The language of high‐stakes truths and lies: Linguistic analysis of true and deceptive statements made during sexual homicide interrogations.

  • Published In: Legal & Criminological Psychology, 2023, v. 28, n. 1. P. 34 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Thompson, Andrew D.; Hartwig, Maria 3 of 3

Abstract

Purpose: Few studies have assessed deception during real‐life, high‐stakes encounters. This study is one of the largest and most geographically diverse to investigate how criminal suspects lie during investigative interviews. It is also one of the most specific; focusing solely on those who committed sexually motivated homicides. Methods: Sections of transcripts from 52 sexually motivated homicide offender interrogations were analysed using Linguistic Inquiry and Word Count software. Truthful (n = 27) and deceptive (n = 25) statements, corroborated through physical evidence, were then compared using the reality monitoring (RM) model of deception. Results: Support for the RM model was mixed. Truthful statements contained more motion and spatial details. There were no significant differences between true and deceptive statements when comparing perceptual, affective, and cognitive process details. Conclusions: The results support the notion that there are verbal cues to deception detectable in high‐stakes, real‐life situations. It also provides a starting point to assess these cues in special forensic populations. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Legal & Criminological Psychology. 2023/02, Vol. 28, Issue 1, p34
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2023
  • ISSN:1355-3259
  • DOI:10.1111/lcrp.12214
  • Accession Number:161180337
  • Copyright Statement:Copyright of Legal & Criminological Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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