Publications

2009

• Fu, W.-T., Kannampallil, T. G., Kang, Ruogu (2009), A Semantic Imitation Model of Social Tag Choices. In Proceedings of the 2009 International Conference on Social Computing, pp. 66-72, Vancouver, BC. We describe a semantic imitation model of social tagging that integrates formal representations of semantics and a stochastic tag choice process to explain and predict emergent behavioral patterns. The model adopts a probabilistic topic model to separately represent external word-topic and internal word-concept relations. These representations are coupled with a tag-based topic inference process that predicts how existing tags may influence the semantic interpretation of a document. The inferred topics influence the choice of tags assigned to a document through a random utility model of tag choices. We show that the model is successful in explaining the stability in tag proportions across time and power-law frequency-rank distributions of tag co-occurrences for semantically general and narrow tags. The model also generates novel predictions on how emergent behavioral patterns may change when users with different domain expertise interact with a social tagging system. The model demonstrates the weaknesses of single-level analyses and highlights the importance of adopting a multi-level modeling approach to explain online social behavior.
• 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.
• Moon, J. M., & Fu, W.-T. (2009). Effects of spatial locations and luminance on finding and re-finding information in a desktop environment. In Proceedings of the 27th annual conference of ACM Computer-Human Interaction (CHI) conference, Boston, MA, US.
• Kang, R., Kannampallil, T., He, J ,& Fu, W.-T. (2009). Conformity out of Diversity: Dynamics of Information Needs and Social Influence of Tags in Exploratory Information Search. In Proceedings of the International conference of Human-Computer Interaction, CA: San Diego, US We studied the dynamic effects of information needs and social influence of tags in an exploratory search task. Although initially differences in information needs led to diversity in tag choices, this diversity disappeared as participants collaboratively tagged the same set of resources. Our findings are in general consistent with the notion that people conform to the collective interpretation of contents in an information system. In addition, our results showed that conformity does not only arise out of imitation of behavior, but also from the same underlying semantic interpretation or knowledge structures of users as they engage in informal collaboration through the social tagging system. Implications for design of social information system are discussed.
• Kannampallil, T., & Fu, W.-T. (2009). Trail Patterns in Social Tagging Systems: Role of Tags as Digital Pheromones. In Proceedings of the International conference of Human-Computer Interaction, CA: San Diego, US.
• Fu, W.-T., & Park, H. (2009). The role of information seeking in risk assessment. In Proceedings of the Annual Conference of the Society of Judgment and Decision Making, Boston, MA
• Chin, J. & Fu, W.-T. (2009). Age-Dependent Interactions Between Cognitive Profiles and Information Search Strategies. In Proceedings of the Annual Meeting of the Psychonomics Society, Boston, MA.

