Mechanisms of implicit learning: connectionist models of sequence processing
Mechanisms of implicit learning: connectionist models of sequence processing
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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FAC '09 Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009
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We discuss the problem of assessing and aiding user performance in dynamic tasks that require rapid selection among multiple information sources. Motivated by research in human sequential learning, we develop a system that learns by observation to predict the information a user desires in different contexts. The model decides when the display should be updated, which is akin to the problem of scene segmentation, and then selects the situationally relevant information display. The model reduces the cognitive burden of selecting situation-relevant displays. We evaluate the system in a tank video game environment and find that the system boosts user performance. The fit of the model to user data provides a quantitative assessment of user behavior, which is useful in assessing individual differences and the progression from novice- to expert-level proficiency. We discuss the relative benefits of adopting a learning approach to predicting information preferences and possible avenues to reduce the negative consequences of automation.