Movement of deception in motion capture.

Deception detection has attracted broad interest in professional practice and academic research, and body movement is considered one of the key aspects in deception detection. Previous work has focused on certain body parts (i.e., hand, head, leg) or gestures (i.e., gaze aversion, leg unnatural movement, etc.), which were manually coded by human judges. However, manual coding of nonverbal behavior is time-consuming and painstaking to the coders, as well as possibly vulnerable to observation biases. To overcome the challenges associated with manual coding, we employed a motion capture system to collect the body movements, in which a total of 80 participants were engaged in two interviews about their holiday experiences. In interviews about their vacation experiences, participants either told the whole truth or lied completely. The results revealed distinct movement patterns across conditions. Lower body movement differentiated honest from deceptive responses, and this association was moderated by task order, whereas upper body movement showed no reliable effects once task order was taken into account. Notably, the pattern does not support a generalized "rigidity effect" (i.e., uniformly reduced movement during deception); instead, the observed movement differences were order-dependent. Exploratory analyses using machine learning approaches further delineated the temporal dynamics of deceptive movement, providing complementary insights into nonverbal markers of deception.