford University Press. [PDF] [Abstract]

We describe the development of a computational cognitive model that explains navigation behavior on the World Wide Web. The model, called SNIF-ACT (Scent-basedNavigation and Information Foraging in the ACT cognitive architecture), is motivated by Information Foraging Theory (IFT), which quantifies the perceived relevance of aWeb link to a user’s goal by a spreading activation mechanism. The model assumes that users evaluate links on a Web page sequentially and decide to click on a link or to go back to the previous page by a Bayesian satisficing model (BSM) that adaptively evaluates and selects actions based on a combination of previous and current assessments of the relevance of link texts to information goals. SNIF-ACT 1.0 utilizes the measure of utility, called informationscent, derived from IFT to predict rankings of links on different Web pages. 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. The model provided a good match to participants’ link selections. In SNIF-ACT 2.0, we included the adaptive link selection mechanism from the BSM that sequentially evaluates links on a Web page. The mechanism allowed the model to dynamically build up the aspiration levels of actions in a satisficing process (e.g., to follow a link or leave aWeb site) as it sequential assessed link texts on aWeb page. The dynamic mechanism provides an integrated account of how and when users decide to click on a link or leave a page based on the sequential, ongoing experiences with the link context on current and previous Web pages. SNIF-ACT 2.0 was validated on a data set obtained from 74 subjects. Monte Carlo simulations of the model showed that SNIF-ACT 2.0 provided better fits to human data than SNIF-ACT 1.0 and a Position model that used position of links on aWeb page to decide which link to select.We conclude that the combination of the IFT and the BSMprovides a good description of user–Web interaction. Practical implications of the model are discussed.