Ames HCI Group

Human Performance Modeling

CCM can be used to support reasoning about human-computer interaction in both human factors and cognitive science. CCM supports the formal reification of the constraints underlying human performance, and the derivation of the implications of these constraints. These two properties make it a powerful approach for describing and reasoning about psychological theory.

The human factor in gadget, Web design (cNET)

"Design is starting to change who succeeds and who fails," said Alonso Vera, a senior research scientist at NASA Ames Research Center who's also a senior systems scientist at Carnegie Mellon University.

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Ask Dr. Usability (ACM Interactions)

For an example of using cognitive modeling in combination with other methods, see the CHI2007 Experience report, "When Two Methods Are Better Than One: Combining User Study with Cognitive Modeling" by Andrea Knight; you can find that one in the ACM digital library.

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Breaking the Fidelity Barrier: An Examination of Our Current Characterization of Prototypes and an Example of a Mixed-Fidelity Success

McCurdy, M., Connors, C., Pyrzak, G., Kanefsky, B., & Vera, A. (2006). Breaking the fidelity barrier: An examination of current prototypes and an example of a mixed-fidelity success. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.

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]]> This paper presents a summary of the space of commonly-used HCI prototyping methods (low-fidelity to high-fidelity) and asserts that with a better understanding of this space, HCI practitioners will be better equipped to direct scarce prototyping resources toward an effort likely to yield specific results. It presents a set of five dimensions along which prototypes can be planned and characterized. The paper then describes an analysis of this space performed by members of the NASA Ames Human-Computer Interaction Group when considering prototyping approaches for a new set of tools for Mars mission planning and scheduling tools. A description is presented of a prototype that demonstrates design solutions that would have been particularly difficult to test given conventional low- or mid- fidelity prototyping methods. The prototype created was "mixed-fidelity," that is, high-fidelity on some dimensions and low-fidelity on others. The prototype is compared to a preexisting tool being redesigned and to a tool that has been developed using the prototype. Experimental data are presented that show the prototype to be a good predictor of eventual user performance with the final application. Given the relative cost of developing prototypes, it is critical to better characterize the space of fidelity in order to more precisely allocate design and development resources.

Jesse Clark

Research Computer Scientist -- NASA

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Research and Applied Work

(bio)

NASA Artificial Intelligence Could Help Astronauts Work More Efficiently in Space (NASA Feature)

"The Mars Exploration Rover activity planning has taken the human team on Earth about one-and-a-half hours every day for each of the two rovers, " said Alonso Vera, who leads a group developing artificial intelligence software at NASA Ames Research Center, located in California's Silicon Valley. "Our goal is to reduce planning time to about 10 minutes for a typical Mars surface mission, and we are a good part of the way there now," he ventured.

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Supporting Efficient Development of Cognitive Models at Multiple Skill Levels: Exploring Recent Advances in Constraint-based Modeling

Tollinger, I., Lewis, R.L., McCurdy, M., Tollinger, P., Vera, A.H., Howes, A., Pelton, L. (2005). Supporting efficient development of cognitive models at multiple skill levels: exploring recent advances in constraint-based modeling. Proceedings of the SIGCHI conference on Human Factors in Computing Systems, 411-420.

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]]> This paper presents X-PRT, a new cognitive modeling tool supporting activities ranging from interface design to basic cognitive research. X-PRT provides a graphical model development environment for the CORE constraint-based cognitive modeling engine. X-PRT comprises a novel feature set: (a) it supports the automatic generation of predictive models at multiple skill levels from a single task- specification, (b) it supports a comprehensive set of modeling activities, and (c) it supports compositional reuse of existing cognitive/perceptual/motor skills by transforming high-level, hierarchical task descriptions into detailed performance predictions. Task hierarchies play a central role in X-PRT, serving as the organizing construct for task knowledge, the locus for compositionality, and the cognitive structures over which the learning theory is predicated. Empirical evidence supports the role of task hierarchies in routine skill acquisition.

A Constraint-based Approach to Understanding the Composition of Skill

Lewis, R.L., Howes, A., Vera, A. (2004). A constraint-based approach to understanding the composition of skill. International Conference on Cognitive Modeling, Pittsburgh, 2004.

