Association-based Concealed Information Test: A Novel Reaction Time-Based Deception Detection Method

Lukács, Gáspár; Gula, Bartosz; Szegedi-Hallgató, Emese; Csifcsák, Gábor

doi:10.31234/osf.io/sqkua

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…The necessity of, and suggestions for, examining the generalizability of fillers have been mooted several times (e.g., already by Lukács, Gula, et al, 2017). But, to date, all studies except one have only used autobiographical items when examining the E-CIT.…”

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confidence: 99%

Importance-related fillers improve the classification accuracy of the Response Time Concealed Information Test in a crime scenario

Wojciechowski¹,

Lukács²

2021

Preprint

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Purpose. The Response Time Concealed Information Test (RT-CIT) can reveal when a person recognizes a relevant item among other irrelevant items, based on comparatively slower responding. Therefore, if a person is concealing knowledge about the relevance of this item (e.g., recognizing it as a murder weapon), this deception can be revealed. A recent study introduced additional “familiarity-related fillers”, and these items substantially enhanced diagnostic efficiency in detecting autobiographical data. However, the generalizability of the efficiency of fillers to other scenarios remains an open question. We empirically investigated whether new importance-related fillers enhanced diagnostic efficiency in an imaginary crime scenario. Methods. Two hundred and thirty-nine volunteers participated in an independent samples experiment. Participants were asked to imagine either committing a crime (“guilty” group) or to imagine visiting a museum (“innocent” group). Then, all participants underwent RT-CIT testing using either a standard single probe or an enhanced single probe (with importance-related fillers) protocol.Results. The enhanced RT-CIT (with importance-related fillers) showed high diagnostic efficiency (AUC = .810), and significantly outperformed the standard version (AUC = .562). Neither dropout rates nor exclusion criteria influenced this enhancement.Conclusions. Importance-related fillers improve diagnostic efficiency when detecting episodic information using the RT-CIT, and seem to be useful in detecting knowledge in a wide range of scenarios.

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confidence: 99%

Importance-related fillers improve the classification accuracy of the Response Time Concealed Information Test in a crime scenario

Wojciechowski¹,

Lukács²

2021

Preprint

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“…In [12], genetic SVM as classifier has been applied to identify guilty subject using a novel CIT method. The author in [13] also proposed a novel association-based CIT having similarities with reaction time-CIT, which considers reaction time differences between irrelevant and probe stimuli.…”

Section: Machine Learning Approachmentioning

confidence: 99%

“…Ten subjects, each subjects' 16-channel EEG data, have been recorded by conducting a CIT. Analysis of variance (ANOVA) is the most commonly used statistical technique for CIT-based studies such as [5,6,13]. In this work, ANOVA has been applied on subject-wise EEG data for single trial and it has been observed that means of two groups or two classes overlaps.…”

Section: Classificationmentioning

confidence: 99%

An efficient Concealed Information Test: EEG feature extraction and ensemble classification for lie identification

Bablani

Edla

Tripathi³

et al. 2018

Machine Vision and Applications

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EEG-based lie detectors have become popular over polygraphs because it cannot be controlled by human intentions. Various studies have performed "Guilty Knowledge Test" or "Concealed Information Test" by creating a mock crime scenario to identify changes in brain potential. In this study, an individual's behavior during lying is analyzed and a new scenario is developed for "Concealed Information Test." This work involves a mock crime scenario using an EEG acquisition device for 10 participants. Data acquisition has been performed by placing 16 electrodes on the subjects' scalp. For this experiment, the subject has to recognize faces of some known and unknown personalities among 10 images flashed. These images behave as stimulus for the subject which generate corresponding brain responses. Various feature extraction approaches such as statistical, time domain, frequency domain and time-frequency domain are applied to the 16-channel EEG data. For classifying a subject as guilty or innocent, five classifiers have been applied on subject-wise EEG data. Moreover, the classifiers' ranking is considered based on the performance of classifiers. An ensemble framework is developed by aggregating the results of the best three classifiers out of the tested five classifiers. The classifiers' results are aggregated using a weighted voting approach and have been compared with popular conventional approaches using various classification performance measures. Results present a comparative performance of different feature extraction approaches and classifiers using subjectwise single-trial EEG data. The wavelet approach performs better for EEG data of most of the subjects. A comparison between base classifiers and ensemble framework is provided with the ensemble approach outperforming over the base classifiers. Further proposed framework is compared with some existing approaches, and a highest accuracy of 92.4% has been achieved.

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Association-based Concealed Information Test: A Novel Reaction Time-Based Deception Detection Method

Cited by 2 publications

References 29 publications

Importance-related fillers improve the classification accuracy of the Response Time Concealed Information Test in a crime scenario

Importance-related fillers improve the classification accuracy of the Response Time Concealed Information Test in a crime scenario

An efficient Concealed Information Test: EEG feature extraction and ensemble classification for lie identification

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