Basic Research

Human categorization
In order to understand the cognitive processes underlying how we make sense of information we conducted a number of experiments examining human categorization. While most studies of categorization have focused on concepts defined by simple features, we examined categories based on the relations between features. For example, understanding that water flowing through pipes is in some ways the same as electricity flowing through wires requires putting both into the same category despite water and electricity sharing few surface features -- instead, the key is the relation they share. Through a number of empirical experiments we’ve shown that such relational categories are learned, used, and remembered differently from traditionally studied feature-based concepts. For example, some of the most robust findings in the last thirty years of categorization research -- such as typicality, central tendencies, and family resemblance structures -- appear to be very different in relational categories. We’ve used Bayesian statistical modeling to quantitatively capture these differences and to demonstrate the computational constraints needed to model them. These experiments suggest that we may need to reconsider how we categorize and organize information in the real world.

Human memory modeling
The flip side of encoding information is the ability to retrieve it. Understanding how the human memory system retrieves information could have important implications for computer-based information environments. Starting from a rational statistical analysis of human memory, we developed a simple yet powerful computational model that captures a wide range of human retrieval dynamics. In addition to accounting for many different, sometimes unintuitive memory dynamics, the model can easily be implemented as part of a larger cognitive or computational architecture. Current applications include cognitive modeling for interface design and context-aware retrieval.

Testing social computing theories in artificial communities
One problem with much social computing research is that theories and results tend to be based on correlational data on “dead” datasets with no way of establishing causality.  To truly test theories about what makes communities work we need to do experiments, which are extremely hard or impossible on living communities.  We are building a platform to create mini “artificial” communities, recruiting users from Amazon’s Mechanical Turk to do controlled experimental studies at a massive scale.

Applied Research

Social collaborative systems
Recently the web has seen massive growth in the use of distributed collaboration in developing large knowledge bases, with Wikipedia proving one of the biggest success stories for online collaborative information environments. However, as users and information are added to the system, conflict and coordination costs inevitably grow. Our work combines statistical, machine learning, and visualization techniques to understand and augment these systems. For example, we have examined the growth of conflict and coordination costs in Wikipedia and developed tools to help deal with conflict at the global, article, and user levels. We also characterized the evolution of user influence on Wikipedia and del.icio.us, finding that for both there has been a decline in the power of elite users and a corresponding rise of novice user influence. We continue to investigate how large scale collaborative dynamics can be harnessed for knowledge production, insight, and discovery.

Collaborative ontology building
The rapid growth of medical and neuroscientific data require structured ontologies which can help people find and make use of large data stores. However, traditional bioinformatics approaches to ontology building are insufficient for many domains where rapid advances mean that knowledge is both dynamic and distributed among many sources. Other drawbacks include high participation costs, limited longevity, and adoption issues. In contrast, we are developing a system for ontology building that combines the bazaar-like aspects of emerging collaborative online paradigms such as Wikipedia with cathedral-like support for structured data. The goal of this research is to enable researchers to make robust cross-disciplinary inferences (such as tying together cognitive, behavioral, and neuroimaging data) in a system that brings together knowledge from many researchers while maintaining low participation costs. A prototype of the system can be found at cognitiveatlas.org.

Literature discovery
The recent rapid advancement of science has been accompanied by an explosion in the accompanying scientific literature. Nowadays to make a significant advance it seems necessary to study a single area of science for many years; yet some of the most important discoveries are made by bridging between fields. In order to help people more quickly understand a new field we are developing tools that combine visualization and large scale data mining to help researchers build mental models of information spaces.

Collaborative knowledge mapping
Increasingly specialized knowledge is required to comprehend our complex world. Often, making sense of consumer choice, scientific advancements, and social issues involves frequently changing information, rapid learning, and competing viewpoints. This project aims to design a web-based knowledge building environment for the collaborative creation of knowledge maps. Knowledge maps are interactive visualizations that capture and convey the information, argumentation, and perspective that underlies people's informal theories about the empirical world.

Harnessing crowdsourcing for science and sensemaking
Micro task markets such as Amazon’s Mechanical Turk provide a potential mechanism to improve how we do science and make sense of information.  However, little is known about how to harness this power as of yet.  We are building models of turker behavior based on mining the previous history of mechanical turk tasks, testing these experimentally, and developing a platform for both scientists and general users to use these system for complex, interrelated tasks and experiments.

Publications

Balakrishnan, A., Fussell, S., Kiesler, S., Kittur, A. (2010). Pitfalls of Information Access with Visualizations in Remote Collaborative Analysis. CSCW 2010: Proceedings of the ACM Conference on Computer-Supported Cooperative Work. New York: ACM Press.

Kittur, A., & Kraut, R. E. (2010). Beyond Wikipedia: Conflict and coordination in online production groups. CSCW 2010: Proceedings of the ACM Conference on Computer-Supported Cooperative Work. New York: ACM Press.

Miller, E., Seppa, C., Kittur, A., Sabb, F., Poldrack, R. A. (2010). The Cognitive Atlas: Employing Interaction Design Processes to Facilitate Collaborative Ontology Creation. WWW 2010, Workshop on the Future of the Web for Collaborative Science.

