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Algorithms for detecting concealed knowledge among groups when the critical information is unavailable.

Assaf Breska¹, Gershon Ben-Shakhar, Nurit Gronau

¹Department of Psychology, Hebrew University of Jerusalem, Israel, Mount Scopus, Jerusalem 91905, Israel.

Journal of Experimental Psychology. Applied

|June 20, 2012

Summary

The Searching Concealed Information Test (SCIT) effectively identifies critical details even when investigators lack information. This method shows promise for criminal investigations and security threat detection.

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Area of Science:

Psychology
Forensic Science
Cognitive Science

Background:

The Concealed Information Test (CIT) is a polygraph technique used to detect knowledge of specific details.
Investigative limitations often prevent the standard CIT, necessitating methods for situations with unknown critical details.

Purpose of the Study:

To evaluate the feasibility and effectiveness of the Searching Concealed Information Test (SCIT) when critical details are unknown to investigators.
To assess two classes of algorithms for detecting critical items and classifying individuals within the SCIT framework.

Main Methods:

Two algorithmic approaches were examined: one based on response averaging across subjects and critical items, and another using clustering of response profiles.
Principal component analysis was employed to identify key components for subject classification.
The performance was evaluated using receiver operating characteristic (ROC) curves on reanalyzed data from three prior CIT studies.

Main Results:

Both algorithmic approaches demonstrated effectiveness in differentiating knowledgeable from unknowledgeable subjects in the SCIT.
The SCIT's differentiation efficiency approached that of the standard CIT across both algorithms.
Algorithm performance proved relatively robust to variations in the number of knowledgeable and unknowledgeable subjects, with at least 2-5 knowledgeable examinees being optimal.

Conclusions:

The Searching Concealed Information Test (SCIT) is a viable method for detecting concealed information when critical details are unknown.
The developed algorithms show promise for applications in criminal investigations and security threat detection.
The SCIT's reliability is maintained across different sample compositions, highlighting its practical utility.