Deception detection in human reasoning

Even though people deal with deceptions throughout their whole lives, deception detection remains a challenging problem. The average detection rate for humans is only around chance, and detection skill is unlikely to be improved through training. Therefore, researchers have studied the features of deceptive behaviors that were largely ignored in human detection. For example, physiologists look at the physiological signals such as breathing rate and blood pressure, psychologists focus on non-verbal cues such as facial expressions and gestures, and computer scientists search for linguistic cues such as length of sentences. Although they all provide promising results, they seem to neglect a critical part in a person´s communication: the reasoning behind the communicated content. In this paper, a method is proposed to detect deception by identifying inconsistencies, explaining the reasoning behind the inconsistencies, and measuring the likelihood of deception based on cues in reasoning. The initial experiment demonstrates the effectiveness of the approach in identifying and explaining communications containing inconsistencies. Reasoning cues that can best discriminate deception from truth are proposed, and aspects of the verification and measurement of such cues as possible future directions of work are discussed.