Supporting efficient development of cognitive models at multiple skill levels: exploring recent advances in constraint-based modeling

  • Authors:
  • Irene Tollinger;Richard L. Lewis;Michael McCurdy;Preston Tollinger;Alonso Vera;Andrew Howes;Laura Pelton

  • Affiliations:
  • NASA Ames Research Center, Moffett Field, CA;University of Michigan, Ann Arbor, MI;NASA Ames Research Center, Moffett Field, CA;NASA Ames Research Center, Moffett Field, CA;NASA Ames Research Center, Moffett Field, CA;Cardiff University, Cardiff, UK;NASA Ames Research Center, Moffett Field, CA

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2005

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Abstract

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 [7,13,21]. 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.