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Cited by 522

The DOI matches its Crossref record; 289 of the 289 references it cites were found in a registry or an index.

Pitfalls and Opportunities in Nonverbal and Verbal Lie Detection

Open access
Vrij, Aldert, Granhag, Pär Anders, Porter, Stephen·2010·Psychological Science in the Public Interest

Abstract

The question of whether discernible differences exist between liars and truth tellers has interested professional lie detectors and laypersons for centuries. In this article we discuss whether people can detect lies when observing someone’s nonverbal behavior or analyzing someone’s speech. An article about detecting lies by observing nonverbal and verbal cues is over-due. Scientific journals regularly publish overviews of research articles regarding nonverbal and verbal cues to deception, but they offer no explicit guidance about what lie detectors should do and should avoid doing to catch liars. We will present such guidance in the present article. The article consists of two parts. The first section focuses on pitfalls to avoid and outlines the major factors that lead to failures in catching liars. Sixteen reasons are clustered into three categories: (a) a lack of motivation to detect lies (because

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Metadata

Authors
Vrij, A., Granhag, P. A., & Porter, S.
Venue
Psychological Science in the Public Interest
Published
2010-12
Volume
11
Issue
3
Pages
89-121
ISSN
  • 1529-1006 (ISSN-L)
  • 2160-0031
OpenAlex
W2039708603
Cited by
522
References
289
FWCI
37.45
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Pitfalls and Opportunities in Nonverbal and Verbal Lie Detection. https://app.cochat.ai/papers/10.1177/1529100610390861
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Vrij, A., Granhag, P. A., & Porter, S. (2010). Pitfalls and Opportunities in Nonverbal and Verbal Lie Detection. Psychological Science in the Public Interest. https://doi.org/10.1177/1529100610390861

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.