Differences in the production and perception of communicative kinematics in autism

Abstract

In human communication, social intentions and meaning are often revealed in the way we move. In this study, we investigate the flexibility of human communication in terms of kinematic modulation in a clinical population, namely, autistic individuals. The aim of this study was twofold: to assess 1) whether communicatively relevant kinematic features of gestures differ between autistic and neurotypical individuals, and 2) if autistic individuals use communicative kinematic modulation to support gesture recognition. We tested autistic and neurotypical individuals on a silent gesture production task and a gesture comprehension task. We measured movement during the gesture production task using a Kinect motion tracking device in order to determine if autistic individuals differed from neurotypical individuals in their gesture kinematics. For the gesture comprehension task, we utilized stick-light figures as stimuli and, by testing for a correlation between the kinematics of these videos and recognition performance, we assessed whether autistic individuals used communicatively relevant kinematic cues to support recognition. We found that 1) silent gestures produced by autistic and neurotypical individuals differ in communicatively relevant kinematic features, such as the number of meaningful holds between movements, and 2) while autistic individuals are overall unimpaired at recognizing gestures, they processed repetition and complexity, measured as the amount of submovements perceived, different than neurotypicals do. These findings highlight how subtle aspects of neurotypical behavior can be experienced differently by autistic individuals, and demonstrate the relationship between movement kinematics and social interaction in high-functioning autistic individuals.

Facial Signals and Social Actions in Multimodal Face-to-Face Interaction

“…For instance, gaze shifts with blinks resulted in a 50% chance of being a question or a response (Figure 5). The permutation tests (number of simulations = 1000) showed an overall accuracy of 61% on the dataset, similar to accuracies obtained using the same type of model [98][99][100], with p = 0.001, suggesting a significant classification accuracy. The shaded area in the output nodes represents the proportion of response cases in that node, while the white area shows the proportion of question cases in that node.…”