ArticleFrontiers in psychology2018
False Identity Detection Using Complex Sentences.
Article in Frontiers in psychology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
9 citing papers in PubMed.
- Detecting lies in investigative interviews through the analysis of response latencies and error rates to unexpected questions.Scientific reports · 2024Article
- The detection of malingering in whiplash-related injuries: a targeted literature review of the available strategies.International journal of legal medicine · 2021Review
- Knowledge of Depression and Malingering: An Exploratory Investigation.Europe's journal of psychology · 2020Article
- Observational
- Malingering Detection of Cognitive Impairment With the b Test Is Boosted Using Machine Learning.Frontiers in psychology · 2019Article
- The Detection of Malingered Amnesia: An Approach Involving Multiple Strategies in a Mock Crime.Frontiers in psychiatry · 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
- The Detection of Malingering: A New Tool to Identify Made-Up Depression.Frontiers in psychiatry · 2018Article
- A Review of Approaches to Detecting Malingering in Forensic Contexts and Promising Cognitive Load-Inducing Lie Detection Techniques.Frontiers in psychiatry · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of faked identities is a current issue for both physical and online security. In this paper, we test the differences between subjects who report their true identity and the ones who give fake identity responding to control, simple, and complex questions. Asking complex questions is a new procedure for increasing liars' cognitive load, which is presented in this paper for the first time. The experiment consisted in an identity verification task, during which response time and errors were collected. Twenty participants were instructed to lie about their identity, whereas the other 20 were asked to respond truthfully. Different machine learning (ML) models were trained, reaching an accuracy level around 90-95% in distinguishing liars from truth tellers based on error rate and response time. Then, to evaluate the generalization and replicability of these models, a new sample of 10 participants were tested and classified, obtaining an accuracy between 80 and 90%. In short, results indicate that liars may be efficiently distinguished from truth tellers on the basis of their response times and errors to complex questions, with an adequate generalization accuracy of the classification models.
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What OpenQuestion holds
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.