Breakthrough percepts of famous names.

Concealed knowledge test EEG Famous names Fringe-P3 Rapid serial visual presentation

Journal

Cortex; a journal devoted to the study of the nervous system and behavior
ISSN: 1973-8102
Titre abrégé: Cortex
Pays: Italy
ID NLM: 0100725

Informations de publication

Date de publication:
06 2021
Historique:
received: 26 08 2020
revised: 13 12 2020
accepted: 04 02 2021
pubmed: 1 5 2021
medline: 13 7 2021
entrez: 30 4 2021
Statut: ppublish

Résumé

Studies have shown that presenting own-name stimuli on the fringe of awareness in Rapid Serial Visual Presentation (RSVP) generates a P3 component and provides an accurate and countermeasure resistant method for detecting identity deception (Bowman et al., 2013, 2014). The current study investigates how effective this Fringe-P3 method is at detecting recognition of familiar name stimuli with lower salience (i.e., famous names) than own-name stimuli, as well as its accuracy with multi-item stimuli (i.e., first and second name pairs presented sequentially). The results demonstrated a highly significant ERP difference between famous and non-famous names at the group level and a detectable P3 for famous names for 86% of participants at the individual level. This demonstrates that the Fringe-P3 method can be used for detecting name stimuli other than own-names and for multi-item stimuli, thus further supporting the method's potential usefulness in forensic applications such as in detecting recognition of accomplices.

Identifiants

pubmed: 33930660
pii: S0010-9452(21)00105-2
doi: 10.1016/j.cortex.2021.02.030
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

267-281

Informations de copyright

Copyright © 2021 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest None.

Auteurs

Abdulmajeed Alsufyani (A)

Department of Computer Science, Taif University, Taif, Saudi Arabia.

Kathryn Harris (K)

School of Computing, University of Kent, Canterbury, United Kingdom. Electronic address: kh468@kent.ac.uk.

Alexia Zoumpoulaki (A)

School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom.

Marco Filetti (M)

Helsinki Institute for Information Technology, University of Helsinki, Helsinki, Finland.

Howard Bowman (H)

School of Computing, University of Kent, Canterbury, United Kingdom; School of Psychology, University of Birmingham, Birmingham, United Kingdom.

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Classifications MeSH