2008

• Fu, W.-T., Anderson, J. (2008). Dual Learning Processes in Interactive Skill Acquisition. Journal of Experimental Psychology, Applied, 14 (2), 179-191. Fu, W.-T., Anderson, J. (2008). Dual Learning Processes in Interactive Skill Acquisition. Journal of Experimental Psychology, Applied, 14 (2), 179-191. Acquisition of interactive skills involves the use of internal and external cues. Experiment 1 showed that when actions were interdependent, learning was effective with and without external cues in the single-task condition but was effective only with the presence of external cues in the dual-task condition. In the dual-task condition, actions closer to the feedback were learned faster than actions farther away but this difference was reversed in the single-task condition. Experiment 2 tested how knowledge acquired in single and dual-task conditions would transfer to a new reward structure. Results confirmed the two forms of learning mediated by the secondary task: A declarative memory encoding process that simultaneously assigned credits to actions and a reinforcement-learning process that slowly propagated credits backward from the feedback. The results showed that both forms of learning were engaged during training, but only at the response selection stage, one form of knowledge may dominate over the other depending on the availability of attentional resources.
• Fu, W.-T., Anderson, J. (2008), Solving the Credit Assignment Problem: Explicit and Implicit Learning of Action Sequences with Probabilistic Outcomes. Psychological Research, 72 (3), 321-330. In most problem-solving activities, feedback is received at the end of an action sequence. This creates a credit-assignment problem where the learner must associate the feedback with earlier actions, and the interdependencies of actions require the learner to remember past choices of actions. In two studies, we investigated the nature of explicit and implicit learning processes in the credit-assignment problem using a probabilistic sequential choice task with and without a secondary memory task. We found that when explicit learning was dominant, learning was faster to select the better option in their Wrst choices than in the last choices. When implicit reinforcement learning was dominant, learning was faster to select the better option in their last choices than in their Wrst choices. Consistent with the probability learning and sequence-learning literature, the results show that credit assignment involves two processes: an explicit memory encoding process that requires memory rehearsals and an implicit reinforcement-learning process that propagates credits backwards to previous choices.
• Fu, W.-T. (2008). Is a single-bladed knife enough to dissect cognition? Cognitive Science, 32 (1), 155-161. Griffiths,Christian, and Kalish (this issue) present an iterative-learning paradigm applying aBayesian model to understand inductive biases in categorization. The authors argue that the paradigm is useful as an exploratory tool to understand inductive biases in situations where little is known about the task. It is argued that a theory developed only at the computational level is much like a single-bladed knife that is only useful in highly idealized situations. To be useful as a general tool that cuts through the complex fabric of cognition, we need at least two-bladed scissors that combine both computational and psychological constraints to characterize human behavior. To temper its sometimes expansive claims, it is time to show what a Bayesian model cannot explain. Insight as to how human reality may differ from the Bayesian predictions may shed more light on human cognition than the simpler focus on what the Bayesian approach can explain. There remains much to be done in terms of integrating Bayesian approaches and other approaches in modeling human cognition.
• Fu, W.-T. (2008). The microstructures of social tagging: A rational model. In Proceedings of the ACM 2008 conference on Computer Supported Cooperative Work (CSCW), pp 229-238. San Diego, CA, US. This article presents a rational model developed under the distributed cognition framework that explains how social tags influence knowledge acquisition and adaptation in exploratory ill-defined information tasks. The model provides integrated predictions on the interactions among link selections, use and creation of tags, and the formation of mental categories. The model shows that the quality of tags not only influences search efficiency, but also the quality of mental categories formed during exploratory search. In addition, the model shows that aggregate regularities can be explained by microstructures of behavior that emerged from the adaptive assimilation of concepts and categories of multiple users through the social tagging system. The model has important implications on how collaborative systems could influence higher-level cognitive activities.
• Fu, W.-T. (2008). How adaptive is consumer sequential decision making? In Proceedings of the Annual Conference of the Society of Judgment and Decision Making, Chicago, IL, US
• Fu, W.-T. (2008). SNIF-DM: A Cognitive Model of Information Seeking and Decision Making on the World Wide Web. In Proceedings of the 2nd International Conference on Applied Human Factors, Las Vegas, NV, US.
• Chin, J., Fu, W.-T., & Kannampallil, T. (2009). Adaptive Information Search: Age-Dependent Interactions between Cognitive Profiles and Strategies. In Proceedings of the 27th annual conference of ACM Computer-Human Interaction (CHI) conference, Boston, MA, US. Previous research has shown that older adults performed worse in web search tasks, and attributed poorer performance to a decline in their cognitive abilities. We conducted a study involving younger and older adults to compare their web search behavior and performance in illdefined and well-defined information tasks using a health information website. In ill-defined tasks, only a general description about information needs was given, while in well-defined tasks, information needs as well as the specific target information were given. We found that older adults performed worse than younger adults in well-defined tasks, but the reverse was true in ill-defined tasks. Older adults compensated for their lower cognitive abilities by adopting a top-down knowledge-driven strategy to achieve the same level of performance in the ill-defined tasks. Indeed, path analysis showed that cognitive abilities, health literacy, and knowledge influenced search strategies adopted by older and younger adults. Design implications are also discussed.
• Moon, M., & Fu, W.-T. (2008). A situated cognitive model of the routine evolution of skills. In Proceedings of the 52nd Conference of the Human Factors and Ergonomics Society, New York, NY.

2007

• 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. How do humans or animals adapt to a new environment? After years of research, it is embarrassing how little we understand the underlying processes of adaptation: just look at how difficult it is to build a robot that learns to navigate in a new environment or to teach someone to master a second language. It is amazing how seagulls and vultures have learned to be landfill scavengers in the last century and be able to sort through human garbage to dig out edible morsels. At the time this chapter is written, an alligator is found in the city park of Los Angeles, outwitting licensed hunters who tried to trap the alligator for over 2 months. The ability to adapt to new environments goes beyond hardwired processes and relies on the ability to acquire new knowledge of the environment. An important step in the adaptation process is to sample the effects of possible actions and world states so that the right set of actions can be chosen to attain important goals in the new environment.
• Fu, W.-T., Pirolli, P. (2007), SNIF-ACT: A Model of Information-Seeking Behavior in the World Wide Web. Human-Computer Interaction, 22, 355-412. 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.
• Miller, S. & Fu, W.-T. (2007). The role of temporal sequence learning in guiding visual attention allocation. In Proceedings of the 51st Conference of the Human Factors and Ergonomics Society, Baltimore, MD, US.

