Within-individual discrimination on the Concealed Information Test using dynamic mixture modeling

Whether an examinee has information about a crime is determined by the Concealed Information Test based on autonomic differences between the crime-related item and other control items. Multivariate quantitative statistical methods have been proposed for this determination. However, these require specific databases of responses, which are problematic for field application. Alternative methods, using only an individual's data, are preferable, but traditionally such within-individual approaches have limitations because of small data sample size. The present study proposes a new within-individual judgment method, the hidden Markov discrimination method, in which time series-data are modeled with dynamic mixture distributions. This method was applied to experimental data and showed sufficient potential in discriminating guilty from innocent examinees in a mock theft experiment compared with performance of previous methods.