Disclosing concealed information on the basis of cortical activations

Nose, Izuru; Murai, Junichiro; Taira, Masato

doi:10.1016/j.neuroimage.2008.11.002

Cited by 46 publications

(35 citation statements)

References 34 publications

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“…To estimate the criterion validity of the CIT with reaction times, we rely on a total of nine studies that included a group of participants knowledgeable of crime details and a group of unknowledgeable participants, and reported data from which a could be derived (Hu, Finally, only four studies assessed the criterion validity of the fMRI-based CIT, using both crime-knowledgeable and unknowledgeable participants (Cui et al, 2013;Ganis, Rosenfeld, Meixner, Kievit, & Schendan, 2011;Nose et al, 2009;Peth et al, 2015). Collectively, these studies yield a weighted average a value of .94.…”

Section: Citmentioning

confidence: 99%

Deception detection with behavioral, autonomic, and neural measures: Conceptual and methodological considerations that warrant modesty

et al. 2016

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The detection of deception has attracted increased attention among psychological researchers, legal scholars, and ethicists during the last decade. Much of this has been driven by the possibility of using neuroimaging techniques for lie detection. Yet, neuroimaging studies addressing deception detection are clouded by lack of conceptual clarity and a host of methodological problems that are not unique to neuroimaging. We review the various research paradigms and the dependent measures that have been adopted to study deception and its detection. In doing so, we differentiate between basic research designed to shed light on the neurocognitive mechanisms underlying deceptive behavior and applied research aimed at detecting lies. We also stress the distinction between paradigms attempting to detect deception directly and those attempting to establish involvement by detecting crime-related knowledge, and discuss the methodological difficulties and threats to validity associated with each paradigm. Our conclusion is that the main challenge of future research is to find paradigms that can isolate cognitive factors associated with deception, rather than the discovery of a unique (brain) correlate of lying. We argue that the Comparison Question Test currently applied in many countries has weak scientific validity, which cannot be remedied by using neuroimaging measures. Other paradigms are promising, but the absence of data from ecologically valid studies poses a challenge for legal admissibility of their outcomes.

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Section: Citmentioning

confidence: 99%

Deception detection with behavioral, autonomic, and neural measures: Conceptual and methodological considerations that warrant modesty

et al. 2016

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“…In contrast, classification with feature selection was consistently between 65 and 80%. Nose, Murai, and Taira (2009) conducted a study using a concealed information test paradigm in which participants were asked to hide having seen a card (probe) they selected before the study. Note that even in this case participants are lying, as they are attempting to convince the experimenter that they do not have knowledge about the probe.…”

Section: Accuracy Of Fmri-based Deception Detectionmentioning

confidence: 99%

Deception Detection Using Neuroimaging

Ganis¹

2014

Detecting Deception

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“…Of the minority of studies reporting statistics that are directly relevant to assessing accuracy at the level of individual subjects or individual events 2,7,8,16,17,[19][20][21][22][23][24][25][26][27] , only two studies (to our knowledge) report data relevant to detecting deception at the individual event level. Specifically, using the same dataset, Langleben et al 16 and Davatzikos et al 17 focused on whether instructed lie and truth events could be discriminated, using either logistic regression16 or non-linear machine learning analyses 17 .…”

Section: Translational Challenges: From the Laboratory To The Real Womentioning

confidence: 99%

“…A variety of statistical approaches have been implemented, including single-subject univariate analyses 7,19,20,21 , univiariate analyses combined with the counting of above-threshold voxels in targeted regions of interest 8,22,25 , and machine learning classification. 2 The reported accuracies in these individual-subject level analyses have ranged from 69-100%, suggesting promise.…”

Section: Translational Challenges: From the Laboratory To The Real Womentioning

confidence: 99%

Erratum: The brain reward circuitry in mood disorders

Russo¹,

Nestler²

2013

Nat Rev Neurosci

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Functional MRI (fMRI)-based lie detection has been marketed as a tool for enhancing personnel selection, strengthening national security and protecting personal reputations, and at least three US courts have been asked to admit the results of lie detection scans as evidence during trials. How well does fMRI-based lie detection perform, and how should the courts, and society more generally, respond? Here, we address various questions -some of which are based on a meta-analysis of published studies -concerning the scientific state of the art in fMRI-based lie detection and its legal status, and discuss broader ethical and societal implications. We close with three general policy recommendations.

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Disclosing concealed information on the basis of cortical activations

Cited by 46 publications

References 34 publications

Deception detection with behavioral, autonomic, and neural measures: Conceptual and methodological considerations that warrant modesty

Deception detection with behavioral, autonomic, and neural measures: Conceptual and methodological considerations that warrant modesty

Deception Detection Using Neuroimaging

Erratum: The brain reward circuitry in mood disorders

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