The Impact of Autistic Traits on Self-Recognition of Body Movements

Abstract

Despite the sparse visual information and paucity of self-identifying cues provided by point-light stimuli, as well as a dearth of experience in seeing our own-body movements, people can identify themselves solely based on the kinematics of body movements. The present study found converging evidence of this remarkable ability using a broad range of actions with whole-body movements. In addition, we found that individuals with a high degree of autistic traits showed worse performance in identifying own-body movements, particularly for simple actions. A Bayesian analysis showed that action complexity modulates the relationship between autistic traits and self-recognition performance. These findings reveal the impact of autistic traits on the ability to represent and recognize own-body movements.

Keywords: autism-spectrum quotient; biological motion; body movements; self-recognition; visual perception.

Figure 1

Motion capture data for the simple action kicking. The top row shows three image frames from the action sequence, and the bottom row shows the corresponding point-light display that was captured from the Kinect device. Not all of the points shown were used for display during the self-recognition task. The depicted individual has given written, informed consent to use her image in the publication.

Figure 2

Point-light displays of the self-recognition task. The image shows the subject and three distractor actors performing the waving action.

Figure 3

A boxplot of self-recognition accuracy by AQ group and action type. The boxplot shows the median accuracy and the first/third quartiles. The points denote the average accuracy of individual participants. Note that data points for participants yielding the same accuracy level may overlap in the plot.

Figure 4

Self-recognition accuracy for each action, categorized by action type. The boxplot displays the median (the central vertical line), the lower 25th and upper 75th percentiles (the left and right edges of the box), and whiskers (box edge ±1.5 × box width). The points denote the outliers (any value beyond the whiskers).

Figure 5

Differences in posterior predictive accuracy are shown for specific sets of contrasts and effects along the vertical axis. Posterior predictions are determined from the observed data and by comparing the mean predicted value at specific levels of one or more factors. The “no difference” marker is indicated by the vertical line at value zero. The shape of the posterior distribution is shown for each contrast, with 90% intervals indicated by the darker shaded region inside the distribution, and 1 SD of the posterior by the lighter, center shaded region. Posterior means are indicated by the center vertical lines inside each distribution.