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]]> A hallmark of human cognition is the ability to compose novel behaviors from an existing repertoire of skills (Newell, 1990). These compositional processes range from search-based problem solving to the rapid, smoothly meshed perceptual-motor coordinations of well-practiced device interaction. In this paper we describe an approach to partially automating the composition of both semi-routine and highly skilled interactive behaviors. This approach, called Cognitive Constraint Modeling (CCM), is characterized by three principles: (a) descriptions of behavior are derived via constraint satisfaction over explicitly declared architectural, task, and strategy constraints; (b) the details of behavioral control (and therefore behavior composition) emerge in part from optimizing behavior with respect to objective functions intended to capture general strategic goals (e.g., go as fast as possible); and (c) the architectural building blocks are based on a simple ontology of resource-constrained cascaded processes. We show that these three principles jointly support modeling two important aspects of an interactive task: the overlapping and anticipatory behavior of highly skilled performance, and the hierarchical control of behavior evident earlier in practice. We contrast this approach with complementary approaches based on modeling the procedural learning processes themselves.

A Constraint Satisfaction Approach to Predicting Skilled Interactive Cognition

Vera, A.H., Howes, A., McCurdy, M., Lewis, R.L., (2004) A constraint satisfaction approach to predicting skilled interactive cognition. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 121-128.

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]]> In this paper we report a new approach to generating predictions about skilled interactive cognition. The approach, which we call Cognitive Constraint Modeling, takes as input a description of the constraints on a task environment, on user strategies, and on the human cognitive architecture and generates as output a prediction of the time course of interaction. In the Cognitive Constraint Models that we have built this is achieved by encoding the assumptions inherent in CPM-GOMS as a set of constraints and reasoning about them using finite domain constraint satisfaction.

James Kurien, Ph.D.

Research Scientist -- NASA

Dr. Kurien leads the Ensemble development team at NASA Ames.

Bob Kanefsky

... -- NASA

Activity Planning for the Phoenix Mars Lander Mission

Fox, J.M. & McCurdy, M. (2006). Activity planning for the Phoenix Mars Lander mission. IEEEAC 2006.

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]]> The Phoenix Mars Mission is the first mission in the NASA Scout Program. As a Scout Mission, Phoenix must be relatively low-cost and yet highly innovative. Likewise, the Ground Data System (GDS) is under similar constraints. Key to the GDS tool development has been the utilization of the Ensemble framework – an open architecture for the development, integration, and deployment of mission operations software. Ensemble has enabled the production of a low-cost, feature-rich tool to support tactical mission activity planning called The Phoenix Science Interface (PSI).

Andrew Bachmann

... -- ...

Alfredo Bencomo

... -- ...

Jessica Nowinski, Ph.D.

Research Psychologist -- NASA

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Ph.D. in Cognitive Psychology, Stanford University, 1999
A.B. in Psychology, Barnard College, Columbia University, 1992

Research and Applied Work

Jessica Nowinksi is a Research Psychologist in the Human-Computer Interaction Group at NASA Ames Research Center. She received her undergraduate degree in Psychology from Barnard College at Columbia University (1992). She earned her Ph.D. (2004) in Cognitive Psychology at Stanford University (1999). While at Stanford Dr. Nowinski worked with Gordon Bower on issues of bias and interference in memory, and conducted her dissertation research on possible inhibition mechanisms related to forgetting. In 1999 she came to NASA Ames as an NRC post-doc to work with Key Dismukes researching theoretical and practical aspects of prospective memory (memory for intentions), especially as they relate to cockpit and ATC operations. She is currently a contributor to the software design and requirements development efforts in the HCI Group.

Recent Papers

R. K. Dismukes, & J. L. Nowinski (2006) Prospective Memory, concurrent task management, and pilot error, In A. Kramer, D. Wiegmann, & A. Kirlik (Eds.) Attention: From Theory to Practice. New York: Oxford University Press.

R. K. Dismukes, & J. L. Nowinski (2005) Effects of ongoing task context and target typicality on prospective memory performance: The importance of associative cueing, Memory, 13(6), 649-657.

J. L. Nowinski, J. B. Holbrook, & R. K. Dismukes (2003) Human memory and cockpit operations: An ASRS study, In Proceedings of the 12th International Symposium on Aviation Psychology (pp. 888-893). Dayton, OH: The Wright State University.