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      The detection of faked identity using unexpected questions and mouse dynamics.

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          Abstract

          The detection of faked identities is a major problem in security. Current memory-detection techniques cannot be used as they require prior knowledge of the respondent's true identity. Here, we report a novel technique for detecting faked identities based on the use of unexpected questions that may be used to check the respondent identity without any prior autobiographical information. While truth-tellers respond automatically to unexpected questions, liars have to "build" and verify their responses. This lack of automaticity is reflected in the mouse movements used to record the responses as well as in the number of errors. Responses to unexpected questions are compared to responses to expected and control questions (i.e., questions to which a liar also must respond truthfully). Parameters that encode mouse movement were analyzed using machine learning classifiers and the results indicate that the mouse trajectories and errors on unexpected questions efficiently distinguish liars from truth-tellers. Furthermore, we showed that liars may be identified also when they are responding truthfully. Unexpected questions combined with the analysis of mouse movement may efficiently spot participants with faked identities without the need for any prior information on the examinee.

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          Logistic Model Trees

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            Improvements to Platt's SMO Algorithm for SVM Classifier Design

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              MouseTracker: software for studying real-time mental processing using a computer mouse-tracking method.

              In the present article, we present a software package, MouseTracker, that allows researchers to use a computer mouse-tracking method for assessing real-time processing in psychological tasks. By recording the streaming x-, y-coordinates of the computer mouse while participants move the mouse into one of multiple response alternatives, motor dynamics of the hand can reveal the time course of mental processes. MouseTracker provides researchers with fine-grained information about the real-time evolution of participant responses by sampling 60-75 times/sec the online competition between multiple response alternatives. MouseTracker allows researchers to develop and run experiments and subsequently analyze mouse trajectories in a user-interactive, graphics-based environment. Experiments may incorporate images, letter strings, and sounds. Mouse trajectories can be processed, averaged, visualized, and explored, and measures of spatial attraction/curvature, complexity, velocity, and acceleration can be computed. We describe the software and the method, and we provide details on mouse trajectory analysis. We validate the software by demonstrating the accuracy and reliability of its trajectory and reaction time data. The latest version of MouseTracker is freely available at http://mousetracker.jbfreeman.net.
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                Author and article information

                Journal
                PLoS ONE
                PloS one
                Public Library of Science (PLoS)
                1932-6203
                1932-6203
                2017
                : 12
                : 5
                Affiliations
                [1 ] PhD Program in Brain, Mind and Computer Science, University of Padova, Padova, Italy.
                [2 ] University of Padova, Human Inspired Technology Research Centre, Padova, Italy.
                [3 ] University of Padova, Department of General Psychology, Padova, Italy.
                Article
                PONE-D-17-01238
                10.1371/journal.pone.0177851
                5436828
                28542248
                53c4f2c7-b992-437a-9742-615fc3a06b5d
                History

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