Literature DB >> 21689693

Improved method for calculating the respiratory line length in the Concealed Information Test.

Izumi Matsuda1, Tokihiro Ogawa.   

Abstract

The Concealed Information Test (CIT) assesses an examinee's knowledge about a crime based on response differences between crime-relevant and crime-irrelevant items. One effective measure in the CIT is the respiration line length, which is the average of the moving distances of the respiration curve in a specified time interval after the item onset. However, the moving distance differs between parts of a respiratory cycle. As a result, the calculated respiration line length is biased by how the parts of the respiratory cycles are included in the time interval. To resolve this problem, we propose a weighted average method, which calculates the respiration line length per cycle and weights it with the proportion that the cycle occupies in the time interval. Simulation results indicated that the weighted average method removes the bias of respiration line lengths compared to the original method. The results of experimental CIT data demonstrated that the weighted average method significantly increased the discrimination performance as compared with the original method. The weighted average method is a promising method for assessing respiration changes in response to question items more accurately, which improves the respiration-based discrimination performance of the CIT.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 21689693     DOI: 10.1016/j.ijpsycho.2011.06.002

Source DB:  PubMed          Journal:  Int J Psychophysiol        ISSN: 0167-8760            Impact factor:   2.997


  2 in total

1.  The current and future status of the concealed information test for field use.

Authors:  Izumi Matsuda; Hiroshi Nittono; John J B Allen
Journal:  Front Psychol       Date:  2012-11-27

2.  A linear model for event-related respiration responses.

Authors:  Dominik R Bach; Samuel Gerster; Athina Tzovara; Giuseppe Castegnetti
Journal:  J Neurosci Methods       Date:  2016-06-03       Impact factor: 2.390

  2 in total

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