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August 15, 2025Applied Cognitive PsychologyOpen Access

Noise in the Verifiability Approach to Lie Detection

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Authors

GGGermán Galvéz-GarcíaPMPatricio Mena‐ChamorroAMAdam D. Moline

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Overview

Experimental analysis shows algorithmic classification improves accuracy but may produce false alarms in deception detection.

Key Points

  • Algorithmic classification improved accuracy to 61%, but it also had a false alarm rate of 59%.
  • The consensus-based judgment method reduced false alarms to 30% and enhanced reliability of truthfulness assessments.
  • Experiments revealed subjective variability in detail counts as a significant noise source affecting deception detection.
  • Verifiability Approach enables detection of deception, yet judgement biases limit its effectiveness and reliability.

Cite This Study

Galvéz-García et al. (2025) studied this question.

synapsesocial.com/papers/68a365600a429f797332b6c8https://doi.org/10.1002/acp.70089
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