ArticlePloS one2017
The detection of faked identity using unexpected questions and mouse dynamics.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed.
- Unanticipated questions can yield unanticipated outcomes in investigative interviews.PloS one · 2018Trial
- The trajectory of crime: Integrating mouse-tracking into concealed memory detection.Behavior research methods · 2025Article
- Unraveling the neural basis of repeated moral decisions with mouse tracking and fMRI.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Detecting lies in investigative interviews through the analysis of response latencies and error rates to unexpected questions.Scientific reports · 2024Article
- Do privacy assurances work? a study of truthfulness in healthcare history data collection.PloS one · 2022Article
- Micro-Behavioral Accidental Click Detection System for Preventing Slip-Based Human Error.Sensors (Basel, Switzerland) · 2021Article
- Detecting faking-good response style in personality questionnaires with four choice alternatives.Psychological research · 2021Article
- The detection of faked identity using unexpected questions and choice reaction times.Psychological research · 2021Article
- The detection of malingering in whiplash-related injuries: a targeted literature review of the available strategies.International journal of legal medicine · 2021Review
- Assessing the impact of previous experience on lie effects through a transfer paradigm.Scientific reports · 2021Article
- Brain-Machine Interfaces as Commodities: Exchanging Mind for Matter.The Linacre quarterly · 2020Article
- Knowledge of Depression and Malingering: An Exploratory Investigation.Europe's journal of psychology · 2020Article
- The Detection of Malingered Amnesia: An Approach Involving Multiple Strategies in a Mock Crime.Frontiers in psychiatry · 2019Article
- Population-scale hand tremor analysis via anonymized mouse cursor signals.NPJ digital medicine · 2019Article
- MMPI-2-RF Profiles in Child Custody Litigants.Frontiers in psychiatry · 2019Article
- A State Space Approach to Dynamic Modeling of Mouse-Tracking Data.Frontiers in psychology · 2019Article
- Introducing Machine Learning to Detect Personality Faking-Good in a Male Sample: A New Model Based on Minnesota Multiphasic Personality Inventory-2 Restructured Form Scales and Reaction Times.Frontiers in psychiatry · 2019Article
- Covert lie detection using keyboard dynamics.Scientific reports · 2018Article
- A Review of Approaches to Detecting Malingering in Forensic Contexts and Promising Cognitive Load-Inducing Lie Detection Techniques.Frontiers in psychiatry · 2018Review
- The Detection of Malingering: A New Tool to Identify Made-Up Depression.Frontiers in psychiatry · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.