Gregory Trafton, Ph.D.

Affiliations: 
Psychology George Mason University, Washington, DC 
Google:
"http://www.nrl.navy.mil/aic/iss/aas/poc-trafton.php"

Parents

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Phil Johnson-Laird grad student George Mason (Neurotree)
Brian J. Reiser grad student Princeton

Children

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Kevin Zish grad student 2012- George Mason
Susan B. Trickett grad student 2004 George Mason
Raj M. Ratwani grad student 2008 George Mason
Esube T Bekele post-doc 2016- Naval Research Laboratory (E-Tree)
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Publications

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Khemlani SS, Harrison AM, Trafton JG. (2015) Episodes, events, and models. Frontiers in Human Neuroscience. 9: 590
Khemlani S, Lotstein M, Trafton JG, et al. (2015) Immediate inferences from quantified assertions. Quarterly Journal of Experimental Psychology (2006). 1-24
Trafton JG, Harrison AM, Lawson WE. (2015) Using spatial representations in gesture to facilitate early word learning: A priming process model 5th Joint International Conference On Development and Learning and Epigenetic Robotics, Icdl-Epirob 2015. 70-77
Kirschenbaum SS, Trafton JG, Schunn CD, et al. (2014) Visualizing uncertainty: the impact on performance. Human Factors. 56: 509-20
Altmann EM, Trafton JG, Hambrick DZ. (2014) Momentary interruptions can derail the train of thought. Journal of Experimental Psychology. General. 143: 215-26
Zish K, Trafton JG. (2014) Interruption practice reduces procedural errors at the post-completion step Proceedings of the Human Factors and Ergonomics Society. 2014: 265-269
Baxter P, Trafton G. (2014) Cognitive architectures for Human-Robot Interaction Acm/Ieee International Conference On Human-Robot Interaction. 504-505
Moshkina L, Trickett S, Trafton JG. (2014) Social engagement in public places: A tale of one robot Acm/Ieee International Conference On Human-Robot Interaction. 382-389
Trafton JG, Ratwani RM. (2014) The law of unintended consequences: The case of external subgoal support Conference On Human Factors in Computing Systems - Proceedings. 1767-1776
Breslow LA, Gartenberg D, McCurry JM, et al. (2014) Dynamic operator overload: A model for predicting workload during supervisory control Ieee Transactions On Human-Machine Systems. 44: 30-40
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