Mining Bodily Cues to Deception
Bibliographic Data
| ID | 21475791 |
|---|---|
| Authors | Ronald Poppe (0000-0002-0843-7878, Utrecht University, corresponding author), Sophie van der Zee (0000-0002-3457-5916, Erasmus University Rotterdam), Paul Taylor (0000-0002-8900-2912, Lancaster University), Ross Anderson (0000-0001-8697-5682, University of Cambridge), Remco C Veltkamp (0000-0003-1934-7170, Utrecht University) |
| Year | 2024 |
| Volume | 48 |
| Issue | 1 |
| Pages | 137-159 |
| Publication date | 2024-03-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Nonverbal Behavior (JOURNAL) |
| Journal identifiers | ISSN: 0191-5886 • E-ISSN: 1573-3653 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s10919-023-00450-9 |
| PMID | 38566623 |
| OpenAlex | W4390905655 |
| Language | EN |
| Citations received | 1 |
| References cited | 49 |
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
Cognitive psychology · Deception · Communication · Deception detection and forensic psychology · Psychology · Psychopathy, Forensic Psychiatry, Sexual Offending · Social Psychology · Stalking, Cyberstalking, and Harassment
The Oxford Handbook of Affective Computing
Accuracy of Deception Judgments
Choosing Prediction Over Explanation in Psychology
Nonverbal Leakage and Clues to Deception †
OpenPose
Bagging predictors
How humans impair automated deception detection performance
The politics of deceptive borders
Ecological Validity and Deception Detection Research Design
Interpersonal Deception Theory
Moderators of nonverbal indicators of deception
Pitfalls and Opportunities in Nonverbal and Verbal Lie Detection
Cues to deception
Deception and Arousal
| Unique citing works | 1 |
|---|---|
| Citations per year | 0.5 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |