Learning Predictive Models of Memory Landmarks

Eric Horvitz, Susan Dumais, Paul Koch

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We describe the construction of statistical models that provide inferences about the probability that subjects will consider events to be memory landmarks. We review methods and report results of experiments probing the classification accuracy and receiver-operator characteristics of the models. Then, we discuss opportunities for integrating models of memory landmarks into computing applications, and present a prototype time-line oriented content-browsing tool.

Keywords: Models of memory, Bayesian reasoning, timelines.

In: E. Horvitz, S. Dumais, P. Koch. Learning Predictive Models of Memory Landmarks, CogSci 2004: 26th Annual Meeting of the Cognitive Science Society, Chicago, August 2004.

Author Email: horvitz@microsoft.com

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