Mining Bodily Cues to Deception.

A significant body of research has investigated potential correlates of deception and bodily behavior. The vast majority of these studies consider discrete, subjectively coded bodily movements such as specific hand or head gestures. Such studies fail to consider quantitative aspects of body movement...

Descripción completa

Detalles Bibliográficos
Publicado en:Journal of Nonverbal Behavior Vol. 48; no. 1; pp. 137 - 160
Autores principales: Poppe, Ronald, van der Zee, Sophie, Taylor, Paul J., Anderson, Ross J., Veltkamp, Remco C.
Formato: Artículo
Publicado: Springer Nature Mar2024
Materias:
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=176353862&site=ehost-live
header:
  @attributes:
    shortDbName: ssf
    uiTerm: 176353862
    longDbName: Social Sciences Full Text (H.W. Wilson)
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        01915886
        JNV
      jtl: Journal of Nonverbal Behavior
      issn: 01915886
      maglogo: N
    pubinfo:
      dt: Mar2024
      vid: 48
      iid: 1
      pid: 237
      pub: Springer Nature
    artinfo:
      ui:
        176353862
        10.1007/s10919-023-00450-9
      ppf: 137
      ppct: 23
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
              size: 1.1MB
      tig:
        atl: Mining Bodily Cues to Deception.
      aug:
        au:
          Poppe, Ronald
          van der Zee, Sophie
          Taylor, Paul J.
          Anderson, Ross J.
          Veltkamp, Remco C.
        affil:
          https://ror.org/04pp8hn57 Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
          https://ror.org/057w15z03 Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
          https://ror.org/057w15z03 Erasmus School of Law, Erasmus University Rotterdam, Rotterdam, The Netherlands
          https://ror.org/04f2nsd36 Psychology, Lancaster University, Lancaster, UK
          https://ror.org/006hf6230 Psychology, University of Twente, Enschede, The Netherlands
          https://ror.org/013meh722 Computer Laboratory, University of Cambridge, Cambridge, UK
          https://ror.org/01nrxwf90 Security Engineering, School of Informatics Institute for Computing Systems Architecture, University of Edinburgh, Edinburgh, UK
      su:
        Interviewing
        Deception
        Body language
        Data mining
        Secondary analysis
        Research funding
        Digital video
        Medical coding
        Body movement
        Motion capture (Human mechanics)
      sug:
        subj:
          Interviewing
          Deception
          Body language
          Data mining
          Secondary analysis
          Research funding
          Digital video
          Medical coding
          Body movement
          Motion capture (Human mechanics)
      keyword:
        Body motion
        Motion capture
        Movement analysis
        Body motion
        Motion capture
        Movement analysis
      ab: A significant body of research has investigated potential correlates of deception and bodily behavior. The vast majority of these studies consider discrete, subjectively coded bodily movements such as specific hand or head gestures. Such studies fail to consider quantitative aspects of body movement such as the precise movement direction, magnitude and timing. In this paper, we employ an innovative data mining approach to systematically study bodily correlates of deception. We re-analyze motion capture data from a previously published deception study, and experiment with different data coding options. We report how deception detection rates are affected by variables such as body part, the coding of the pose and movement, the length of the observation, and the amount of measurement noise. Our results demonstrate the feasibility of a data mining approach, with detection rates above 65%, significantly outperforming human judgement (52.80%). Owing to the systematic analysis, our analyses allow for an understanding of the importance of various coding factor. Moreover, we can reconcile seemingly discrepant findings in previous research. Our approach highlights the merits of data-driven research to support the validation and development of deception theory.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: N
    holdings:
      @attributes:
        islocal: N