@inbook{1e5e89d1d91848e59844a8da2b204edd,
title = "Testing various modes of computer-based training for deception detection",
abstract = "People are not very good at detecting deception in normal communication, and past efforts to train them to become better detectors have been mixed. This paper reports on a training study involving 177 military officers. In addition to developing and testing a training program to improve deception detection, this study also aimed to test different versions of a computer-based training system. Participants received training on deception generally and on specific indicators. The training program was delivered via a videotaped lecture or via one of four versions of Agent99, the computer-based training system. Participants completed pre- and post-tests on their knowledge of deception and on their ability to detect it. Detection accuracy was measured by asking participants to judge if behavior in video, audio and text examples was deceptive or honest. Agent99 users and video lecture viewers all improved their knowledge of deception and their ability to detect it.",
author = "George, \{Joey F.\} and Biros, \{David P.\} and Mark Adkins and Burgoon, \{Judee K.\} and Nunamaker, \{Jay F.\}",
year = "2004",
doi = "10.1007/978-3-540-25952-7\_31",
language = "English (US)",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "411--417",
editor = "Hsinchun Chen and Zeng, \{Daniel D.\} and Reagan Moore and John Leavitt",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}