Looking guilty: Handcuffing suspects influences judgements of deception

Zloteanu, Mircea; Salman, Nadine L.; Krumhuber, Eva; Richardson, Daniel C.

doi:10.1002/jip.1597

Cited by 3 publications

(1 citation statement)

References 128 publications

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“…While SDT models commonly assume equal variance for the noise and signal distributions and a constant response criterion across trials, as did the present models, these assumptions can be relaxed (Kellen et al, 2021). Similarly, if researchers prefer to employ an honesty scale (e.g., Zloteanu et al, 2022), an unequal variance SDT model can be employed, allowing for more nuanced analyses (see Bürkner & Vuorre, 2019;Vuorre, 2017). Additionally, there are non-linear models available that can incorporate guessing, providing a thorough investigation of decision strategies (see Bürkner, 2019).…”

Section: Extensionsmentioning

confidence: 97%

Bayesian Generalized Linear Mixed Effects Models for Deception Detection Analyses

Zloteanu¹,

Vuorre²

2023

Preprint

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Historically, deception detection research has relied on factorial analyses of response accuracy to make inferences. But this practice overlooks important sources of variability resulting in potentially misleading estimates and may conflate response bias with participants’ underlying sensitivity to detect lies from truths. We offer an alternative approach using Bayesian Generalized Linear Mixed Models (BGLMMs) within a Signal Detection Theory (SDT) framework to address these limitations. Our approach incorporates individual differences from both judges and senders, which are a principal source of spurious findings in deception research. By avoiding data transformations and aggregations, this methodology outperforms traditional methods and provides more informative and reliable effect estimates. The proposed framework offers researchers a powerful tool for analyzing deception data and advances our understanding of veracity judgments. All code and data are openly available.

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Section: Extensionsmentioning

confidence: 97%

Bayesian Generalized Linear Mixed Effects Models for Deception Detection Analyses

Zloteanu¹,

Vuorre²

2023

Preprint

View full text Add to dashboard Cite

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A Tutorial for Deception Detection Analysis or: How I Learned to Stop Aggregating Veracity Judgments and Embraced Signal Detection Theory Mixed Models

Zloteanu,

Vuorre

2024

J Nonverbal Behav

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Historically, deception detection research has relied on factorial analyses of response accuracy to make inferences. However, this practice overlooks important sources of variability resulting in potentially misleading estimates and may conflate response bias with participants’ underlying sensitivity to detect lies from truths. We showcase an alternative approach using a signal detection theory (SDT) with generalized linear mixed models framework to address these limitations. This SDT approach incorporates individual differences from both judges and senders, which are a principal source of spurious findings in deception research. By avoiding data transformations and aggregations, this methodology outperforms traditional methods and provides more informative and reliable effect estimates. This well-established framework offers researchers a powerful tool for analyzing deception data and advances our understanding of veracity judgments. All code and data are openly available.

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Implications of media reports of crime for public trust and social support: a conceptual analysis of individuals’ psychological wellbeing

Şahinoğlu,

Saygılı,

Muhtaroğulları

et al. 2024

Humanit Soc Sci Commun

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Crime reporting influences the public regardless of its source (traditional or social media outlets). In this regard, the current research focuses on crime reports in Northern Cyprus, a small society, where individual psychological and social aspects can be influenced by media content. Specifically the effects of media on public trust, presumption of innocence, psychological wellbeing and social support are addressed through a qualitative approach. In this study, a mixed methodology of content and discourse analysis determined the negative coverage of crime through the existence of unethical principles among 507 articles from different media outlets. Furthermore, a thematic network approach was used to analyse semi-structured interviews, in which 38 individuals provided in-depth data about their personal experiences and emotions regarding media publication. Results showed how the influence of media coverage of crime can extend to affect the psychological well-being and social relationships of those individuals involved, particularly concerning anxiety, public trust, and social support. These effects can be further amplified due to the increased stress and anxiety felt among the public during the COVID-19 pandemic, ongoing at the time of the interviews. This research can provide a better understanding of media and its influence on trust and support during uncertain times. Hence, both scholars and practitioners can benefit from the current findings.

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Looking guilty: Handcuffing suspects influences judgements of deception

Cited by 3 publications

References 128 publications

Bayesian Generalized Linear Mixed Effects Models for Deception Detection Analyses

Bayesian Generalized Linear Mixed Effects Models for Deception Detection Analyses

A Tutorial for Deception Detection Analysis or: How I Learned to Stop Aggregating Veracity Judgments and Embraced Signal Detection Theory Mixed Models

Implications of media reports of crime for public trust and social support: a conceptual analysis of individuals’ psychological wellbeing

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