Generating automated predictions of behavior strategically adapted to specific performance objectives

Authors:
Katherine Eng;Richard L. Lewis;Irene Tollinger;Alina Chu;Andrew Howes;Alonso Vera
Affiliations:
NASA Ames Research Center, Moffett Field, CA;University of Michigan, Ann Arbor, MI;NASA Ames Research Center, Moffett Field, CA;University of Michigan, Ann Arbor, MI;Manchester University, Manchester, UK;NASA Ames Research Center, Moffett Field, CA
Venue:
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Year:
2006

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Abstract

It has been well established in Cognitive Psychology that humans are able to strategically adapt performance, even highly skilled performance, to meet explicit task goals such as being accurate (rather than fast). This paper describes a new capability for generating multiple human performance predictions from a single task specification as a function of different performance objective functions. As a demonstration of this capability, the Cognitive Constraint Modeling approach was used to develop models for several tasks across two interfaces from the aviation domain. Performance objectives are explicitly declared as part of the model, and the CORE (Constraint-based Optimal Reasoning Engine) architecture itself formally derives the detailed strategies that are maximally adapted to these objectives. The models are analyzed for emergent strategic variation, comparing those optimized for task time with those optimized for working memory load. The approach has potential application in user interface and procedure design.