The Face of Deception: How Your Mimicry Tells the True Story

Peter Jan Hendrik Slijkhuis

Steven James Watson

Mariëlle Stel

Ellen Giebels

Presentation Abstract:
Objective: There are contested claims that communication receivers mimic communication senders less when senders are deceptive compared to truthful. Our studies evaluate a novel computer-based measure of facial mimicry to determine its potential to inform the debate. By examining the facial reactions of individuals receiving deceptive and truthful messages, we test whether this innovative approach can offer fresh insights into the relationship between facial mimicry and deception.

Methodology: We used video stimuli featuring individuals telling deceptive and truthful narratives. In Study A 65 participants were shown high stakes videos about real-life child abduction police broadcasts and asked to assess the truthfulness of the people depicted. Study B, replicated Study A using lower stakes lies, but with higher quality video stimuli, improving confidence in the accuracy of the analyses. Study B also contrasted positively and negatively valanced lies. In both studies facial mimicry was measured using Affectiva Affdex within the iMotions platform.

Results and Expected Results: Contrary to hypotheses, Study A showed a small but significant effect whereby participants mimicked deceptive speakers more than truthful ones. In ongoing study B, our results will test if the unexpected results of study A replicate with higher quality stimuli.

Conclusion: The findings of study A raised questions about the validity of using mimicry as an indicator of deception, as we found only a small effect in the opposite direction to that hypothesised. In study B, with the introduction of higher-quality video stimuli, we can test if the effect replicates. Our findings also indicate a need to directly contrast human versus automated coding to determine if our unexpected findings are due to differing measures. Given the scarcity of studies focusing on the receiver’s mimicry in deception detection, especially utilising advanced research techniques, further research is important to validate and expand upon these preliminary findings.

This publication uses Facial Expression Analysis which is fully integrated into iMotions Lab

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