Emotions and deception detection

Humans have developed a complex social structure which relies heavily on communication between members. However, not all communication is honest. Distinguishing honest from deceptive information is clearly a useful skills, but individuals do not possess a strong ability to discriminate veracity. As others will not willingly admit they are lying, one must rely on different information to discern veracity. In deception detection, individuals are told to rely on behavioural indices to discriminate lies and truths. A source of such indices are the emotions displayed by another. This thesis focuses on the role that emotions have on the ability to detect deception, exploring the reasons for low judgemental accuracy when individuals focus on emotion information. I aim to demonstrate that emotion recognition does not aid the detection of deception, and can result in decreased accuracy. This is attributed to the biasing relationship of emotion recognition on veracity judgements, stemming from the inability of decoders to separate the authenticity of emotional cues. To support my claims, I will demonstrate the lack of ability of decoders to make rational judgements regarding veracity, even if allowed to pool the knowledge of multiple decoders, and disprove the notion that decoders can utilise emotional cues, both innately and through training, to detect deception. I assert, and find, that decoders are poor at discriminating between genuine and deceptive emotional displays, advocating for a new conceptualisation of emotional cues in veracity judgements. Finally, I illustrate the importance of behavioural information in detecting deception using two approaches aimed at improving the process of separating lies and truths. First, I address the role of situational factors in detecting deception, demonstrating their impact on decoding ability. Lastly, I introduce a new technique for improving accuracy, passive lie detection, utilising body postures that aid decoders in processing behavioural information. The research will conclude suggesting deception detection should focus on improving information processing and accurate classification of emotional information