The impact of romantic relationships on deception detection: Exploring the gender differences and the mediating role of mentalizing

In evolution, romantic relationships serve as the foundation for breeding and producing offspring. The ability to detect deception in these relationships can safeguard the investment and cultivation of descendants, leading to greater chances of survival and reproduction. However, barely any research has been carried out within this domain. The current study investigated the preliminary relationship between romantic relationships, mentalizing ability, and deception detection ability through an empirical experiment. Participants were primed by their romantic experiences and neutral experiences, and then went through a Reading the Mind in the Eyes (RTM) task and the deception detecting task for real person crime-type videos. Results showed that romantic relationships can improve participants' emotion recognition ability toward negative emotions, and females performed better in the deception detection task than males did. Most importantly, romantic relationships can improve participants' deception detection ability through the mediator of mentalizing ability. Though gender difference was not statistically significant in the RTM task, the results lay a solid foundation for further investigation into females' mentalizing ability and disclose the evolutionary meaning of romantic relationships.

Catching a Liar Through Facial Expression of Fear

High stakes can be stressful whether one is telling the truth or lying. However, liars can feel extra fear from worrying to be discovered than truth-tellers, and according to the "leakage theory," the fear is almost impossible to be repressed. Therefore, we assumed that analyzing the facial expression of fear could reveal deceits. Detecting and analyzing the subtle leaked fear facial expressions is a challenging task for laypeople. It is, however, a relatively easy job for computer vision and machine learning. To test the hypothesis, we analyzed video clips from a game show "The moment of truth" by using OpenFace (for outputting the Action Units (AUs) of fear and face landmarks) and WEKA (for classifying the video clips in which the players were lying or telling the truth). The results showed that some algorithms achieved an accuracy of >80% merely using AUs of fear. Besides, the total duration of AU20 of fear was found to be shorter under the lying condition than that from the truth-telling condition. Further analysis found that the reason for a shorter duration in the lying condition was that the time window from peak to offset of AU20 under the lying condition was less than that under the truth-telling condition. The results also showed that facial movements around the eyes were more asymmetrical when people are telling lies. All the results suggested that facial clues can be used to detect deception, and fear could be a cue for distinguishing liars from truth-tellers.