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Original Research

Polygraphy and Functional Magnetic Resonance Imaging in Lie Detection: A Controlled Blind Comparison Using the Concealed Information Test

Daniel D. Langleben, MD; Jonathan G. Hakun, PhD; David Seelig, VMD; An-Li Wang, PhD; Kosha Ruparel, MS; Warren B. Bilker, PhD; and Ruben C. Gur, PhD

Published: October 26, 2016

Article Abstract

Objective: Intentional deception is a common act that often has detrimental social, legal, and clinical implications. In the last decade, brain activation patterns associated with deception have been mapped with functional magnetic resonance imaging (fMRI), significantly expanding our theoretical understanding of the phenomenon. However, despite substantial criticism, polygraphy remains the only biological method of lie detection in practical use today. We conducted a blind, prospective, and controlled within-subjects study to compare the accuracy of fMRI and polygraphy in the detection of concealed information. Data were collected between July 2008 and August 2009.

Method: Participants (N = 28) secretly wrote down a number between 3 and 8 on a slip of paper and were questioned about what number they wrote during consecutive and counterbalanced fMRI and polygraphy sessions. The Concealed Information Test (CIT) paradigm was used to evoke deceptive responses about the concealed number. Each participant’s preprocessed fMRI images and 5-channel polygraph data were independently evaluated by 3 fMRI and 3 polygraph experts, who made an independent determination of the number the participant wrote down and concealed.

Results: Using a logistic regression, we found that fMRI experts were 24% more likely (relative risk = 1.24, P < .001) to detect the concealed number than the polygraphy experts. Incidentally, when 2 out of 3 raters in each modality agreed on a number (N = 17), the combined accuracy was 100%.

Conclusions: These data justify further evaluation of fMRI as a potential alternative to polygraphy. The sequential or concurrent use of psychophysiology and neuroimaging in lie detection also deserves new consideration.

Volume: 77

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