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  1. Meta-analyses have not shown emotions to be significant predictors of deception. Criticisms of this conclusion argued that individuals must be engaged with each other in higher stake situations for such emotions to manifest, and that these emotions must be evaluated in their verbal context (Frank and Svetieva in J Appl Res Memory Cognit 1:131–133, 10.1016/j.jarmac.2012.04.006, 2012). This study examined behavioral synchrony as a marker of engagement in higher stakes truthful and deceptive interactions, and then compared the differences in facial expressions of fear, contempt, disgust, anger, and sadness not consistent with the verbal content. Forty-eight pairs of participants were randomly assigned to interviewer and interviewee, and the interviewee was assigned to steal either a watch or a ring and to lie about the item they stole, and tell the truth about the other, under conditions of higher stakes of up to $30 rewards for successful deception, and $0 plus having to write a 15-min essay for unsuccessful deception. The interviews were coded for expression of emotions using EMFACS (Friesen and Ekman in EMFACS-7; emotional facial action coding system, 1984). Synchrony was demonstrated by the pairs of participants expressing overlapping instances of happiness (AU6 + 12). A 3 (low, moderate, high synchrony) × 2 (truth, lie) mixed-design ANOVA found that negative facial expressions of emotion were a significant predictor of deception, but only when they were not consistent with the verbal content, in the moderate and high synchrony conditions. This finding is consistent with data and theorizing that shows that with higher stakes, or with higher engagement, emotions can be a predictor of deception. 
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    Free, publicly-accessible full text available December 16, 2024
  2. This research work explores different machine learning techniques for recognizing the existence of rapport between two people engaged in a conversation, based on their facial expressions. First using artificially generated pairs of correlated data signals, a coupled gated recurrent unit (cGRU) neural network is developed to measure the extent of similarity between the temporal evolution of pairs of time-series signals. By pre-selecting their covariance values (between 0.1 and 1.0), pairs of coupled sequences are generated. Using the developed cGRU architecture, this covariance between the signals is successfully recovered. Using this and various other coupled architectures, tests for rapport (measured by the extent of mirroring and mimicking of behaviors) are conducted on real-life datasets. On fifty-nine (N = 59) pairs of interactants in an interview setting, a transformer based coupled architecture performs the best in determining the existence of rapport. To test for generalization, the models were applied on never-been-seen data collected 14 years prior, also to predict the existence of rapport. The coupled transformer model again performed the best for this transfer learning task, determining which pairs of interactants had rapport and which did not. The experiments and results demonstrate the advantages of coupled architectures for predicting an interactional process such as rapport, even in the presence of limited data. 
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