Halfaker, A., Kittur, A., Kraut, R. E., Riedl, J. (2009). Quality, Experience and Ownership in WikiWork. WikiSym 2009: The International Symposium on Wikis and Open Collaboration. New York: ACM Press.

Kittur, A., Pendleton, B., Kraut, R. E. (2009). Herding the Cats: The Influence of Groups in Coordinating Peer Production. WikiSym 2009: The International Symposium on Wikis and Open Collaboration. New York: ACM Press.

Kittur, A., Lee, B., Kraut, R. E. (2009). Coordination in Collective Intelligence: The Role of Team Structure and Task Interdependence. CHI 2009: Proceedings of the ACM Conference on Human-factors in Computing Systems. New York: ACM Press.

Kittur, A., Suh, B., Chi, E. (2009). What's in Wikipedia? Mapping Topics and Conflict Using Collaboratively Annotated Category Links. CHI 2009: Proceedings of the ACM Conference on Human-factors in Computing Systems. New York: ACM Press.

Kittur, A., Chau, D., Hong, J., Faloutsos, C. (2009). Supporting Ad Hoc Sensemaking: Integrating Cognitive, HCI, and Data Mining Approaches. CHI 2009, Workshop on Sensemaking. Boston, MA.

Kittur, A., Kraut, R. E. (2008). Harnessing the Wisdom of Crowds in Wikipedia: Quality Through Coordination. CSCW 2008: Proceedings of the ACM Conference on Computer-Supported Cooperative Work. New York: ACM Press. (Best paper nomination)

Kittur, A., Chi, E., Suh, B. (2008). Can You Ever Trust a Wiki? Impacting Perceived Trustworthiness in Wikipedia. CSCW 2008: Proceedings of the ACM Conference on Computer-Supported Cooperative Work. New York: ACM Press.(Best paper award)

Kittur, A., Chi, E., Suh, B. (2008). Crowdsourcing User Studies With Mechanical Turk. CHI 2007: Proceedings of the ACM Conference on Human-factors in Computing Systems. New York: ACM Press

Suh, B., Chi, E., Kittur, A., Pendleton, B. (2008). Lifting the Veil: Improving Accountability and Social Transparency in Wikipedia with WikiDashboard.CHI 2007: Proceedings of the ACM Conference on Human-factors in Computing Systems. New York: ACM Press

Suh, B., Chi, E., Pendleton, B. A., Kittur, A. (2007). Us vs. Them: Understanding Social Dynamics in Wikipedia with Revert Graph Visualizations. VAST 2007: IEEE Symposium on Visual Analytics Science and Technology.

Kittur, A., Suh, B., Pendleton, B. A., Chi., E. (2007). He Says, She Says: Conflict and Coordination in Wikipedia. CHI 2007: Proceedings of the ACM Conference on Human-factors in Computing Systems. New York: ACM Press

Kittur, A., Chi, E., Pendleton, B. A., Suh, B., Mytkowicz, T. (2007). Power of the Few vs. Wisdom of the Crowd: Wikipedia and the Rise of the Bourgeoisie. Alt.CHI 2007

Kittur, A., Holyoak, K. J., & Hummel, J. E. (2006). Using Ideal Observers in Higher-order Human Category Learning. Proceedings of the Twenty Eighth Annual Meeting of the Cognitive Science Society. Vancouver, CA.

Kittur, A., Hummel, J. E., & Holyoak, K. J. (2006). Ideals Aren’t Always Typical: Dissociating Goodness-of-Exemplar From Typicality Judgments. Proceedings of the Twenty Eighth Annual Meeting of the Cognitive Science Society. Vancouver, CA.

Green, C., & Kittur, A. (2006). Retrieval-Induced Forgetting in a Multiple-Trace Memory Model. Proceedings of the Twenty Eighth Annual Meeting of the Cognitive Science Society. Vancouver, CA.

Kittur, A., Hummel, J.E., & Holyoak, K.J. (2004). Feature- vs. Relation-Defined Categories: Probab(alistic)ly Not the Same. Proceedings of the Twenty Sixth Annual Meeting of the Cognitive Science Society. Chicago, IL.

Green, C., & Kittur, A. (2004). A Multiple-Trace Memory Model Exhibiting Realistic Retrieval Dynamics. Proceedings of the Twenty Sixth Annual Meeting of the Cognitive Science Society. Chicago, IL.

Hummel, J.E., Holyoak, K.J., Green, C., Doumas, L.A.A., Devnich, D., Kittur, A., & Kalar, D.J. (2004). A Solution to the Binding Problem for Compositional Connectionism. In S.D. Levy & R. Gayler: Compositional Connectionism in Cognitive Science: Papers from the AAAI Fall Symposium. Menlo Park, CA: AAAI Press.

Lee, M. K., Borchelt, D. R., Kim, G., Thinakaran, G., Slunt, H. H., Ratovitski, T., Martin, L. J., Kittur, A., Gandy, S., Levey, A. I., Jenkins, N., Copeland, N., Price, D. L., & Sisodia, S. S. (1997). Hyperaccumulation of FAD-linked presenilin 1 variants in vivo. Nature Medicine, 3, 756-60.