Hierarchical Bayesian Modeling of Autonomic Responses during the Concealed Information Test

The autonomic-based concealed information test continuously measures physiological indices such as skin conductance, normalized pulse volume, heart rate, and respiration. This study proposes a hierarchical Bayesian model to analyze these physiological responses’ time series data. The proposed approa...

Full description

Saved in:
Bibliographic Details
Published inJapanese Journal of Physiological Psychology and Psychophysiology Vol. 41; no. 2; pp. 202 - 210
Main Authors SHIBUYA, YUSUKE, TSUNEOKA, MICHIKO, TAKAHASHI, REO, OGAWA, TOKIHIRO
Format Journal Article
LanguageJapanese
Published Japanese Society for Physiological Psychology and Psychophysiology 31.12.2023
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:The autonomic-based concealed information test continuously measures physiological indices such as skin conductance, normalized pulse volume, heart rate, and respiration. This study proposes a hierarchical Bayesian model to analyze these physiological responses’ time series data. The proposed approach uses a state-space model that comprises observation equations with truncated normal distributions and state equations with hierarchical structures. We applied the proposed method to experimental data from 167 participants to illustrate the technique’s efficacy. This method allows us to visualize the magnitude of typical physiological responses in their respective units of measurement and individual differences, which had previously been overlooked in conventional analysis that requires standardization of observed data. Furthermore, this hierarchical state-space model can be easily adapted to various experimental designs.
ISSN:0289-2405
2185-551X
DOI:10.5674/jjppp.2309tn