2006

• Fu, W.-T., Gray, W. D. (2006), Suboptimal tradeoffs in information-seeking. Cognitive Psychology, 52, 195-242. Explicit information-seeking actions are needed to evaluate alternative actions in problem-solving tasks. Information-seeking costs are often traded off against the utility of information. We present three experiments that show how subjects adapt to the cost and information structures of environments in a map-navigation task. We found that subjects often stabilize at suboptimal levels of performance. A Bayesian satisficing model (BSM) is proposed and implemented in the ACT-R architecture to predict information-seeking behavior. The BSM uses a local decision rule and a global Bayesian learning mechanism to decide when to stop seeking information. The model matched the human data well, suggesting that adaptation to cost and information structures can be achieved by a simple local decision rule. The local decision rule, however, often limits exploration of the environment and leads to suboptimal performance. We propose that suboptimal performance is an emergent property of the dynamic interactions between cognition and the environment.
• Fu, W.-T., Anderson, J. R. (2006), From recurrent choice to skilled learning: A reinforcement learning model. Journal of Experimental Psychology: General, 135(2), 184-206. The authors propose a reinforcement-learning mechanism as a model for recurrent choice and extend it to account for skill learning. The model was inspired by recent research in neurophysiological studies of the basal ganglia and provides an integrated explanation of recurrent choice behavior and skill learning. The behavior includes effects of differential probabilities, magnitudes, variabilities, and delay of reinforcement. The model can also produce the violation of independence, preference reversals, and the goal gradient of reinforcement in maze learning. An experiment was conducted to study learning of action sequences in a multistep task. The fit of the model to the data demonstrated its ability to account for complex skill learning. The advantages of incorporating the mechanism into a larger cognitive architecture are discussed.
• Fu, W.-T., Bothell, D., Douglass, S., Haimson, C., Sohn, M.-H., Anderson, J. A. (2006), Toward a real-time model-based training system. Interacting with Computers, 18(6), 1216-1230. This article describes the development of a real-time model-based training system that provides adaptive ‘‘over-the-shoulder’’ (OTS) instructions to trainees as they learn to perform an Anti-Air Warfare Coordinator (AAWC) task. The long-term goal is to develop a system that will provide real-time instructional materials based on learners’ actions, so that eventually the initial set of instructions on a task can be strengthened, complemented, or overridden at different stages of training. The training system is based on the ACT-R architecture, which serves as the theoretical background for the cognitive model that monitors the learning process of the trainee. An experiment was designed to study the impact of OTS instructions on learning. Results showed that while OTS instructions facilitated short-term learning, (a) they took time away from the processing of current information, (b) their effects tended to decay rapidly in initial stages of training, and (c) their effects on training diminished when the OTS instructions were proceduralized in later stages of training. A cognitive model that learned from both theupfront and OTS instructions was created and provided good fits to the learning and performance data collected from human participants. Our results suggest that to fully capture the symbiotic performance between humans and intelligent training systems, it is important to closely monitor the learning process of the trainee so that instructional interventions can be delivered effectively at different stages of training. We proposed that such a flexible system can be developed based on an adaptive cognitive model that provides real-time predictions on learning and performance.
• Gray, W. D., Sims, C. R., Fu, W.-T., Schoelles, M. J. (2006), The soft constraints hypothesis: A rational analysis approach to resource allocation for interactive behavior. Psychological Review, 113(3), 461-482. Soft constraints hypothesis (SCH) is a rational analysis approach that holds that the mixture of perceptual-motor and cognitive resources allocated for interactive behavior is adjusted based on temporal cost-benefit tradeoffs. Alternative approaches maintain that cognitive resources are in some sense protected or conserved in that greater amounts of perceptual-motor effort will be expended to conserve lesser amounts of cognitive effort. One alternative, the minimum memory hypothesis (MMH), holds that people favor strategies that minimize the use of memory. SCH is compared with MMH across 3 experiments and with predictions of an Ideal Performer Model that uses ACT-R’s memory system in a reinforcement learning approach that maximizes expected utility by minimizing time. 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.
• Fu, W.-T., Anderson, J. R. (2006), Solving the credit assignment problem: Explicit and implicit learning with internal and external state information. Proceedings of the 28th Annual Conference of the Cognitive Science Society. Hillsdale, NJ: LEA.
• Fu, W.-T., Gonzalez, C. (2006), Learning to control dynamic systems: Information utilization and future planning. Proceedings of the 28th Annual Conference of the Cognitive Science Society. Hillsdale, NJ: LEA.
• Fu, W.-T., Gonzalez, C, Healy, A., Kole, J., Bourne, L. (2006), Building predictive human performance models of skill acquisition in a data entry task. Proceedings of the 50th Annual Meeting of the Human Factors and Ergonomics Society (pp. 1122-1126). Santa Monica, CA: Human Factors and Ergonomics Society.
• Fu, W.-T. (2006), Adaptive acquisition of enactive knowledge. Proceedings of the Third International Conference on Enactive Interfaces, Montpellier, France.

