Computational Models for Representation Change in Human Learning

Jennifer Roberts

Changes in representation allow us to model how the world works, organize information into comprehensible units, and identify pertinent information so that we can ignore irrelevant details. To develop models of human learning that account for representation change, we must first identify classes of representation change commonly encountered during learning and development, analyze how mechanisms for representation change might interact with other mental processes, and identify cognitive constraints that might lead to representation change. In this paper, I identify representation changes observed during human learning and development, suggest a working model of how representation change mechanisms might interact with other cognitive subsystems, and suggest computational mechanisms that might account for the types of representation change observed in humans.

Subjects: 4. Cognitive Modeling; 12. Machine Learning and Discovery

Submitted: Sep 13, 2007