Strong, but Wrong: Lay People’s and Police Officers’ Beliefs about Verbal and Nonverbal Cues to Deception
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The present study investigated the beliefs of students and police officers about cues to deception. A total of 95 police officers and 104 undergraduate students filled out a questionnaire addressing beliefs about cues to deception. Twenty-eight verbal cues were included in the questionnaire, all extracted from verbal credibility assessment tools (i.e., CBCA, RM, and SCAN). We investigated to what extent beliefs about nonverbal and verbal cues of deception differed between lay people (students) and police officers, and whether these beliefs were in agreement with objective cues known from research. Both students and police officers believed the usual stereotypical, but non-diagnostic (nonverbal) cues such as gaze aversion and increased movement to be indicative of deception. Yet, participants were less inclined to overestimate the relationship between verbal cues and deception and their beliefs fitted better with what we know from research. The implications of these findings for practice are discussed.
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Liars often provide less consistent or coherent verbal accounts lacking informational content, with fewer event details.
“Liars often provide less consistent or coherent verbal accounts lacking informational content, with fewer event details (Bogaard et al., 2016; DePaulo et al., 2003; Hartwig et al., 2011).”
From Sorting Insiders From Co-Workers: Remote Synchronous Computer-Mediated Triage for Investigating Insider Attacks (2024) · cites this paper as Bogaard et al., 2016
Our results confirm that questioning strategies which maximize cognitive burden can amplify signals of deception.
“Our results confirm findings of previous research in face-to-face and remote person-to-person contexts that questioning strategies which maximize cognitive burden can amplify signals of deception (e.g., Bogaard et al., 2016; DePaulo et al., 2003; Pentland et al., 2017), highlighting the potential of remote automated CMC.”
From Sorting Insiders From Co-Workers: Remote Synchronous Computer-Mediated Triage for Investigating Insider Attacks (2024) · cites this paper as Bogaard et al., 2016
The Methods describe a questionnaire about beliefs concerning deception cues, and the Discussion says the study did not examine actual deception-detection performance. Searches for cognitive burden, cognitive load, and questioning strategies found no passage showing that such strategies amplify deception signals. (LLM, conf=0.77)
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“…in face-to-face and remote person-to-person contexts that questioning strategies which maximize cognitive burden can amplify signals of deception (e.g., Bogaard et al., 2016; DePaulo et al., 2003; Pentland et al., 2017), highlighting the potential of remote automated CMC. Open questions increased…”
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Sitting in Judgment: How Body Posture Influences Deception Detection and Gazing Behavior
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No evidence that instructions to ignore nonverbal cues improve deception detection accuracy
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Patient Deception in Health Care: Physical Therapy Education, Beliefs, and Attitudes
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Reconsidering facial expressions and deception detection
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Influence of emoticons on deception detection: An empirical exploration
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Can you see me lying? Investigating the role of deception on gaze behavior
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Verbal Cues Fostering Perceptions of Credibility and Truth/Lie Detection
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Human-AI Collaboration for the Detection of Deceptive Speech
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Criminal Profiling as a Method of Detecting Lies in Nonverbal Communication
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Psicología, ciencias forenses y prueba: ¿(No) es oro todo lo que reluce?
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La communication non verbale dans les médias télévisuels
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Looks like a liar? Beliefs about native and non‐native speakers' deception
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Denault, V., Plusquellec, P., Jupe, L. M., St‐Yves, M., Dunbar, N. E., Hartwig, M., Sporer, S. L., Rioux-Turcotte, J., Jarry, J., Walsh, D., et al. (2023) · doi:10.32920/22096157 · Cited by 4 · see citing works
Neural correlates of guilt knowledge analysis using electroencephalographic technique
Jagannath, M., Adalarasu, K., & Dhivya, R. K. (2022) · Journal of Forensic Psychiatry and Psychology · doi:10.1080/14789949.2022.2042360 · Cited by 3 · see citing works
Beliefs about suspect alibis: A survey of lay people in the United Kingdom, Israel, and Sweden
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Looking guilty: Handcuffing suspects influences judgements of deception
Zloteanu, M., Salman, N. L., Krumhuber, E. G., & Richardson, D. C. (2022) · Journal of Investigative Psychology and Offender Profiling · doi:10.1002/jip.1597 · Cited by 2 · see citing works
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Beliefs, Confidence, and Attribution Bias Regarding Deception Cues in Police Officers and Lay People
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The effect of episodic future thinking ability on subjective cue use when judging credibility
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Finnish district judges' assessments of live versus video‐mediated party statements in court
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Australian criminal justice student’s beliefs about lie detection methods
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Acting Like a Liar: An Experimental Test of the Self-Presentational Theory of Deception
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Assessing threats of violence: Professional skill or common sense?
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Self-citationExpanding the scope of reflective knowledge: From MINE to OURS
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Catching liars by listening carefully
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Interviewing to assess and manage threats of violence
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Use the best, ignore the rest: How heuristics allow to tell a lie from the truthPreprint
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Wilcoxson, R., Denault, V., Browne, M., Brooks, N., & DUCKETT, P. S. (2025) · The International Journal of Evidence & Proof · doi:10.1177/13657127251326782 · Cited by 1 · see citing works
Effective faking of verbal deception detection with target‐aligned adversarial attacks
Kleinberg, B., Loconte, R., & Verschuère, B. (2025) · Legal and Criminological Psychology · doi:10.1111/lcrp.70001 · Cited by 3 · see citing works
DecepTech AI: Advancing Lie Detection Through Multimodal Nonverbal Behavior Analysis
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And Nothing but the Truth: an Exploration of Perjury
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EDUCAÇÃO PERMANENTE EM SAÚDE EM TEMPOS DE PANDEMIA COVID-19
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Does being emotionally intelligent and empathic predict deception detection accuracy?
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Use the best, ignore the rest: How heuristics allow to tell lie from truthPreprint
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Nowoczesne metody wykorzystujące ruch w terapii ruchowej, poznawczej oraz w edukacji
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Detecting Deception Through Non-Verbal Behaviour
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The Master of Detecting Deception: Machine Learning
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- Authors
- Bogaard, G., Meijer, E. H., Vrij, A., & Merckelbach, H.
- Affiliations
- Maastricht University (NL)
- University of Portsmouth (GB)
- Corresponding author
- Bogaard, Glynis
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-06-03
- Volume
- 11
- Issue
- 6
- Pages
- e0156615
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4892574
- OpenAlex
- W2412576869
- Cited by
- 123
- References
- 71
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 9 · 2023: 16 · 2022: 14 · 2021: 19 · 2020: 18 · 2019: 15 · 2018: 8 · 2017: 8
- FWCI
- 16.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Research Publications (Maastricht University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- Ministerie van Binnenlandse Zaken en Koninkrijksrelaties
- Topics
- Social Sciences › Psychology › Social Psychology › Deception detection and forensic psychology
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Keywords
- deception detection, verbal cues, nonverbal cues, lay beliefs, police officers, students, gaze aversion, credibility assessment, deception detection training
- MeSH terms
- Adult, Cues, Culture, Female, Humans, Deception, Male, Middle Aged, Surveys and Questionnaires, Students, Verbal Behavior, Police, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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