2004

• Fu, W.-T., Gray, W. D. (2004), Resolving the paradox of the active user: Stable suboptimal performance in interactive tasks. Cognitive Science, 28(6), 901-935. This paper brings the intellectual tools of cognitive science to bear on resolving the “paradox of the active user” [Interfacing Thought: Cognitive Aspects of Human–Computer Interaction, Cambridge, MIT Press, MA, USA]—the persistent use of inefficient procedures in interactive tasks by experienced or even expert users when demonstrably more efficient procedures exist. The goal of this paper is to understand the roots of this paradox by finding regularities in these inefficient procedures. We examine three very different data sets. For each data set, we first satisfy ourselves that the preferred procedures used by some subjects are indeed less efficient than the recommended procedures. We then amass evidence, for each set, and conclude that when a preferred procedure is used instead of a more efficient, recommended procedure, the preferred procedure tends to have two major characteristics: (1) the preferred procedure is a well-practiced, generic procedure that is applicable either within the same task environment in different contexts or across different task environments, and (2) the preferred procedure is composed of interactive components that bring fast, incremental feedback on the external problem states. The support amassed for these characteristics leads to a new understanding of the paradox. In interactive tasks, people are biased towards the use of general procedures that start with interactive actions. These actions require much less cognitive effort as each action results in an immediate change to the external display that, in turn, cues the next action. Unfortunately for the users, the bias to use interactive unit tasks leads to a path that requires more effort in the long run. Our data suggest that interactive behavior is composed of a series of distributed choices; that is, people seldom make a once-and-for-all decision on procedures. This series of biased selection of interactive unit tasks often leads to a stable suboptimal level of performance.
• Gray, W. D., Fu, W.-T. (2004), Soft constraints in interactive behavior: The case of ignoring perfect knowledge in-the-world for imperfect knowledge in-the-head. Cognitive Science, 28(3), 359-382. Constraints and dependencies among the elements of embodied cognition form patterns or microstrategies of interactive behavior. Hard constraints determine which microstrategies are possible. Soft constraints determine which of the possible microstrategies are most likely to be selected. When selection is non-deliberate or automatic the least effort microstrategy is chosen. In calculating the effort required to execute a microstrategy each of the three types of operations, memory retrieval, perception, and action, are given equal weight; that is, perceptual-motor activity does not have a privileged status with respect to memory. Soft constraints can work contrary to the designer’s intentions by making the access of perfect knowledge in-the-world more effortful than the access of imperfect knowledge in-the-head. These implications of soft constraints are tested in two experiments. In experiment 1 we varied the perceptual-motor effort of accessing knowledge in-the-world as well as the effort of retrieving items from memory. In experiment 2 we replicated one of the experiment 1 conditions to collect eye movement data. The results suggest that milliseconds matter. 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. We discuss the implications of soft constraints for routine interactive behavior, accounts of embodied cognition, and tool and interface design.
• Fu, W.-T., Anderson, J. R. (2004), Extending the computational abilities of the procedural learning mechanism in ACT-R. Proceedings of the 26th Annual Conference of the Cognitive Science Society. Hillsdale, NJ: LEA.
• Fu, W.-T., Bothell, D., Douglass, S., Haimson, C., Sohn, M.-H, Anderson, J. A. (2004), Learning from real-time over-the-shoulder instructions in a dynamic task. Proceedings of the Sixth International Conference on Cognitive Modeling. Pittsburgh, PA.
• Gray, W. D., Veksler, D., Fu, W.-T. (2004), Probing the paradox of the active user: Asymmetrical transfer may produce stable, suboptimal performance. Proceedings of the 26th Annual Conference of the Cognitive Science Society. Hillsdale, NJ: LEA.

2003

• Fu, W.-T. (2003), A Bayesian satisficing model of human adaptive planning. Proceedings of the 25th Annual Conference of the Cognitive Science Society (pp. 420-425). Hillsdale, NJ: LEA.
• Fu, W.-T. (2003), An ACT-R adaptive planner in a simple map- navigation task. In F. Detje, D. Doerner, & H. Schaub (Eds.), Proceedings of the Fifth International Conference on Cognitive Modeling (pp. 99-104). Bamberg, Germany: Universitats-Verlag Bamberg.
• Pirolli, P., Fu, W.-T. (2003), SNIF-ACT: A model of information foraging on the world wide web. Proceedings of the Ninth International Conference on User Modeling. New York: Springer-Verlag. (Winner of Best Theoretical Paper Award).

2002

• Pirolli, P., Fu, W.-T., Reeder, R., Card, S. K. (2002), A user-tracing architecture for modeling interaction with world wide web. Proceedings of the Sixth International Conference on Advanced Visual Interfaces. New York: ACM Press.

2001

• Fu, W.-T. (2001), ACT-PRO action protocol analyzer: A tool for analyzing discrete action protocols. Behavior Research Methods, Instruments, & Computers, 33 (2), 149-158. This article presents a top-down approach for analyzing sequential events in behavioral data. Analysis of behavioral sequential data often entails identifying patterns specified by the researchers. Algorithms were developed and applied to analyze a kind of behavioral data, called discrete action protocol data. Discrete action protocols consist of discrete user actions, such as mouse clicks and keypresses. Unfortunately, the process of analyzing the huge volume of actions (typically, >105) is very labor intensive. To facilitate this process, we developed an action protocol analyzer (ACT-PRO) that provides two levels of pattern matching. Level one uses formal grammars to identify sequential patterns. Level two matches these patterns to a hierarchical structure. ACT-PRO can be used to determine how well data fit the patterns specified by an experimenter. Complementarily, it can be used to focus an experimenter’s attention on data that do not fit the prespecified patterns.
• Fu, W.-T., Gray, W. D. (2001), Modeling cognitive versus perceptual-motor tradeoffs using ACT-R/PM. Proceedings of the Fourth International Conference on Cognitive Modeling (pp. 247-248). Mahwah, NJ: Lawrence Erlbaum Associates.
• Gray, W. D., Fu, W.-T. (2001), Ignoring perfect knowledge in-the- world for imperfect knowledge in-the-head: Implications of rational analysis for interface design. Proceedings of the ACM CHI'01 Conference on Human Factors in Computing Systems. Also in CHI Letters, 3(1).

2000 and before

• Fu, W.-T., Gray, W. D. (2000), Memory versus perceptual-motor tradeoffs in a blocks world task. Proceedings of the 22nd Annual Conference of the Cognitive Science Society, Mahwah, NJ: Erlbaum.
• Gray, W. D., Fu, W.-T. (2000), The influence of source and cost of information access on correct and errorful interactive behavior. Proceedings of the 22nd Annual Conference of the Cognitive Science Society, Mahwah, NJ: Erlbaum.
• Gray, W. D., Schoelles, J. J., Fu, W.-T. (2000), Modeling a continuous dynamic task. Proceedings of the Third International Conference on Cognitive Modeling (pp. 158-168). Groningen, NL: Universal Press.
• Trickett, S. B., Fu, W.-T., Schunn, D. D., Trafton, J. G. (2000), From dipsy-doodles to streaming motions: Changes in representation in the analysis of visual scientific data. Proceedings of the 22nd Annual Conference of the Cognitive Science Society, Mahwah, NJ: Erlbaum.
• Fu, W.-T., Gray, W. D. (1999), Redirecting direct-manipulation, or, what happens when the goal is in front of you but the interface says to turn left. Proceedings of ACM CHI'99 Conference on Human Factors in Computing Systems (pp. 226-227). New York: ACM Press.
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